Levenshtein Distance Embedding with Poisson Regression for DNA Storage
Authors: Xiang Wei, Alan J.X. Guo, Sihan Sun, Mengyi Wei, Wei Yu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through comprehensive experiments on real DNA storage data, we demonstrate the superior performance of the proposed method compared to state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | 1 Center for Applied Mathematics, Tianjin University, No. 92, Weijin Road, Tianjin, 300072, China 2 China Mobile Research Institute, No. 32, Xuanwumen West Street, Beijing, 100053, China |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides links to the dataset but does not state that the code for their method is open-source or provide a link to it. |
| Open Datasets | Yes | The data can be accessed through https://github.com/ Team Erlich/dna-fountain and https://www.ebi.ac.uk/ena/data/ view/PRJEB19305. |
| Dataset Splits | No | The paper mentions 'training and testing sets' but does not specify details about a validation set or the exact percentages/counts for data splits. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions using different network structures and loss functions, and discusses embedding dimension. However, it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other system-level training settings in the main text. It states 'Further details can be found in the Appendices', indicating that such details are not in the main paper. |