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1. The far-reaching effects of genetic process in a keystone predator species, grey wolves

   https://doi.org/10.1126/sciadv.adc8724  

   Hoy, Sarah R.; Hedrick, Philip W.; Peterson, Rolf O.; Vucetich, Leah M.; Brzeski, Kristin E.; Vucetich, John A. (August 2023, Science Advances)

   Although detrimental genetic processes are known to adversely affect the viability of populations, little is known about how detrimental genetic processes in a keystone species can affect the functioning of ecosystems. Here, we assessed how changes in the genetic characteristics of a keystone predator, grey wolves, affected the ecosystem of Isle Royale National Park over two decades. Changes in the genetic characteristic of the wolf population associated with a genetic rescue event, followed by high levels of inbreeding, led to a rise and then fall in predation rates on moose, the primary prey of wolves and dominant mammalian herbivore in this system. Those changes in predation rate led to large fluctuations in moose abundance, which in turn affected browse rates on balsam fir, the dominant forage for moose during winter and an important boreal forest species. Thus, forest dynamics can be traced back to changes in the genetic characteristics of a predator population. 

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2. Eliminating artefacts in polarimetric images using deep learning

   https://doi.org/10.1093/mnras/stz3250  

   Paranjpye, D; Mahabal, A; Ramaprakash, A N; Panopoulou, G V; Cleary, K; Readhead, A C; Blinov, D; Tassis, K (February 2020, Monthly Notices of the Royal Astronomical Society)

   ABSTRACT Polarization measurements done using Imaging Polarimeters such as the Robotic Polarimeter are very sensitive to the presence of artefacts in images. Artefacts can range from internal reflections in a telescope to satellite trails that could contaminate an area of interest in the image. With the advent of wide-field polarimetry surveys, it is imperative to develop methods that automatically flag artefacts in images. In this paper, we implement a Convolutional Neural Network to identify the most dominant artefacts in the images. We find that our model can successfully classify sources with 98 per cent true positive and 97 per cent true negative rates. Such models, combined with transfer learning, will give us a running start in artefact elimination for near-future surveys like WALOP. 

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3. Understanding adversarial examples requires a theory of artefacts for deep learning

   https://doi.org/10.1038/s42256-020-00266-y  

   Buckner, Cameron (December 2020, Nature Machine Intelligence)

   null (Ed.)
4. Reaching a consensus with limited information

   https://doi.org/10.1016/j.sysconle.2023.105524  

   Zhu, Jingxuan; Lin, Yixuan; Liu, Ji; Morse, A. Stephen (June 2023, Systems & Control Letters)
5. Reaching a Consensus with Limited Information

   https://doi.org/10.1109/CDC51059.2022.9993039  

   Zhu, Jingxuan; Lin, Yixuan; Liu, Ji; Morse, A. Stephen (December 2022, 61st IEEE Conference on Decision and Control)