[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-78608":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":10,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":21,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":43,"readmeContent":44,"aiSummary":45,"trendingCount":15,"starSnapshotCount":15,"syncStatus":46,"lastSyncTime":47,"discoverSource":48},78608,"figures4papers","ChenLiu-1996\u002Ffigures4papers","ChenLiu-1996","My Python scripts to make high-quality figures for publications in top AI conferences and journals.","https:\u002F\u002Fchenliu-1996.github.io\u002F",null,"Python",2337,149,7,0,49,153,253,147,105.53,false,"main",true,[25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"acl","cvpr","eccv","emnlp","figures","iccv","iclr","icml","llm","llm-skills","machine-learning","nature","nature-machine-intelligence","neurips","python","scientific-figure","skill","skills","2026-06-12 04:01:23","\u003Cdiv align=\"center\">\n\n\u003Ch1>\u003Ccode>Figures for Papers\u003C\u002Fcode>\u003C\u002Fh1>\n\n[![oosmetrics](https:\u002F\u002Fapi.oosmetrics.com\u002Fapi\u002Fv1\u002Fbadge\u002Fachievement\u002F9cb2283a-461d-44fd-bd2d-83d82f53fd17.svg)](https:\u002F\u002Foosmetrics.com\u002Frepo\u002FChenLiu-1996\u002Ffigures4papers)\n\u003Cbr>[![LinkedIn](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLinkedIn-Chen-blue)](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fchenliu1996\u002F)\n[![Twitter Follow](https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002FChen.svg?style=social)](https:\u002F\u002Fx.com\u002FChenLiu_1996)\n[![Google Scholar](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGoogle_Scholar-Chen-4a86cf?logo=google-scholar&logoColor=white)](https:\u002F\u002Fscholar.google.com\u002Fcitations?user=3rDjnykAAAAJ&sortby=pubdate)\n\n\u003C\u002Fdiv>\n\nI am [Chen Liu](https:\u002F\u002Fchenliu-1996.github.io\u002F), a Computer Science PhD Candidate at Yale University.\n\nThis is a centralized repository of my own **Python scripts for high-quality figures**.\n\nThese figures appear in top venues including but not limited to *Nature Machine Intelligence*, *ICML*, and *NeurIPS*.\n\n\u003Cbr>\n\nCan you do me a favor and **star this repository** too: [https:\u002F\u002Fgithub.com\u002FChenLiu-1996\u002FLM-Dispersion](https:\u002F\u002Fgithub.com\u002FChenLiu-1996\u002FLM-Dispersion)? Thank you!\n\n\u003Cbr>\n\u003Cbr>\n\n### Bar plots for quantitative comparison\n\u003Cimg src=\"figure_ImmunoStruct\u002Ffigures\u002Fbars_comparison_IEDB.png\" width=\"800\">\n\n\u003Cimg src=\"figure_CellSpliceNet\u002Ffigures\u002Fcomparison.png\" width=\"800\">\n\n### Bar plots for composition breakdown\n\u003Cimg src=\"figure_brainteaser\u002Ffigures\u002Fbrute_force.png\" width=\"800\">\n\n\u003Cdiv align=\"center\">\n\n\u003Ch3 align=\"left\">Radar plots                   Line plots\u003C\u002Fh3>\n\n\u003Cp align=\"left\">\n\u003Cimg align=\"left\" src=\"figure_VIGIL\u002Ffigures\u002Fcomparison_radar.png\" width=\"400\" alt=\"Radar comparison\">\n\u003Cimg align=\"left\" src=\"figure_VIGIL\u002Ffigures\u002Fcomparison_posttraining.png\" width=\"350\" alt=\"Post-training comparison\">\n\u003Cbr clear=\"all\">\n\u003C\u002Fp>\n\n\u003C\u002Fdiv>\n\n### Trend plots\n\u003Cimg src=\"figure_ophthal_review\u002Ffigures\u002Ftrend_by_month.png\" width=\"800\">\n\n### Heat maps\n\u003Cimg src=\"figure_RNAGenScape\u002Ffigures\u002Fresults_comparison_optimization.png\" width=\"800\">\n\n### 3D spheres\n\u003Cimg src=\"figure_Dispersion\u002Ffigures\u002Fillustration.png\" width=\"800\">\n\n### Miscellaneous: figures not made end-to-end in Python\nThese figures were made partially in Python. I included them to acknowledge the time and efforts I spent on them.\n\n\u003Cimg src=\"assets\u002FImmunoStruct_schematic.png\" width=\"400\">\u003Cimg src=\"assets\u002FImmunoStruct_contrastive.png\" width=\"400\">\n\u003Cbr>\u003Cimg src=\"assets\u002FImmunoStruct_results_IEDB.png\" width=\"400\">\u003Cimg src=\"assets\u002FImmunoStruct_results_CEDAR.png\" width=\"400\">\n\u003Cbr>\u003Cimg src=\"assets\u002FRNAGenScape_schematic.png\" width=\"400\">\u003Cimg src=\"assets\u002FDispersion_motivation.png\" width=\"400\">\n\u003Cbr>\u003Cimg src=\"assets\u002FDispersion_observation.png\" width=\"400\">\u003Cimg src=\"assets\u002FDispersion_observation_distillation.png\" width=\"400\">\n\n\u003Cbr>\n\n## LLM skill integration (some credits to my friend [Shan Chen](https:\u002F\u002Fshanchen.dev\u002F))\n\nThe **scientific figure making** skill lives in `scientific-figure-making\u002F`. Demo figures live in `assets\u002F`. Project-specific scripts and outputs live in `figure_*\u002F`.