arXiv Vanity
arXiv Vanity renders papers from arXiv as responsive web pages so you don't have to squint at a PDF.
It turns this sort of thing:
Into this:
This is the web interface for viewing papers. The actual LaTeX to HTML conversion (the interesting bit) is done by Engrafo.
Running in development
Install Docker for Mac or Windows.
Do the initial database migration and set up a user:
$ script/manage migrate
$ script/manage createsuperuser
Pull the Engrafo Docker image, which is needed for rendering papers:
$ docker pull arxivvanity/engrafo
Then to run the app:
$ docker-compose up --build
Your app is now available at http://localhost:8000. The admin interface is at http://localhost:8000/admin/.
You can scrape the latest papers from arXiv by running:
$ script/manage scrape_papers
It'll probably fetch quite a lot, so hit ctrl-C when you've got enough.
Running tests
$ script/test
Using a development version of Engrafo
Engrafo is the LaTeX to HTML converter. If you are working on Engrafo, you might want to use the version you are working on locally.
To do that, run script/docker-build in your local Engrafo directory. This will create an image called engrafo-dev.
Then, in the arXiv Vanity directory (the same one this readme is in), create a file called .env to tell arXiv Vanity to use that image to render papers:
ENGRAFO_IMAGE=engrafo-dev
VS Code development environment
This project is configured with a dev container to get completions, etc inside VS Code. When VS Code opens, click "reopen in container" in the popup and it'll run the development environment inside the same container used by docker-compose.
Sponsors
Thanks to our generous sponsors for supporting the development of arXiv Vanity! Sponsor us to get your logo here.
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什么是arXiv.org? 先看看来自wikipedia的定义：The arXiv (pronounced "archive", as if the "X" were the Greek letter Chi, χ) is an archive for electronic preprints of scientific papers in the fields of mathematics, p
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Introduction 作者提出 Hyperbolic Contrastive Learning (HCL) 来在双曲空间中进行自监督对比学习，并进一步将 HCL 推广为了 Supervised Hyperbolic Contrastive Learning. 此外，作者还尝试了提出了 Robust Hyperbolic Contrastive Learning (RHCL)，将 HCL 和对抗
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Introduce 简单来说，为了防止自己的idea在论文被收录前被别人剽窃，我们会将预稿上传到arvix作为预收录，因此这就是个可以证明论文原创性（上传时间戳）的文档收录网站 。 arXiv（X依希腊文的χ发音，读音如英语的 archive（中文意思：档案），谐音：阿凯五）是一个收集物理学、数学、计算机科学与生物学的论文预印本的网站，始于1991年8月14日。截至2008年10月，arXiv.o
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最近看到知乎的一个方法：http://www.zhihu.com/question/38684296 http://arxiv.org/list/cs.CV/recent 看最近一周的论文 http://arxiv.org/list/cs.CV/1512 看15年12月的论文 http://arxiv.org/list/cs.CV/15 看15年的论文 http://arxivs
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提示：文章写完后，目录可以自动生成，如何生成可参考右边的帮助文档 CoRR和arXiv到底是什么 文章目录 一、arXiv 二、CoRR 前言 arXiv（X依希腊文的χ发音，读音如英语的archive）是一个收集物理学、数学、计算机科学、生物学与数理经济学的论文预印本的网站，始于1991年8月14日。截至2008年10月，arXiv.org已收集超过50万篇预印本；至2014年底，藏量达到1
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White Dwarfs as Physics Laboratories: Lights and Shadows https://arxiv.org/pdf/2202.02052.pdf abstract: The evolution of white dwarfs is essentially a gravothermal process of cooling in which the basi
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arXiv只是个提交论文预印本（preprint）的平台，而且里面的论文都没有经过同行评审（peer review），所以文章质量参差不齐。 比较有名的计算机检索数据库DBLP数据库可以检索arXiv里的文章，DBLP把arXiv归类为非正式发表（informal publication）。 所以arXiv不应该算正式发表，在学术圈内也不被认为是正式发表。 arXiv简介： 简单来说，为了防止自己
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为了防止自己的idea在论文被收录前被别人剽窃，我们会将预稿上传到arvix作为预收录，因此这就是个可以证明论文原创性（上传时间戳）的文档收录网站 。 论文未经过同行评审，论文水平参差不齐，谨慎引用，谨慎阅读。 arXiv(发音同archive)是一个提供学术文章在线发表的服务器，领域涵盖物理学、数学、非线性科学、计算机科学、定量生命科学、计量金融学和统计学。arXiv名中的“X”对应于希腊字母“
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https://www.zhihu.com/question/31864895 简单来说，为了防止自己的idea在论文被收录前被别人剽窃，我们会将预稿上传到arvix作为预收录，因此这就是个可以证明论文原创性（上传时间戳）的文档收录网站。
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A small script to collect your LaTeX files for submission to the arXiv. Particularly useful if you use biblatex, and you can use it directly on Overleaf. Usage Install with pip install arxiv-collector
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Description: The project hosts an aesthetic and simple LaTeX style suitable for "preprint" publications such as arXiv and bio-arXiv, etc.It is based on the nips_2018.sty style. This styling maintains
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arxiv_latex_cleaner This tool allows you to easily clean the LaTeX code of your paper to submit toarXiv. From a folder containing all your code, e.g. /path/to/latex/, itcreates a new folder /path/to/l
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To train the model, simply run train.py: $ python3 train.py Then, to generate a sample abstract, run sample.py: $ python3 sample.py If you want to change the starting seed of the generated abstrac