Upload README.md

README.md

@@ -1,47 +1,61 @@
-# DataBack: Dataset of CNF Formulas and Backbone Variable Phases
+# DataBack: Dataset of SAT Formulas and Backbone Variable Phases
 
 ## What is DataBack
-`DataBack` is a dataset that consists of SAT CNF formulas, each labeled with the phases of its backbone variables. Within `DataBack`, there are two distinct subsets: the pre-training set, named `DataBack-PT`, and the fine-tuning set, named `DataBack-FT`. The state-of-the-art backbone extractor, `CadiBack`, has been employed to obtain the backbone labels. Due to the increased complexity of the fine-tuning formulas, we have allocated a timeout of 1,000 seconds for the pre-training formulas and 5,000 seconds for the fine-tuning ones.
+`DataBack` is a dataset that consists of SAT formulas in CNF format, each labeled with the phases of its backbone variables. Within `DataBack`, there are two distinct subsets: the pre-training set, named `DataBack-PT`, and the fine-tuning set, named `DataBack-FT`. The state-of-the-art backbone extractor called [`CadiBack`](https://wenxiwang.github.io/papers/cadiback.pdf) has been employed to obtain the backbone variable phases. We have allocated a timeout of 1,000 seconds for the pre-training formulas and 5,000 seconds for the fine-tuning ones due to the increased complexity of the fine-tuning formulas.
 
-The `DataBack` dataset has been employed to both pre-train and fine-tune our `NeuroBack` model, which has demonstrated significant improvements in SAT solving efficiency. For an in-depth exploration of `DataBack`, please refer to [our `NeuroBack` paper](https://arxiv.org/pdf/2110.14053.pdf).
+The `DataBack` dataset has been employed to both pre-train and fine-tune our `NeuroBack` model, which has demonstrated significant improvements in SAT solving efficiency. For an in-depth exploration of `DataBack`, please refer to our [`NeuroBack`](https://arxiv.org/pdf/2110.14053.pdf) or [`CadiBack`](https://wenxiwang.github.io/papers/cadiback.pdf) papers.
 
-## Authors
-Wenxi Wang, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Ken McMillan, Risto Miikkulainen
+## Contributors
+Wenxi Wang (wenxiw@utexas.edu), Yang Hu (huyang@utexas.edu)
 
-## Publication
-If you use `DataBack` in your research, please kindly cite [our paper](https://arxiv.org/pdf/2110.14053.pdf):
+## References
+If you use `DataBack` in your research, please kindly cite the following papers.
+
+[`NeuroBack`](https://arxiv.org/pdf/2110.14053.pdf) paper:
 ```bib
 @article{wang2023neuroback,
-  author       = {Wang, Wenxi and
-                  Hu, Yang and
-                  Tiwari, Mohit and
-                  Khurshid, Sarfraz and
-                  McMillan, Kenneth L. and
-                  Miikkulainen, Risto},
-  title        = {NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks},
+  author = {Wang, Wenxi and
+            Hu, Yang and
+            Tiwari, Mohit and
+            Khurshid, Sarfraz and
+            McMillan, Kenneth L. and
+            Miikkulainen, Risto},
+  title = {NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks},
   journal={arXiv preprint arXiv:2110.14053},
   year={2021}
 }
 ```
+
+[`CadiBack`](https://wenxiwang.github.io/papers/cadiback.pdf) paper:
+```bib
+@inproceedings{biere2023cadiback,
+  title={CadiBack: Extracting Backbones with CaDiCaL},
+  author={Biere, Armin and Froleyks, Nils and Wang, Wenxi},
+  booktitle={26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)},
+  year={2023},
+  organization={Schloss Dagstuhl-Leibniz-Zentrum f{\"u}r Informatik}
+}
+```
+
 ## Directory Structure
 ```
-|- original             # Original CNFs and their backbone variable phases
-|    |- cnf_pt.tar.gz   # CNFs for model pre-training
-|    |- bb_pt.tar.gz    # Backbone phases for pre-training CNFs
-|    |- cnf_ft.tar.gz   # CNFs for model fine-tuning
-|    |- bb_ft.tar.gz    # Backbone phases for fine-tuning CNFs
+|- original             # Original SAT formulas and their backbone variable phases
+|    |- cnf_pt.tar.gz   # SAT formulas (in CNF format) for model pre-training
+|    |- bb_pt.tar.gz    # Backbone phases for pre-training formulas
+|    |- cnf_ft.tar.gz   # SAT formulas (in CNF format) for model fine-tuning
+|    |- bb_ft.tar.gz    # Backbone phases for fine-tuning formulas
 |
-|- dual                      # Dual CNFs and their backbone variable phases
-|    |- dual_cnf_pt.tar.gz   # Dual CNFs for model pre-training
+|- dual                      # Dual SAT formulas and their backbone variable phases
+|    |- dual_cnf_pt.tar.gz   # Dual SAT formulas (in CNF format) for model pre-training
 |    |- dual_bb_pt.tar.gz    # Backbone phases for dual pre-training CNFs
-|    |- dual_cnf_ft.tar.gz   # Dual CNFs for model fine-tuning
+|    |- dual_cnf_ft.tar.gz   # Dual SAT formulas (in CNF format) for model fine-tuning
 |    |- dual_bb_ft.tar.gz    # Backbone phases for dual fine-tuning CNFs
 ```
 
 ## File Naming Convention
-In the original directory, each CNF tar file contains compressed CNF files named: `[cnf_name].[compression_format]`, where `[compression_format]` could be bz2, lzma, xz, gz, etc. Correspondingly, each backbone tar file comprises compressed backbone files named: `[cnf_name].backbone.xz`. It is important to note that a compressed CNF file will always share its `[cnf_name]` with its associated compressed backbone file.
+In the original directory, each CNF tar file (`cnf_*.tar.gz`) contains compressed CNF files named: `[cnf_name].[compression_format]`, where `[compression_format]` could be bz2, lzma, xz, gz, etc. Correspondingly, each backbone tar file (`bb_*.tar.gz`) comprises compressed backbone files named: `[cnf_name].backbone.xz`. It is important to note that a compressed CNF file will always share its `[cnf_name]` with its associated compressed backbone file.
 
-In the dual directory, the naming convention remains consistent, but with an added `d_` prefix for each compressed CNF or backbone file to indicate it pertains to a dual CNF formula.
+In the dual directory, the naming convention remains consistent, but with an added `dual_` prefix for each CNF tar file or backbone tar file, and an `d_` prefix for each compressed CNF or backbone file to indicate that it pertains to a dual SAT formula.
 
-## Contact
-Wenxi Wang (wenxiw@utexas.edu), Yang Hu (huyang@utexas.edu)
+## Format of the Extracted Backbone File 
+The extracted backbone file (`*.backbone`) adheres to the output format of [`CadiBack`](https://wenxiwang.github.io/papers/cadiback.pdf). Specifically, each line contains the letter 'b' followed by an integer. This integer represents the backbone variable ID and its associated phase.