\n\n### Skill folder hierarchy\n\n```\nscientific-figure-making\u002F\n├── SKILL.md                              # Quick reference: metadata, when to use, patterns, links\n└── references\u002F\n    ├── api.md                            # API\u002Fconventions to implement (palette, helpers, export)\n    ├── common-patterns.md                # Reusable figure patterns\n    ├── demos.md                          # Real-world figure_* projects (with URLs)\n    ├── design-theory.md                  # Style rationale and design principles\n    └── tutorials.md                      # Step-by-step guides\n```\n\n### Using this skill in an AI coding agent\n\n\u003Cdetails>\n\u003Csummary>\u003Cstrong>No installation (path-based)\u003C\u002Fstrong>\u003C\u002Fsummary>\n\nYou can use this skill **without installing anything**: open this repo in your AI coding agent (e.g. [Cursor](https:\u002F\u002Fcursor.com), Claude Code, etc.) and reference the skill by path in your prompts. The agent reads `scientific-figure-making\u002FSKILL.md` and the `references\u002F` files from the repo—no symlinks or plugins required.\n\n**Simple AI workflow**\n\n1. Open this repository in your AI coding agent (e.g. Cursor).\n2. Ask the AI to create or update a plotting script in your target folder (for example `figure_PROJECT_NAME\u002F`).\n3. In your prompt, explicitly ask it to follow `scientific-figure-making\u002FSKILL.md` and `scientific-figure-making\u002Freferences\u002Fdesign-theory.md`.\n4. Run the generated script and check the exported figure.\n\n**Prompt template (copy\u002Fpaste)**\n\n```text\nCreate a publication-quality figure script at \u003Ctarget_path>.\nUse the Scientific Figure Making skill conventions from:\n- scientific-figure-making\u002FSKILL.md\n- scientific-figure-making\u002Freferences\u002Fdesign-theory.md\n- scientific-figure-making\u002Freferences\u002Fapi.md (palette, helpers, export)\n\nImplement or adapt the patterns (apply_publication_style, make_* helpers, finalize_figure). See figure_* folders for reference scripts.\nInput data: \u003Cdescribe your data or paste arrays>.\nOutput files: \u003Cname>.png and \u003Cname>.pdf.\nKeep the style consistent with this repository.\n```\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n\u003Csummary>\u003Cstrong>Install as a skill (symlink)\u003C\u002Fstrong>\u003C\u002Fsummary>\n\nFrom the repository root, run:\n\n| Agent       | Commands |\n|------------|----------|\n| **Cursor** | `mkdir -p ~\u002F.cursor\u002Fskills` then `ln -s \"$(pwd)\u002Fscientific-figure-making\" ~\u002F.cursor\u002Fskills\u002Fscientific-figure-making` |\n| **Claude Code** | `mkdir -p ~\u002F.claude\u002Fskills` then `ln -s \"$(pwd)\u002Fscientific-figure-making\" ~\u002F.claude\u002Fskills\u002Fscientific-figure-making` |\n| **Codex**  | `mkdir -p ~\u002F.codex\u002Fskills` then `ln -s \"$(pwd)\u002Fscientific-figure-making\" ~\u002F.codex\u002Fskills\u002Fscientific-figure-making` |\n\nRestart the agent (or refresh its skill list) after linking. You can then invoke or cite the skill by name in addition to using path-based references when the repo is open.\n\n\u003C\u002Fdetails>\n\n## Related Papers\n\u003Cdetails>\n\u003Csummary>ImmunoStruct\u003C\u002Fsummary>\n\n[![nature](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fnature-machine_intelligence-gold)](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-025-01163-y)\n[![PDF](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPDF-DADBDD)](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs42256-025-01163-y.pdf)\n[![Huggingface](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDataset-ImmunoStruct-orange)](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FChenLiu1996\u002FImmunoStruct)\n[![Huggingface](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FModel-ImmunoStruct-orange)](https:\u002F\u002Fhuggingface.co\u002FChenLiu1996\u002FImmunoStruct)\n[![GitHub Stars](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002FKrishnaswamyLab\u002FImmunoStruct.svg?style=social\\&label=Stars)](https:\u002F\u002Fgithub.com\u002FKrishnaswamyLab\u002FImmunoStruct)\n```bibtex\n@article{givechian2026immunostruct,\n  