Showing 1 - 20 results of 3,676 for search '(( space ((maps decrease) OR (mean decrease)) ) OR ( a ((large decrease) OR (larger decrease)) ))', query time: 0.83s Refine Results
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    The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness. by Gui Araujo (22170819)

    Published 2025
    “…When they stop being introduced in further assembly events (i.e. introduced species do not carry any mutualistic interactions), their proportion slowly decreases with successive invasions. (B) Even though higher proportions of mutualism promote higher richness, introducing this type of interaction into already assembled large communities promotes a sudden drop in richness, while stopping mutualism promotes a slight boost in richness increase. …”
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    <b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

    Published 2025
    “…The locomotion values (traces and metrics) are in arbitrary units with larger integers representing a greater displacement of the spherical treadmill, the hemodynamic (Hbt) values (traces and metrics) are a percentage change from the normalised baseline (prior to stimulus presentation), and the corresponding time series vector is presented in seconds. …”
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    Mean and standard error of pupil diameter in the dim states. by Tomoe Hayakawa (20978784)

    Published 2025
    “…However, in the second experiment (Experiment 2), the pupil diameter in the TD group showed a significant decrease. Significance levels are based on unpaired t-tests (**<i>p</i> <  0.01).…”
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    Heat maps for different models. by Haisong Xu (141729)

    Published 2025
    “…First, an efficient channel attention mechanism (ECA) is inserted in the layer before the SPPF in the backbone network, which realizes efficient computation of channel attention and reduces redundant computation while decreasing the model complication. Second, using the Content-Aware ReAssembly of Features (CARAFE) module instead of the original nearest-neighbor up-sampling module achieves light weighting while allowing for better aggregation of contextual information within a larger sensory field, which effectively improves the diversity and effectiveness of the model. …”
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