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1. Identifying the Central Figure of a Scientific Paper

   https://doi.org/10.1109/ICDAR.2019.00173  

   Yang, Sean T.; Lee, Po-Shen; Kazakova, Lia; Joshi, Abhishek; Oh, Bum Mook; West, Jevin D.; Howe, Bill (September 2019, 2019 International Conference on Document Analysis and Recognition (ICDAR))

   Publishers are increasingly using graphical abstracts to facilitate scientific search, especially across disciplinary boundaries. They are presented on various media, easily shared and information rich. However, very small amount of scientific publications are equipped with graphical abstracts. What can we do with the vast majority of papers with no selected graphical abstract? In this paper, we first hypothesize that scientific papers actually include a "central figure" that serve as a graphical abstract. These figures convey the key results and provide a visual identity for the paper. Using survey data collected from 6,263 authors regarding 8,353 papers over 15 years, we find that over 87% of papers are considered to contain a central figure, and that these central figures are primarily used to summarize important results, explain the key methods, or provide additional discussion. We then train a model to automatically recognize the central figure, achieving top-3 accuracy of 78% and exact match accuracy of 34%. We find that the primary boost in accuracy comes from figure captions that resemble the abstract. We make all our data and results publicly available at https://github.com/viziometrics/centraul_figure. Our goal is to automate central figure identification to improve search engine performance and to help scientists connect ideas across the literature. 

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2. Communicating over the Torn-Paper Channel

   https://doi.org/10.1109/GLOBECOM42002.2020.9348147  

   Shomorony, Ilan; Vahid, Alireza (December 2020, GLOBECOM 2020)

   null (Ed.)
3. Capacity of the Torn Paper Channel with Lost Pieces

   https://doi.org/10.1109/ISIT45174.2021.9518272  

   Ravi, Aditya Narayan; Vahid, Alireza; Shomorony, Ilan (July 2021, IEEE International Symposium on Information Theory)

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4. Flaw sensitivity of cellulose paper

   https://doi.org/10.1016/j.eml.2022.101865  

   Chen, Qiongyu; Chen, Bo; Jing, Shuangshuang; Liu, Yu; Li, Teng (October 2022, Extreme Mechanics Letters)
5. Torn-Paper Coding

   https://doi.org/10.1109/TIT.2021.3120920  

   Shomorony, Ilan; Vahid, Alireza (December 2021, IEEE Transactions on Information Theory)