Visualization of learned features via t-SNE. by Song Wu (36667)

Land lots and probabilities of being regular-shaped lots. by Subin Ham (19704398)

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Comparison of experimental results for cervical tumour segmentation algorithms. by Yao Yao (335996)

Spatial distribution of 40 AmeriFlux FLUXNET sites used in this study. by Yeonuk Kim (4589488)

Study area at a lot-level resolution (Figure created by the authors). by Subin Ham (19704398)

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Result of the classification. by Subin Ham (19704398)

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Architecture of the neural network. by Subin Ham (19704398)

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Study and test areas (Figure created by the authors). by Subin Ham (19704398)

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Dataset configuration. by Subin Ham (19704398)

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Feature dimensions used in dataset design. by Christian Jarvers (6328562)

Algorithm schematic diagram of the CSM module. by Huiying Zhang (200681)

“…The backbone network uses the lightweight Repvit model, improving detection performance and reducing model weight through transfer learning. The proposed MPA module integrates multi-scale contextual information, capturing complex dependencies between spatial and channel dimensions, thereby enhancing the representation capability of the neural network. …”

Data Sheet 1_Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive.pdf by Elena Pastorelli (7024235)

“…This work provides the computational community with a two-compartment spiking neuron model that supports the proposed forms of brain-state-specific activity. A machine learning evolutionary algorithm, guided by a set of fitness functions, selected parameters defining neurons that express the desired apical dendritic mechanisms. …”

Experimental results of the ablation experiment. by Huiying Zhang (200681)

“…The backbone network uses the lightweight Repvit model, improving detection performance and reducing model weight through transfer learning. The proposed MPA module integrates multi-scale contextual information, capturing complex dependencies between spatial and channel dimensions, thereby enhancing the representation capability of the neural network. …”

Statistics of Large, Medium, and Small Targets. by Huiying Zhang (200681)

“…The backbone network uses the lightweight Repvit model, improving detection performance and reducing model weight through transfer learning. The proposed MPA module integrates multi-scale contextual information, capturing complex dependencies between spatial and channel dimensions, thereby enhancing the representation capability of the neural network. …”

Model comparison test results. by Huiying Zhang (200681)

“…The backbone network uses the lightweight Repvit model, improving detection performance and reducing model weight through transfer learning. The proposed MPA module integrates multi-scale contextual information, capturing complex dependencies between spatial and channel dimensions, thereby enhancing the representation capability of the neural network. …”

Comparative experimental data of loss functions. by Huiying Zhang (200681)

“…The backbone network uses the lightweight Repvit model, improving detection performance and reducing model weight through transfer learning. The proposed MPA module integrates multi-scale contextual information, capturing complex dependencies between spatial and channel dimensions, thereby enhancing the representation capability of the neural network. …”

Block diagram of YOLOv8 model architecture. by Huiying Zhang (200681)

“…The backbone network uses the lightweight Repvit model, improving detection performance and reducing model weight through transfer learning. The proposed MPA module integrates multi-scale contextual information, capturing complex dependencies between spatial and channel dimensions, thereby enhancing the representation capability of the neural network. …”

Table 1_Interpretable machine learning analysis of environmental characteristics on bacillary dysentery in Sichuan Province.docx by Yao Zhang (134381)

“…Additionally, precipitation displayed a U-shaped relationship with BD risk in both the Subtropical Semi-Humid and Plateau Cold Climate Zones.…”

Image 1_Interpretable machine learning analysis of environmental characteristics on bacillary dysentery in Sichuan Province.jpeg by Yao Zhang (134381)

“…Additionally, precipitation displayed a U-shaped relationship with BD risk in both the Subtropical Semi-Humid and Plateau Cold Climate Zones.…”

Data Sheet 1_Outliers and anomalies in training and testing datasets for AI-powered morphometry—evidence from CT scans of the spleen.pdf by Yuriy Vasilev (21717089)

“…Using visual methods (1.5 interquartile range; heat map; boxplot; histogram; scatter plot), machine learning algorithms (Isolation forest; Density-Based Spatial Clustering of Applications with Noise; K-nearest neighbors algorithm; Local outlier factor; One-class support vector machines; EllipticEnvelope; Autoencoders), and mathematical statistics (z-score, Grubb’s test; Rosner’s test).…”