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Material point method is an efficient and promising method for simulating complex stuff. Not used much in Astro, a lot in gaming, cartoons etc.... Worth having a look in comparison with SPH in simulation (for example those connected to the HERAS mission)

beautiful! anybody knows of similar trials to fool EO data classifiers?

I am not aware of any similar trials, yet this study remind me of generative adversarial talking heads https://arxiv.org/pdf/1803.07716.pdf https://www.youtube.com/watch?v=p1b5aiTrGzY

Let's keep a eye on this... Animals use odor cues to navigate through their environments, helping them locate targets and assess danger. Much of how animal brains organize, read out, and respond to odor stimuli across spatial and temporal scales is not well understood. To tackle these questions, Odor2Action uses a highly interdisciplinary team science approach. Our work uses fruit fly, honeybee, and mouse models to determine how neural representations of odor are generated, reformatted, and translated to generate useful behaviors that guide how animals interact with their environment.

reminds me of the methan smelling source finding study we did ...

This is a social sciences paper trying to make use of ML. Quote from text: "Social scientists studying the life course must find a way to reconcile a widespread belief that understanding has been generated by these data-as demonstrated by more than 750 published journal articles using the Fragile Families data (10)-with the fact that the very same data could not yield accurate predictions of these important outcomes." "(...) In other words, the submissions were much better at predicting each other than at predicting the truth."

an important message to learn from