Correlations between perceptual bias scores, age, and autism-associated features: Simple linear regression showing a significant correlation between elevated proprioceptive bias an... by Daniel E. Lidstone (21567523)

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Calculation of perceptual bias scores. by Daniel E. Lidstone (21567523)

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Group differences in perceptual bias scores. by Daniel E. Lidstone (21567523)

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Experimental paradigm and sample data. by Daniel E. Lidstone (21567523)

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Error calculation. by Daniel E. Lidstone (21567523)

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Participant demographics for all participants included in analysis. by Daniel E. Lidstone (21567523)

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HaptiKart videogame. by Daniel E. Lidstone (21567523)

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Visual and proprioceptive-error feedback. by Daniel E. Lidstone (21567523)

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Architecture of Swin-T model. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Model the experimental results curve. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Results of comparison experiments. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Architecture of Swin Transformer Block. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Disease distribution map of the GZDL-BD. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Token merging module. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Comparative results of the ablation experiments. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Reproducibility of response time and brain measures. by Jan Willem Koten (17743224)

Balance test results. by Yi Yu (28902)

“…Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. This effect remains significant after a series of robustness checks. …”

Mechanism analysis results. by Yi Yu (28902)

“…Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. This effect remains significant after a series of robustness checks. …”

Endogenous test results. by Yi Yu (28902)

“…Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. This effect remains significant after a series of robustness checks. …”

Heterogeneity analysis results. by Yi Yu (28902)

“…Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. This effect remains significant after a series of robustness checks. …”