Tectonic is a modernized, self-contained TeX/LaTeX engine that compiles .tex documents to PDF or HTML. It is built on a fork of XeTeX and integrates BibTeX, font shaping via HarfBuzz/FreeType, SVG-to-HTML conversion, and full PDF output via xdvipdfmx. The architecture is a Rust workspace with multiple crates implementing engines, I/O abstraction, bundle management, and C library bridges. The CLI tool provides subcommands (build, watch, new, bundle, dump, show) and supports workspace configuration via Tectonic.toml. The system is production-ready, with extensive test coverage, fuzzing, and support for cross-compilation. While the core TeX engine remains in C/C++ with global state, the Rust layers provide clean abstractions (IoProvider, StatusBackend, CachableBundle) that are reusable and well-architected. The codebase is actively maintained, with regular commits and CI/CD pipelines, though some C components show age and legacy patterns.
This repository is a comprehensive, curated bibliography and knowledge base for the field of active learning. It organizes hundreds of research papers by conference (ICML, NeurIPS, CVPR, etc.), journal (JMLR, TPAMI, etc.), thesis, and content topic (problem settings, deep active learning, applications, etc.). The repository also provides taxonomies, summaries, and links to papers. It is not a software application or data pipeline; it is a structured reference list intended for researchers and practitioners. The content is entirely in Markdown files, making it trivially reproducible and easy to navigate. The repository is actively maintained with recent commits and a growing star count.
This repository is a comprehensive, curated bibliography for the field of dataset distillation, maintained by three researchers. It aggregates over 400 papers organized by methodology (gradient matching, distribution matching, generative approaches, etc.) and application domain (continual learning, privacy, medical, federated, graph, NLP, video, etc.). The project provides a GitHub Pages website (index.html) that renders the paper list with links to code, project pages, and BibTeX citations, plus a sidebar for navigation. Supporting Python scripts (transform_record.py, validate_articles.py, catch_latest.py) manage the data pipeline: parsing a custom update format, validating article records, and merging latest additions. The repository is a hybrid of a data curation project (structured paper database) and a static web application (client-side rendering). While the code is simple and functional, there are data quality issues such as trailing whitespace in URLs, mismatched citation keys, and future-dated entries, indicating manual update processes that are not fully automated. The project is actively maintained with frequent additions, but the lack of pinned dependencies and automated CI validation limits reproducibility.
This repository is the public release of the SLAM Handbook, a comprehensive book on Simultaneous Localization and Mapping (SLAM) to be published by Cambridge University Press. It contains the book manuscript (main.pdf), LaTeX style files for acronyms and bibliography strings, and a bug report template. The repository is primarily a document project rather than a software application, but the LaTeX infrastructure (SH_acronyms.sty and SH_string.bib) serves as a reusable library for typesetting SLAM-related documents. The book covers foundations, practical implementations, and spatial AI, with contributions from numerous experts. The repository is actively maintained with incremental releases and community feedback mechanisms via issues and discussions.
This repository is a curated bibliography and roadmap for Quantum Machine Learning (QML). It contains chronological lists of papers, books, and reviews from 2007 to 2026, organized into yearly markdown files. The content is entirely reference links with basic metadata. While there is no executable code, data pipelines, or analytical workflows, the repository serves as an authoritative, up-to-date resource for researchers entering or tracking the QML field. It also includes a workforce section and a separate reviews page. The repository is actively maintained with recent updates and follows a consistent structural pattern across years.
This repository is a curated, community-driven directory of academic computer science papers, organized by topic (e.g., distributed systems, cryptography, machine learning). It primarily consists of README files listing papers with links to hosted PDFs or external sources, along with a subset of PDF files stored directly in the repo. The only executable code is a simple bash script (`scripts/download.sh`) that scrapes markdown files for PDF URLs and downloads them. There are no data pipelines, models, or analytics workflows. The value lies in the curated bibliography, not in any software implementation. As such, it is not a typical software application or data research project; it is a reference resource for practitioners and researchers.
This repository is a curated 'awesome list' (bibliography) of research papers, datasets, and software tools at the intersection of artificial intelligence and molecular dynamics/conformational analysis. It contains no executable code, data files, or notebooks—only a single README.md that organizes hundreds of references by topic (e.g., neural network potentials, force fields, generative models for protein conformations, AlphaFold-based methods). The repo serves as a comprehensive literature index for researchers in computational chemistry and structural biology, but it is not a functional software project or data pipeline. No raw data, transformation scripts, or analytical models are present; reproducibility is not applicable.