本页用于测试主题对 Markdown 各种格式的渲染效果。内容覆盖文本格式、列表、引用、代码块(多种语言)、表格、数学公式、链接、脚注、图片等。

Text Formatting - 文本格式

Bold Text · Italic Text · Bold and Italic · Strikethrough

Inline code · Ctrl + C · Highlighted

This is a blockquote.
这是中文引用。

多段引用:第二段内容。

Headings - 标题层级

H1 Heading

H2 Heading

H3 Heading

H4 Heading

H5 Heading
H6 Heading

Lists - 列表

Unordered

  • Machine Learning
  • Data Science
    • Deep Learning
    • Neural Networks
      • Computer Vision

Ordered

  1. First step
  2. Second step
    1. Nested A
    2. Nested B
  3. Third step

Task List

  • Extract entities
  • Set up evaluation
  • Publish results

Code - 代码块

Python

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import numpy as np
from sklearn.model_selection import train_test_split

def preprocess_data(df):
"""Preprocess the dataset"""
df_cleaned = df.dropna()
return df_cleaned

class MultilingualClassifier:
def __init__(self, n_estimators=100):
self.model = self._build_model(n_estimators)

def train(self, X, y):
self.model.fit(X, y)
return self

JavaScript

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class MultilingualProcessor {
constructor() {
this.languages = { en: 'English', zh: '中文', de: 'Deutsch', ja: '日本語' };
}

getText(key, lang = 'en') {
const translation = this.languages[key];
return translation || key;
}
}

SQL

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SELECT r.name_en AS researcher,
p.title_zh AS chinese_title,
p.year,
p.citations
FROM publications p
JOIN researchers r ON p.researcher_id = r.id
WHERE p.year >= 2020
ORDER BY p.citations DESC;

C++

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#include <vector>
#include <string>

std::vector<double> gradient_descent(
const std::vector<double>& X, const std::vector<double>& y, double lr = 0.01) {
// Standard gradient descent implementation
return X;
}

Bash / JSON / YAML

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#!/usr/bin/env bash
set -euo pipefail
echo "Installing dependencies..."
yarn install
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{
"name": "hexo-theme-researcher",
"version": "0.1.5",
"private": true
}
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theme: hexo-theme-researcher
language: en
highlight:
enable: true
line_number: true

Inline code example

Use the hits@k benchmark and npx hexo generate to build the site. Variable $FOO and array[0].

Tables - 表格

Method Full Name Language Accuracy
CNN Convolutional Neural Network 中文 94.2%
LSTM Long Short-Term Memory English 92.1%
Transformer Transformer Architecture Deutsch 96.5%
RNN Recurrent Neural Network 日本語 89.7%
Alignment Left Center Right
Row 1 a b c
Row 2 d e f

Mathematical Equations - 数学公式

Inline Math

The quadratic formula is $x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}$.

Einstein’s mass–energy equivalence: $E = mc^2$.

Block Math

$$
\nabla_\theta J(\theta) = \frac{1}{m} \sum_{i=1}^{m} \left( h_\theta(x^{(i)}) - y^{(i)} \right) x^{(i)}
$$

$$
\int_{-\infty}^{\infty} e^{-x^2} , dx = \sqrt{\pi}
$$

$$
\mathbf{a}^{(l)} = \sigma\left( \mathbf{W}^{(l)} \mathbf{a}^{(l-1)} + \mathbf{b}^{(l)} \right)
$$

Multi-line Display

$$
\begin{aligned}
z^{(l)} &= W^{(l)} a^{(l-1)} + b^{(l)} \
a^{(l)} &= \sigma(z^{(l)})
\end{aligned}
$$

Hexo Documentation · GitHub · Theme Repo

Images - 图片

Placeholder

Footnotes - 脚注

Here is a footnote reference.[^1] And another.[^2]

[^1]: First footnote content.
[^2]: Second footnote with a link.

Horizontal Rules


Multilingual Content - 多语言内容

English

This study investigates the application of deep learning in multilingual natural language processing.

中文

本研究探讨了深度学习技术在多语言自然语言处理中的应用。

繁體中文

本研究探討了深度學習技術在多語言自然語言處理中的應用。

Deutsch

Diese Studie untersucht die Anwendung von Deep-Learning-Techniken in der mehrsprachigen Verarbeitung natürlicher Sprache.

日本語

本研究では、多言語自然言語処理における深層学習技術の応用を調査します。

한국어

이 연구는 다국어 자연어 처리에서 딥러닝 기술의 응용을 조사합니다.

Français

Cette étude examine l’application des techniques d’apprentissage profond dans le traitement du langage naturel multilingue.

Español

Este estudio investiga la aplicación de técnicas de aprendizaje profundo en el procesamiento de lenguaje natural multilingüe.

Escaping - 转义

<div> 标签不会被解析。Literal $$$ 需要转义。使用 \! 转义感叹号。

Conclusion - 结论

This post verifies that bold, code, tables, formulas, code blocks, lists, quotes, footnotes and multilingual content render correctly in the theme.