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   https://doi.org/10.1186/s12915-017-0391-5  

   Russell, James J.; Theriot, Julie A.; Sood, Pranidhi; Marshall, Wallace F.; Landweber, Laura F.; Fritz-Laylin, Lillian; Polka, Jessica K.; Oliferenko, Snezhana; Gerbich, Therese; Gladfelter, Amy; et al (December 2017, BMC Biology)
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   https://doi.org/10.1093/plcell/koad260  

   Mabry, Makenzie E; Abrahams, R Shawn; Al-Shehbaz, Ihsan A; Baker, William J; Barak, Simon; Barker, Michael S; Barrett, Russell L; Beric, Aleksandra; Bhattacharya, Samik; Carey, Sarah B; et al (October 2023, The Plant Cell)

   Abstract Model species continue to underpin groundbreaking plant science research. At the same time, the phylogenetic resolution of the land plant Tree of Life continues to improve. The intersection of these two research paths creates a unique opportunity to further extend the usefulness of model species across larger taxonomic groups. Here we promote the utility of the Arabidopsis thaliana model species, especially the ability to connect its genetic and functional resources, to species across the entire Brassicales order. We focus on the utility of using genomics and phylogenomics to bridge the evolution and diversification of several traits across the Brassicales to the resources in Arabidopsis, thereby extending scope from a model species by establishing a “model clade”. These Brassicales-wide traits are discussed in the context of both the model species Arabidopsis thaliana and the family Brassicaceae. We promote the utility of such a “model clade” and make suggestions for building global networks to support future studies in the model order Brassicales. 

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5. Multi-concept Model Immunization through Differentiable Model Merging

   https://doi.org/10.1609/aaai.v39i10.33145  

   Zheng, Amber Yijia; Yeh, Raymond A (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   Model immunization is an emerging direction that aims to mitigate the potential risk of misuse associated with open-sourced models and advancing adaptation methods. The idea is to make the released models' weights difficult to fine-tune on certain harmful applications, hence the name immunized. Recent work on model immunization focuses on the single-concept setting. However, in real-world situations, models need to be immunized against multiple concepts. To address this gap, we propose an immunization algorithm that, simultaneously, learns a single difficult initialization for adaptation methods over a set of concepts. We achieve this by incorporating a differentiable merging layer that combines a set of model weights adapted over multiple concepts.In our experiments, we demonstrate the effectiveness of multi-concept immunization by generalizing prior work's experiment setup of re-learning and personalization adaptation to multiple concepts. 

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