The arXiv Manuscripts that Provide Adobe Illustrator Source Files of their Delicate Illustrations
Adobe Illustrator is a vector graphics software, and is used by many researchers to create illustrations in their research papers1. To my mind, it is more professional than Microsoft Visio2 that I commonly use, but also more complicated meanwhile. The learning cost is kind of high (maybe not that high; it’s just that I get used to Visio), and I ever tried to learn it a few times before but end up giving up. That being said, I have been feeling that the graphs created by Adobe Illustrator are more visually formal. I can’t exactly express why, but indeed have some feelings. Taking designing a simple rectangle as an example, even if we keep all settings the same, different graphic design softwares will generate different rectangles which may look very different with distinct styles. I guess this is caused by different rendering mechanisms. I feel that the graphics created by Adobe Illustrator have clearer and cleaner edge. Maybe this is a reason, and another one is that, in Adobe Illustrator, users can export graphs to PDF files more conveniently and directly, which is better than most softwares (including Visio), therefore more suitable to use with LaTeX typesetting system.
Come back to talk about how to learn to use Adobe Illustrator. Before peer-review stage and finally formal publication, some authors will choose to upload their papers and corresponding TeX source files to arXiv website. And what’s better is that recently I found some authors would also upload their .ai files (native vector file type for Adobe Illustrator3) along with TeX files. Actually it’s not the common case, but there are indeed some researchers who opt to do it. These well-organized .ai files, and those specific elements in the files, are fairly important resources to learn Adobe Illustrator; it’s not a bad choice to get everything off the ground by imitation in a brand new field. So, in this post, I plan to record those arXiv manuscripts which provide .ai files, and I hope they will help me one day.
Definitely, this post is to be continued.
(1) Lin’s paper in 2017, Focal loss for dense object detection
Lin, Tsung-Yi, et al. “Focal loss for dense object detection.” Proceedings of the IEEE international conference on computer vision. 2017, available at: [1708.02002] Focal Loss for Dense Object Detection.
Fig. 1, add text annotation:
Fig. 3, design network structure:
References