Update paper.md

paper.md

@@ -34,15 +34,15 @@ bibliography: paper.bib
 
 # Statement of need
 
-It is essential in scientific research to maintain standardized benchmark datasets following the findable, accessible, interoperable, and reproducible (FAIR) data principles (see `@Chen:2021euv`), practices for using the data, and methods for evaluating and comparing different algorithms. This can often be difficult in high energy physics (HEP) because of the broad set of formats in which data is released and the expert knowledge required to parse the relevant information. The `JetNet` Python package aims to facilitate this by providing a standard interface and format for HEP datasets, integrated with PyTorch `[@NEURIPS2019_9015]`, to improve accessibility for both HEP experts and new or interdisciplinary researchers looking to do ML. Furthermore, by providing standard formats and implementations for evaluation metrics, results are more easily reproducible, and models are more easily assessed and benchmarked.
+It is essential in scientific research to maintain standardized benchmark datasets following the findable, accessible, interoperable, and reproducible (FAIR) data principles (see @Chen:2021euv), practices for using the data, and methods for evaluating and comparing different algorithms. This can often be difficult in high energy physics (HEP) because of the broad set of formats in which data is released and the expert knowledge required to parse the relevant information. The `JetNet` Python package aims to facilitate this by providing a standard interface and format for HEP datasets, integrated with PyTorch [@NEURIPS2019_9015], to improve accessibility for both HEP experts and new or interdisciplinary researchers looking to do ML. Furthermore, by providing standard formats and implementations for evaluation metrics, results are more easily reproducible, and models are more easily assessed and benchmarked.
 
 ## Impact
 
-The impact of `JetNet` is demonstrated by the surge in ML and HEP research in recent months facilitated by the package, including in the areas of generative adversarial networks `[@Kansal:2021cqp]`, transformers `[@Kach:2022uzq; @Kansal:2022spb; @Kach:2023rqw]`, diffusion models `[@Leigh:2023toe; @Mikuni:2023dvk]`, and equivariant networks `[@Hao:2022zns; @Buhmann:2023pmh]`, all accessing datasets, metrics, and more through `JetNet`.
+The impact of `JetNet` is demonstrated by the surge in ML and HEP research in recent months facilitated by the package, including in the areas of generative adversarial networks [@Kansal:2021cqp], transformers [@Kach:2022uzq; @Kansal:2022spb; @Kach:2023rqw], diffusion models [@Leigh:2023toe; @Mikuni:2023dvk], and equivariant networks [@Hao:2022zns; @Buhmann:2023pmh], all accessing datasets, metrics, and more through `JetNet`.
 
 ## Future Work
 
-Future work will expand the package to additional dataset loaders, including detector-level data, and different machine learning backends such as JAX `[@jax2018github]`. Improvements to the performance, such as optional lazy loading of large datasets, are also planned.
+Future work will expand the package to additional dataset loaders, including detector-level data, and different machine learning backends such as JAX [@jax2018github]. Improvements to the performance, such as optional lazy loading of large datasets, are also planned.
 
 
 # Acknowledgements