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Showing 1 - 20 results of 310 for search '(( element data algorithm ) OR ((( query processing algorithm ) OR ( fem modeling algorithm ))))', query time: 0.42s Refine Results
  1. 1
    Determination of cyclic behavior of various cross-section composite column-beam connections reinforced with FRP composite using FEM analysis and ML models

    Determination of cyclic behavior of various cross-section composite column-beam connections reinforced with FRP composite using FEM analysis and ML models by Yasemin Simsek Turker (22340676)

    Published 2025
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  2. 2
    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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  3. 3
    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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  4. 4
    Size of FEMs of bridges.

    Size of FEMs of bridges. by Yanqing Men (20283876)

    Published 2025
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  5. 5
    Sound absorption spectrum calculated by TMM (dots) and FEM (solid lines).

    Sound absorption spectrum calculated by TMM (dots) and FEM (solid lines). by Xiaogang Li (299205)

    Published 2024
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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
    , MSE, and MAE of the optimised algorithms.

    , MSE, and MAE of the optimised algorithms. by Itxasne Antúnez Sáenz (22794696)

    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
    Performance of ENQUIRE’s gene normalization algorithm.

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

    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
    A comparison of the absorption coefficients obtained from the current simulation method (FEM) with those obtained by Sharma et al. [13].

    A comparison of the absorption coefficients obtained from the current simulation method (FEM) with those obtained by Sharma et al. [13]. by Xiaogang Li (299205)

    Published 2024
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  15. 15
    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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  16. 16
    Risk element category diagram.

    Risk element category diagram. by Yao Hu (3479972)

    Published 2025
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  17. 17
    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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  18. 18
    Supporting data for "Fracture and Non-linear Response of Biopolymer Network with Dynamic Cross-linkers"

    Supporting data for "Fracture and Non-linear Response of Biopolymer Network with Dynamic Cross-linkers" by Bingxian Tang (9151802)

    Published 2025
    “…<h4>This data set provides comprehensive supporting data for the thesis titled "Fracture and Non-linear Response of Biopolymer Network with Dynamic Cross-linkers." …”
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  19. 19
    Visual representation of Trust and elements that are needed for equitable data sharing.

    Visual representation of Trust and elements that are needed for equitable data sharing. by Qunita Brown (19751520)

    Published 2024
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  20. 20
    ML algorithms in cyber security.

    ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

    Published 2025
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