title={ImmunoStruct enables multimodal deep learning for immunogenicity prediction},\n  author={Givechian, Kevin Bijan and Rocha, Jo{\\~a}o Felipe and Liu, Chen and Yang, Edward and Tyagi, Sidharth and Greene, Kerrie and Ying, Rex and Caron, Etienne and Iwasaki, Akiko and Krishnaswamy, Smita},\n  journal={Nature Machine Intelligence},\n  volume={8},\n  pages={70--83},\n  year={2026},\n  publisher={Nature Publishing Group UK London}\n}\n```\n\n\u003C\u002Fdetails>\n\u003Cdetails>\n\u003Csummary>Dispersion\u003C\u002Fsummary>\n\n[![OpenReview](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FOpenReview-eeeeee)](https:\u002F\u002Fopenreview.net\u002Fforum?id=pd6A7jB5D6)\n[![ICML 2026](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICML_2026-purple)](https:\u002F\u002Fopenreview.net\u002Fpdf?id=pd6A7jB5D6)\n[![arXiv](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FarXiv-Dispersion-firebrick)](https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.00217)\n[![PDF](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPDF-DADBDD)](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2602.00217)\n[![GitHub Stars](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002FChenLiu-1996\u002FLM-Dispersion.svg?style=social\\&label=Stars)](https:\u002F\u002Fgithub.com\u002FChenLiu-1996\u002FLM-Dispersion)\n```bibtex\n@inproceedings{liu2026dispersion,\n  title={Dispersion loss counteracts embedding condensation and improves generalization in small language models},\n  author={Liu, Chen and Sun, Xingzhi and Xiao, Xi and Van Tassel, Alexandre and Xu, Ke and Reimann, Kristof and Liao, Danqi and Gerstein, Mark and Wang, Tianyang and Wang, Xiao and Krishnaswamy, Smita},\n  booktitle={International conference on machine learning},\n  year={2026},\n  organization={PMLR}\n}\n```\n\n\u003C\u002Fdetails>\n\u003Cdetails>\n\u003Csummary>RNAGenScape\u003C\u002Fsummary>\n\n[![arXiv](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FarXiv-RNAGenScape-firebrick)](https:\u002F\u002Farxiv.org\u002Fabs\u002F2510.24736)\n[![PDF](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPDF-DADBDD)](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2510.24736)\n```bibtex\n@article{liao2025rnagenscape,\n  title={RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics},\n  author={Liao, Danqi and Liu, Chen and Sun, Xingzhi and Tang, Di{\\'e} and Wang, Haochen and Youlten, Scott and Gopinath, Srikar Krishna and Lee, Haejeong and Strayer, Ethan C and Giraldez, Antonio J and Krishnaswamy, Smita},\n  journal={arXiv preprint arXiv:2510.24736},\n  year={2025}\n}\n```\n\n\u003C\u002Fdetails>\n\u003Cdetails>\n\u003Csummary>brainteaser\u003C\u002Fsummary>\n\n[![OpenReview](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FOpenReview-eeeeee)](https:\u002F\u002Fopenreview.net\u002Fforum?id=3oQDkmW72a)\n[![NeurIPS 2025](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNeurIPS_2025-purple)](https:\u002F\u002Fopenreview.net\u002Fpdf?id=3oQDkmW72a)\n[![HuggingFace Dataset](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDataset-brainteaser-orange)](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FChenLiu1996\u002FBrainteaser)\n[![arXiv](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FarXiv-brainteaser-firebrick)](https:\u002F\u002Farxiv.org\u002Fabs\u002F2505.10844)\n[![PDF](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPDF-DADBDD)](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2505.10844)\n[![GitHub Stars](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fstephenxia1\u002Fbrainteasers.svg?style=social\\&label=Stars)](https:\u002F\u002Fgithub.com\u002Fstephenxia1\u002Fbrainteasers)\n```bibtex\n@article{han2025creativity,\n  title={Creativity or Brute Force? Using Brainteasers as a Window into the Problem-Solving Abilities of Large Language Models},\n  author={Han, Simeng and Dai, Howard and Xia, Stephen and Zhang, Grant and Liu, Chen and Chen, Lichang and Nguyen, Hoang Huy and Mei, Hongyuan and Mao, Jiayuan and McCoy, R. Thomas},\n  journal={Advances in neural information processing systems},\n  year={2025}\n}\n```\n\n\u003C\u002Fdetails>\n","这个项目提供了一套Python脚本，用于生成高质量的科学图表，适用于顶级AI会议和期刊的论文发表。其核心功能包括创建条形图、雷达图、趋势图、热力图以及3D球体等多种类型的图表，特别适合需要在学术出版物中展示复杂数据结构与分析结果的研究人员使用。技术上，该项目利用了Python丰富的可视化库来实现这些功能，并且部分图表还展示了如何结合其他工具完成更复杂的视觉呈现。对于希望提升自己论文图形质量或者寻找高效绘图解决方案的科研工作者来说，这是一个非常实用的资源。",2,"2026-06-11 03:56:58","high_star"]