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processing algorithm » modeling algorithm (Expand Search), routing algorithm (Expand Search), tracking algorithm (Expand Search)
query processing » pre processing (Expand Search)
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Showing 1 - 20 results of 206 for search '(( element mesh algorithm ) OR ((( query processing algorithm ) OR ( neural coding algorithm ))))', query time: 0.52s Refine Results
  1. 1
    Mesh Division Schematic Diagram.

    Mesh Division Schematic Diagram. by Nan Ru (9594384)

    Published 2025
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  2. 2
    Automatic generation of mesh diagrams.

    Automatic generation of mesh diagrams. by Ronghua Meng (20978441)

    Published 2025
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  3. 3
    Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code

    Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

    Published 2025
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  4. 4
    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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  5. 5
    City driving speed query results.

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

    Published 2025
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  6. 6
    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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  7. 7
    Data and code resources.

    Data and code resources. by Sam Hall-McMaster (10343795)

    Published 2025
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  8. 8
    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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  9. 9
    The FEM mesh model for the three-layer case, where 7919 degrees of freedom are used.

    The FEM mesh model for the three-layer case, where 7919 degrees of freedom are used. by Xiaogang Li (299205)

    Published 2024
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  10. 10
    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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  11. 11
    Performance of ENQUIRE’s gene normalization algorithm.

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

    Published 2025
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  12. 12
    The parameter setting of the evolutionary neural network.

    The parameter setting of the evolutionary neural network. by Wenbing Shi (5806160)

    Published 2025
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  13. 13
    The genome coding scheme.

    The genome coding scheme. by Wenbing Shi (5806160)

    Published 2025
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  14. 14
    Multi-material topology optimization using scaled boundary finite element method

    Multi-material topology optimization using scaled boundary finite element method by Mohammed Saif Zamiruddin Siddiqui (22384884)

    Published 2025
    “…SBFEM is implemented and its performance is tested across different density interpolation-based MMTO methods: the Alternating Active-Phase (AAP) algorithm, SIMP with mapping based interpolation, and polygonal mesh based MMTO, PolyMat which uses Discrete Material Optimization (DMO) combined with Zhang–Paulino–Ramos (ZPR) update scheme. …”
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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
    The optimal number of clusters identified using the GMM clustering algorithm.

    The optimal number of clusters identified using the GMM clustering algorithm. by Dinh K. Tang (20469082)

    Published 2024
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  17. 17
    Optimal number of clusters identified using the GMM clustering algorithm.

    Optimal number of clusters identified using the GMM clustering algorithm. by Dinh K. Tang (20469082)

    Published 2024
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  18. 18
    Impact of multiunit merging on neural information encoding.

    Impact of multiunit merging on neural information encoding. by Daniel Suárez-Barrera (22676654)

    Published 2025
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  19. 19
    Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>"

    Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>" by Yize Wang (19535173)

    Published 2024
    “…<p dir="ltr">Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>"</p>…”
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  20. 20
    code_4

    code_4 by Olga Ovtsarenko (20432033)

    Published 2024
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