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Showing 1 - 20 results of 348 for search '(((( query modeling algorithm ) OR ( elements mges algorithm ))) OR ( level coding algorithm ))', query time: 0.42s Refine Results
  1. 1
    Geometric and algorithmic solutions to the generalised alibi query

    Geometric and algorithmic solutions to the generalised alibi query by Arthur Jansen (17809244)

    Published 2024
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  2. 2
    Table_1_Files_Performance

    Table_1_Files_Performance by Raquel Quatrini (6161930)

    Published 2025
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  3. 3
    MOBHunter

    MOBHunter by Raquel Quatrini (6161930)

    Published 2024
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  4. 4
    Modeling Molecularly Imprinted Nanoparticles with LNKD: A Resource Efficient Algorithm for Polymer Cross-Linking

    Modeling Molecularly Imprinted Nanoparticles with LNKD: A Resource Efficient Algorithm for Polymer Cross-Linking by Emma Stevens (22257358)

    Published 2025
    “…Here, we propose LNKD (Linking Nodes in KD-trees), a resource-efficient algorithm for predicting pairs of reactive atoms in pre-cross-linked 3D structures of monomers that applies not only to the modeling of MIPs, but also chemical cross-linking in other materials. …”
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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
    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
    Comparison of inference speed and model size for different tracking algorithms.

    Comparison of inference speed and model size for different tracking algorithms. by Yinghong Xie (22319875)

    Published 2025
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  9. 9
    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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  10. 10
    Example of a knowledge query.

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

    Published 2025
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  11. 11
    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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  12. 12
    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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  13. 13
    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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  14. 14
    Chromosome coding.

    Chromosome coding. by Junjie He (700642)

    Published 2024
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  15. 15
    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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  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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