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Showing 781 - 800 results of 13,266 for search '(( significant ((greater decrease) OR (nn decrease)) ) OR ( significantly increased decrease ))', query time: 0.37s Refine Results
  1. 781
    The influence of training-time noise on classification accuracy and reliability

    The influence of training-time noise on classification accuracy and reliability by Eloy Geenjaar (21533195)

    Published 2025
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  2. 782
    Data sample demographics. Note that AP refers to anti-psychotic medication, and AD refers to anti-depressive medication. Thus, AP and AD in this table refer to the percentage of patients taking anti-psychotic and anti-depressive medication, respectively. PANSS is a symptom scale for schizophrenia. We show its positive, negative, and composite scores.

    Data sample demographics. Note that AP refers to anti-psychotic medication, and AD refers to anti-depressive medication. Thus, AP and AD in this table refer to the percentage of pa... by Eloy Geenjaar (21533195)

    Published 2025
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  3. 783
    An abstract depiction of our model, for the independent version of the model, the context representation is not concatenated inside the local encoder.

    An abstract depiction of our model, for the independent version of the model, the context representation is not concatenated inside the local encoder. by Eloy Geenjaar (21533195)

    Published 2025
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  4. 784
    Associations of number of cognitive activities with all-cause mortality and medication analysis of number of cognitive activities by baseline cognitive function.

    Associations of number of cognitive activities with all-cause mortality and medication analysis of number of cognitive activities by baseline cognitive function. by Linjia Duan (13276989)

    Published 2025
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  5. 785
    These subfigures show a visualization of the context embeddings for the DSVAE model with LS=2=4,CS=2. The first subfigure from the left shows three patient clusters, the second subfigure both patients and controls, the third colors context embeddings based on the subject’s age, and the last subfigure colors the context embeddings based on the subject’s cognitive score, CMINDs.

    These subfigures show a visualization of the context embeddings for the DSVAE model with LS=2=4,CS=2. The first subfigure from the left shows three patient clusters, the second sub... by Eloy Geenjaar (21533195)

    Published 2025
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  6. 786
    The window classification accuracy for two of our proposed models (DSVAE, IDSVAE), windowed FNC (wFNC), and two baseline methods: context-only (CVAE), and local-only (LVAE).

    The window classification accuracy for two of our proposed models (DSVAE, IDSVAE), windowed FNC (wFNC), and two baseline methods: context-only (CVAE), and local-only (LVAE). by Eloy Geenjaar (21533195)

    Published 2025
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  7. 787
    Baseline characteristics by number of cognitive activities.

    Baseline characteristics by number of cognitive activities. by Linjia Duan (13276989)

    Published 2025
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  8. 788
    The three schizophrenia patient clusters visualized using functional connectivity matrices. To create the visualizations, we take all the windows belonging to a cluster and average their wFNC matrix.

    The three schizophrenia patient clusters visualized using functional connectivity matrices. To create the visualizations, we take all the windows belonging to a cluster and average... by Eloy Geenjaar (21533195)

    Published 2025
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  9. 789
    Analysis of FROM dynamics (pre-epidemic).

    Analysis of FROM dynamics (pre-epidemic). by Cuicui Liu (1496260)

    Published 2024
    Subjects: “…phenomenon intensified significantly…”
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  10. 790
    Analysis of FROM dynamics (post epidemic).

    Analysis of FROM dynamics (post epidemic). by Cuicui Liu (1496260)

    Published 2024
    Subjects: “…phenomenon intensified significantly…”
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  11. 791
    Robustness check.

    Robustness check. by Cuicui Liu (1496260)

    Published 2024
    Subjects: “…phenomenon intensified significantly…”
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  12. 792
    Third.

    Third. by Cuicui Liu (1496260)

    Published 2024
    Subjects: “…phenomenon intensified significantly…”
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  13. 793
    Analysis of TO dynamics (in-epidemic).

    Analysis of TO dynamics (in-epidemic). by Cuicui Liu (1496260)

    Published 2024
    Subjects: “…phenomenon intensified significantly…”
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  14. 794
    Analysis of TO dynamics (pre-epidemic).

    Analysis of TO dynamics (pre-epidemic). by Cuicui Liu (1496260)

    Published 2024
    Subjects: “…phenomenon intensified significantly…”
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  15. 795
    Descriptive statistical analysis of the total sample.

    Descriptive statistical analysis of the total sample. by Cuicui Liu (1496260)

    Published 2024
    Subjects: “…phenomenon intensified significantly…”
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  16. 796
    Second.

    Second. by Cuicui Liu (1496260)

    Published 2024
    Subjects: “…phenomenon intensified significantly…”
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  17. 797
    Dynamics of net spillage in the first stage (pre-epidemic).

    Dynamics of net spillage in the first stage (pre-epidemic). by Cuicui Liu (1496260)

    Published 2024
    Subjects: “…phenomenon intensified significantly…”
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  18. 798
    Dynamics of net spillage in the third stage (post epidemic).

    Dynamics of net spillage in the third stage (post epidemic). by Cuicui Liu (1496260)

    Published 2024
    Subjects: “…phenomenon intensified significantly…”
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  19. 799
    Analysis of FROM dynamics (in-epidemic).

    Analysis of FROM dynamics (in-epidemic). by Cuicui Liu (1496260)

    Published 2024
    Subjects: “…phenomenon intensified significantly…”
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  20. 800
    Comparison of total overflow for different lag orders.

    Comparison of total overflow for different lag orders. by Cuicui Liu (1496260)

    Published 2024
    Subjects: “…phenomenon intensified significantly…”
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