Showing 1 - 20 results of 2,895 for search '(( safe ((well decrease) OR (small decrease)) ) OR ( a ((larger decrease) OR (marked decrease)) ))', query time: 0.48s Refine Results
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    Biases in larger populations. by Sander W. Keemink (21253563)

    Published 2025
    “…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. …”
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    Discovery of MKP10241, a Novel Small Molecule Targeting the GPR119/Incretin Axis to Treat Metabolic Disorders by Santosh Kumar Rai (22382996)

    Published 2025
    “…Chronic treatment of MKP10241 in DIO-mice exhibited marked reduction in body weight and feed intake at tested doses, which was associated with decreased blood glucose level, lipid parameters, and body fats. …”
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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
    “…Parameter values: interaction strengths were drawn from a half-normal distribution of zero mean and a standard deviation of 0.2, and strength for consumers was made no larger than the strength for resources. …”
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    Experimental environment and parameters. 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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    Results of ablation experiments. 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. …”