Jupyter笔记本Mathjax嵌入式符号与文本重叠

Question

我在Mozilla Firefox（66.0.3）中运行带有ipykernel（v5.1.0）的Jupyter笔记本，但是我也看到这种情况也在Chrome中发生。 如果相关信息，我使用的是Windows 10 64位操作系统。

在现有笔记本上渲染mathjax时，通常显示为：

内联mathjax符号似乎与主要文本重叠。似乎渲染器将数学内容视为具有零宽度（？）。

请明确说明：

• 我键入数学符号时，它们显示正常；如果我执行markdown单元，它们甚至看起来还不错。
• 仅当我保存笔记本，退出服务器，启动新的jupyter服务器并再次启动笔记本时，才会发生上述问题。
• 从图像中可以看到，它仅影响内联数学，不影响显示的数学。
• 我可以通过在每个新的会话中再次重新执行markdown单元来解决此问题，但这变得非常烦人。

基础代码如下：

<span class="kw">Principal Component Analysis</span> is an unsupervised linear transformation technique.
It helps to identify patterns in data based on correlations between features.

PCA aims to find the directions of maximum variance in high-dimensional data and projects them onto a new subspace spanned by these vectors (with equal/fewer dimensions than the original one).
Mathematically-speaking, we construct a $d\times k$-dimensional transformation matrix $W$ that allows us to map a sample vector $x$ in $d$-dimensional feature space ($x\in \mathbb{R}^d$) onto a new $k$-dimensional subspace.

$$
x \longrightarrow z = xW
$$

**General PCA algorithm**
1. Center the $d$-dimensional dataset around the origin. Z-scoring (dividing by stdev may help, or not).
2. Construct covariance matrix.
3. Decompose the covariance matrix onto its eigenvectors and eigenvalues.
4. Sort the eigenvalues by decreasing order to rank the corresponding eigenvectors.
5. Select $k$ eigenvectors that correspond to the $k$ largest eigenvalues, where $k$ is the dimensionality of the new feature subspace ($k\leq d$)
6. Construct a projection matrix $W$ from the $k$ eigenvectors chosen in step 4.
7. Perform the projection by multiplying $X^\top W$ (a $n\times k$ matrix), to obtain a new $k$-dimensional feature subspace.

我不认为自己是唯一经历过此事的人，但是我找不到在网上讨论此问题的人。 我该如何解决？