## 2 Materials and Methods
### Architecture of 6 mAPred-MSFF
#### 2.2.2 Feature Extraction Module  
The inverted residual block. To reduce the computation of the network, we always design the network with a low-dimensional space. However, the network can not capture enough information in low-dimensional space. To solve these problems, the inverted residual with linear bottleneck was proposed by [PERSON] et al. [40] in the MobileNetV2. This module takes an input as a low-dimensional compressed representation, which is first expanded to high dimensions and filtered with a lightweight depthwise convolution. Features are subsequently projected back to a low-dimensional representation with a linear convolution which can prevent the destruction of information in low-dimensional space. Our proposed method follows the idea to build an inverted residual block that consists of an expansion layer, a depthwise layer, and a projection layer. The expansion layer and the projection layer are the \(1\times 1\) convolution layer, called the pointwise convolution with a different expansion ratio. The depthwise layer is a depthwise separable convolution layer that is a key building block for many efficient neural network architectures [40; 41; 42; 43; 44]. The basic idea is to replace a full convolutional operator with a factorized version that splits convolution into two separate layers. The first layer is called a depthwise convolution, and it performs lightweight filtering by applying a single convolutional filter per input channel. The second layer is a point-wise convolution, which is responsible for building new features through computing linear combinations of the input channels. The inverted residual block operator can be expressed as a composition of three operators which is denoted as \(F_{IRB}(X)\):  
\[F_{IRB}(X)=[A\circ N\circ B]X \tag{9}\]  
where \(A\) is the expansion layer that is a linear transformation: \(A:R^{s\times s\times k}\to R^{s\times s\times n}\), \(N\) is the depthwise layer which contains several non-linear per-channel transformation: \(N:R^{s\times s\times n}\rightarrow\ldots\to R^{s^{\prime}\times s^{ \prime}\times n}\), and \(B\) is the projection layer which is again a linear transformation to the output domain: \(B:R^{s\times s\times n}\to R^{s^{\prime}\times s^{\prime}\times k^{ \prime}}\). For our inverted residual block \(F_{IRB}(X)\), \(A\) and \(B\) are the linear transformation without any nonlinear activations. We use the ELU activation function in the operator \(N=ELU\circ\textit{dwise}\circ ELU\). which accelerates the learning rate of the network. The ELU function is shown in the following equation:  
\[f(x)= \begin{cases}a(e^{x}-1)&if(x<0)\\ x&if(0\leq x)\end{cases}  \tag{10}\]  
Thus, we can obtain \(k^{\prime}\) feature maps containing the 6 mA site features captured by the inverted residual block. Multi-scale attention mechanism block. The attention mechanism in deep learning, which mimics the human visual attention mechanism [45; 46], is originally developed on a global scale. For example, the matrix multiplication in self-attention draws global dependencies of each word in a sentence [47] or each pixel in an image [48; 49; 50; 51]. The Squeeze-and-Excitation Networks (SENet) squeeze global spatial information into a channel descriptor to capture channel-wise dependencies [52]. However, merely aggregating contextual information on a global scale is too biased and weakens the features of small objects. To solve the problem, the multi-scale channel attention module (MS-CAM) proposed by [PERSON] et al. [53] aggregates global and local channel contexts given an intermediate feature \(X\in R^{H\times W\times C}\) with \(C\) channels and feature maps of size \(H\times W\). The attention channel weights \(M(X)\in R^{C}\) can be computed as:  
\[M(X)=L(X)\oplus G(X) \tag{11}\]  
where \(L(X)\) denotes the local channel context, and \(G(X)\) denotes the global channel context.