@book{mdBook,
title = {mdBook Documentation},
url = {https://rust-lang.github.io/mdBook/},
publisher = {Springer International Publishing},
author = {Various Contributors},
date = {2015},
langid = {english},
abstract = {mdBook is a command line tool and Rust crate to create books using Markdown (as by the CommonMark specification) files. It's very similar to Gitbook but written in Rust.}
}
@book{mdBookWithAuthorsWithANDInTheirName,
title = {Bug when rendering authors that include the `and` substring in their names},
publisher = {None},
url = {https://github.com/francisco-perez-sorrosal/mdbook-bib/issues/44},
author = {Jane A. Doeander, John B. Doeanderson},
date = {2023},
langid = {english},
abstract = {What a book about nothing... But the names of the authors are important here and
their names should not be rendered split by the `and` substring appearing in their surnames.
This bug was reported by raspobabs in the URL above.}
}
@book{DUMMY:1,
author = {John, Doe},
title = {The Book without Title},
publisher = {Dummy Publisher},
year = {2100},
}
@article{10.1145/3508461,
author = {Ma, Karima and Gharbi, Michael and Adams, Andrew and Kamil, Shoaib and Li, Tzu-Mao and Barnes, Connelly and Ragan-Kelley, Jonathan},
title = {Searching for Fast Demosaicking Algorithms},
year = {2022},
issue_date = {October 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {41},
number = {5},
issn = {0730-0301},
url = {https://doi.org/10.1145/3508461},
doi = {10.1145/3508461},
abstract = {We present a method to automatically synthesize efficient, high-quality demosaicking algorithms, across a range of computational budgets, given a loss function and training data. It performs a multi-objective, discrete-continuous optimization which simultaneously solves for the program structure and parameters that best tradeoff computational cost and image quality. We design the method to exploit domain-specific structure for search efficiency. We apply it to several tasks, including demosaicking both Bayer and Fuji X-Trans color filter patterns, as well as joint demosaicking and super-resolution. In a few days on 8 GPUs, it produces a family of algorithms that significantly improves image quality relative to the prior state-of-the-art across a range of computational budgets from 10 s to 1000 s of operations per pixel (1 dB–3 dB higher quality at the same cost, or 8.5–200\texttimes{} higher throughput at same or better quality). The resulting programs combine features of both classical and deep learning-based demosaicking algorithms into more efficient hybrid combinations, which are bandwidth-efficient and vectorizable by construction. Finally, our method automatically schedules and compiles all generated programs into optimized SIMD code for modern processors.},
journal = {ACM Trans. Graph.},
month = may,
articleno = {172},
numpages = {18},
keywords = {data driven methods, neural architecture search, differentiable programming, domain specific programming, super-resolution, Demosaicking}
}