## 2 Materials and Methods
### Modeling Method
#### 2.2.2 SE Attention Mechanism and Residual Convolutional Network  
The squeeze-and-excitation (SE) attention mechanism constitutes a canonical form of channel attention, designed to augment the representational capacity of convolutional neural networks while reducing the training overhead. It comprises two principal operations--squeeze and excitation. In the squeeze phase, global average pooling is applied to each channel feature map, collapsing its spatial dimensions to generate a channel descriptor that encapsulates the channel's global response. This operation is formalized in Equation (3):  
\[z_{c}=\frac{1}{N}{\sum\limits_{i=1}^{N}{x_{bic}}} \tag{3}\]  
where \(x_{bic}\) represents the value of channel \(c\) at point \(i\) in the BTH batch of the input feature position, \(N\) is the total number of elements on this channel, and \(z_{c}\) is the value of channel \(c\) after compression. During the excitation phase, the squeezed descriptors are passed through a nonlinear transformation to produce a weight vector whose length equals the number of channels, with each element quantifying the importance of its corresponding channel. This operation can be formulated as Equation (4):  
\[s=\sigma_{\text{ReLU}}(W_{2}\sigma_{\text{Sigmoid}}(W_{1}z+b_{1})+b_{2}) \tag{4}\]  
Among them, \(z\) is the compressed feature vector, \(z=[z_{1},z_{2},\ldotsldots,z_{c}]\), \(W_{1},W_{2}\) are the weight parameter of the fully connected layer, \(b_{1},b_{2}\) are the bias term, and \(\sigma\) is the activation function. Subsequently, the channel-wise weights generated in the excitation phase are applied to the original feature maps via channel-specific multiplication, thereby re-calibrating the features. This operation can be represented by Equation (5):  
\[X_{caled}=s\cdot x \tag{5}\]Among them, \(x\) is the original feature and \(X_{caled}\) is the feature after channel weighting. The squeeze-and-excitation (SE) attention mechanism recalibrates each channel feature map in a convolutional neural network through two successive operations--squeeze and excitation--thereby markedly enhancing the representational capacity. A schematic of the SE module adopted in this study is illustrated in Figure 2, where B denotes the batch size, C is the number of channels, L is the sequence length, and r is the reduction (compression) ratio. As a paradigmatic deep-network architecture, the residual neural network alleviates the vanishing-gradient problem commonly encountered during the training of very deep models by incorporating residual learning and cross-layer identity mappings, thereby substantially enhancing the feature representation and generalization capabilities. Each residual block in a ResNet can be formulated as Equation (6):  
\[y_{l}=h(x_{l})+F(x_{l},\{W_{i}\}) \tag{6}\]  
Among them, \(y_{l}\) represents the output of the \(l\) residual block, \(x_{l}\) is the input of the \(l\) residual block, \(h(x_{l})\) is the skip connection, \(F(x_{l},\{W_{i}\})\) is the residual function, and \(W_{i}\) is the weight parameter. In ReSE-AP Net, \(F(x_{l},\{W_{i}\})\) should be expressed as Equation (7):  
\[F(x_{l},\{W_{i}\})=s\cdot(W_{2}\cdot\sigma_{\text{ReLU}}(W_{1}\cdot x_{l}+b_{ 1})+b_{2}) \tag{7}\]  
Among them, \(W_{1},W_{2}\) are the weight parameters of the two convolutional layers, \(b_{1},b_{2}\) are the bias terms, and s is the channel weight vector calculated by the SE module. Finally, the total expression of the residual network weighted by SE attention can be obtained as Equation (8):  
\[y_{L}=x_{L}+\sum_{i=1}^{L}F(x_{i},\{W_{i}\}) \tag{8}\]  
Among them, \(x_{L}\) is the input of the last residual block, and L is the total number of residual blocks. Experimental results indicate that excessively deep architectures (e.g., ResNet-152) deteriorate performance in the target task rather than improving it.