More Like this

1. Collaborating with Undergraduates To Contribute to Biochemistry Community Resources

   https://doi.org/10.1021/acs.biochem.7b00872  

   Haas, Kathryn L.; Heemstra, Jennifer M.; Medema, Marnix H.; Charkoudian, Louise K. (November 2017, Biochemistry)
2. To attract or to oscillate: Validating dynamics with behavior..

   Murray, Keith (November 2023, 5th Conference on the Mathematical Theory of Deep Learning)
3. Learning To Rank Resources with GNN

   Ulugbek Ergashev, Eduard C. (January 2023, WWW)

   As the content on the Internet continues to grow, many new dynamically changing and heterogeneous sources of data constantly emerge. A conventional search engine cannot crawl and index at the same pace as the expansion of the Internet. Moreover, a large portion of the data on the Internet is not accessible to traditional search engines. Distributed Information Retrieval (DIR) is a viable solution to this as it integrates multiple shards (resources) and provides a unified access to them. Resource selection is a key component of DIR systems. There is a rich body of literature on resource selection approaches for DIR. A key limitation of the existing approaches is that they primarily use term-based statistical features and do not generally model resource-query and resource-resource relationships. In this paper, we propose a graph neural network (GNN) based approach to learning-to-rank that is capable of modeling resource-query and resource-resource relationships. Specifically, we utilize a pre-trained language model (PTLM) to obtain semantic information from queries and resources. Then, we explicitly build a heterogeneous graph to preserve structural information of query-resource relationships and employ GNN to extract structural information. In addition, the heterogeneous graph is enriched with resource-resource type of edges to further enhance the ranking accuracy. Extensive experiments on benchmark datasets show that our proposed approach is highly effective in resource selection. Our method outperforms the state-of-the-art by 6.4% to 42% on various performance metrics. 

   more » « less
4. Learning to Describe Scenes with Programs

   Yunchao Liu, Zheng Wu (January 2019, International Conference on Learning Representations)
5. Twenty Things to Make with Biology.

   Kafai, Y. B. (July 2020, Proceedings of Constructionism 2020)

   B. Tangney, J. Bryne (Ed.)

   A 1971 memo by Papert and Solomon introduced twenty things to do with a computer which became the foundation for constructionism. In this paper, we propose bringing constructionist activities into making with living materials. Significant developments in tools and methods have turned biology into a design science: it is now possible to make things with biology—or biodesign— rather than just observing processes and behaviours. Our list of twenty things to make with biology includes examples from making colours, toys, games, insulin, batteries, sensors and more. In the discussion, we review how making with biology addresses key affordances of constructionist learning: “tinkerability,” the ability to experiment; “perceptibility,” the immediacy of feedback on learning process; “expressivity,” the personal customization of products; and “usability,” the ability to use learning designs in everyday contexts. We conclude with an overview of accessible and affordable tools available to K-12 education. 

   more » « less