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Showing 1 - 20 results of 323 for search '(( element data algorithm ) OR ((( query processing algorithm ) OR ( dft modeling algorithm ))))', query time: 0.61s Refine Results
  1. 1
    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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  2. 2
    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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  3. 3
    City driving speed query results.

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

    Published 2025
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  4. 4
    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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  5. 5
    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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  6. 6
    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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  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
    Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization

    Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization by Akksay Singh (20176616)

    Published 2024
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  9. 9
    Example of a knowledge query.

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

    Published 2025
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  10. 10
    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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  11. 11
    Risk element category diagram.

    Risk element category diagram. by Yao Hu (3479972)

    Published 2025
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  12. 12
    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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  13. 13
    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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  14. 14
    ML algorithms in cyber security.

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

    Published 2025
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  15. 15
    Source_data_single_element_motor

    Source_data_single_element_motor by Hiroyuki Ichijo (19799292)

    Published 2024
    “…<p dir="ltr">Source data of experiments by the single-element motor algorithm.…”
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  16. 16
    Predicting Oxidation Potentials with DFT-Driven Machine Learning

    Predicting Oxidation Potentials with DFT-Driven Machine Learning by Shweta Sharma (41033)

    Published 2025
    “…To further facilitate future development of ML models, we extensively tested various algorithms and conducted a thorough feature importance analysis. …”
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  17. 17
    Predicting Oxidation Potentials with DFT-Driven Machine Learning

    Predicting Oxidation Potentials with DFT-Driven Machine Learning by Shweta Sharma (41033)

    Published 2025
    “…To further facilitate future development of ML models, we extensively tested various algorithms and conducted a thorough feature importance analysis. …”
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  18. 18
    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach

    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach by Soundous Touati (20282599)

    Published 2024
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  19. 19
    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach

    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach by Soundous Touati (20282599)

    Published 2024
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  20. 20
    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach

    Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach by Soundous Touati (20282599)

    Published 2024
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