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Showing 141 - 160 results of 718 for search '(((( elements method algorithm ) OR ( complement based algorithm ))) OR ( level coding algorithm ))', query time: 0.31s Refine Results
  1. 141
    The schedule of enrolment, interventions, and assessments.

    The schedule of enrolment, interventions, and assessments. by Marialuisa Gandolfi (8639238)

    Published 2024
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  2. 142
    (A) Illustration of grouping and segmentation based on the global similarity structure when the number of elements is small. A, B, C, and D represent stimulus elements at different spatial locations. The physical pairwise similarities are assumed to be high between A and B and between C and D, while they are relatively low for the other four pairs, as indicated by the physical similarity matrix. This configuration potentially leads to the grouping of {A, B} and {C, D}, as depicted by the dashed red squares. Following neural processing that incorporates the global similarity structure, similar pairs are grouped, whereas dissimilar pairs are segregated, as reflected in the perceptual connection matrix. (B) Demonstration of how computational complexity increases with stimulus size. The left panel presents the physical similarity matrix for 3 × 6 and 4 × 8 element stimuli, respectively. The right panel illustrates the exponential growth in the number o

    (A) Illustration of grouping and segmentation based on the global similarity structure when the number of elements is small. A, B, C, and D represent stimulus elements at different... by Yen-Ju Chen (354876)

    Published 2025
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  3. 143
    Parameters of stimuli used in the study.

    Parameters of stimuli used in the study. by Elnaz Nemati (21402730)

    Published 2025
    “…<div><p>This study introduces a neurobiologically inspired computational model based on the predictive coding algorithm, providing insights into coherent motion detection processes. …”
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  4. 144
    Attributes classification based on MSD.

    Attributes classification based on MSD. by Tamer Abdel Latif Ali (21464041)

    Published 2025
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  5. 145
    The run time for each algorithm in seconds.

    The run time for each algorithm in seconds. by Edward Antonian (21453161)

    Published 2025
    “…The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. Next, we examine the statistical effect of correlated prediction error and propose a method for Generalized Least Squares (GLS) on graphs. …”
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  6. 146
    Correlation matrices of handcrafted features before and after feature selection. The figure presents two heatmaps illustrating the correlation among 52 handcrafted features derived from the 3-level wavelet transform (WT) at four resolutions. These features were used as inputs for traditional ML algorithms. The left map demonstrates the correlation matrix of 52 features crafted from 4 resolutions of the 3-level WT, which are used as inputs for traditional ML algorithms. The heat map (on the right) is the correlation matrix after feature selection, in which features whose Pearson’s correlation coefficient is higher than 0.8 are considered highly correlated. We keep only one representative feature and remove its highly correlated features.

    Correlation matrices of handcrafted features before and after feature selection. The figure presents two heatmaps illustrating the correlation among 52 handcrafted features derived... by Quang Dung Dinh (20995766)

    Published 2025
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  7. 147
    Experimental environment configuration.

    Experimental environment configuration. by Bei Huang (485021)

    Published 2024
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  8. 148
    Performance evaluation results of the ablation experiment.

    Performance evaluation results of the ablation experiment. by Bei Huang (485021)

    Published 2024
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  9. 149
    Comparison between Ghiasi et al.’s 3D virtual scene aesthetic style expression and Hybrid Garden StyleNet.

    Comparison between Ghiasi et al.’s 3D virtual scene aesthetic style expression and Hybrid Garden StyleNet. by Bei Huang (485021)

    Published 2024
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  10. 150
    Structure of the style prediction network.

    Structure of the style prediction network. by Bei Huang (485021)

    Published 2024
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  11. 151
    Structure of the style transfer network.

    Structure of the style transfer network. by Bei Huang (485021)

    Published 2024
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  12. 152
    Comparison of network training duration.

    Comparison of network training duration. by Bei Huang (485021)

    Published 2024
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  13. 153
    Comparison on different number of residual blocks.

    Comparison on different number of residual blocks. by Bei Huang (485021)

    Published 2024
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  14. 154
    Overall flow chart of the model.

    Overall flow chart of the model. by Bei Huang (485021)

    Published 2024
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  15. 155
    Data Sheet 1_Fast forward modeling and response analysis of extra-deep azimuthal resistivity measurements in complex model.docx

    Data Sheet 1_Fast forward modeling and response analysis of extra-deep azimuthal resistivity measurements in complex model.docx by Pan Zhang (206408)

    Published 2025
    “…Considering the increased detection range of EDARM and the requirements for computational efficiency, this paper presents a 2.5-dimensional (2.5D) finite element method (FEM). By leveraging the symmetry of simulated signals in the spectral domain, the algorithm reduces computation time by 50%, significantly enhancing computational efficiency while preserving accuracy. …”
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  16. 156
    Example gauge chart outputs produced by the M.E.D.V.I.S. algorithm.

    Example gauge chart outputs produced by the M.E.D.V.I.S. algorithm. by Hector Torres (11708207)

    Published 2025
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  17. 157
    Execution time comparisons between DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B) and Epi-SSA(P) on the DNME3 100 dataset.

    Execution time comparisons between DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B) and Epi-SSA(P) on the DNME3 100 dataset. by Liyan Sun (760586)

    Published 2024
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  18. 158
    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.

    F-measure comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset. by Liyan Sun (760586)

    Published 2024
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  19. 159
    Genes of the epistastic interactions detected on the seven GWAS data using Epi-SSA.

    Genes of the epistastic interactions detected on the seven GWAS data using Epi-SSA. by Liyan Sun (760586)

    Published 2024
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  20. 160
    Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 1000 dataset.

    Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DME 1000 dataset. by Liyan Sun (760586)

    Published 2024
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