Sample-efficient active learning for materials informatics using integrated posterior variance

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This is a powerful but portable power option from Jackery for your home, camping, or road trips. It gives you a 2,200W output and 2,040Wh capacity through three AC ports, alongside a fast 100W USB-C PD port. And despite its high power capacity, it’s compact and lightweight thanks to EV-inspired cell-to-body technology that improves both efficiency and safety. It even has seamless 20ms switching, making it a reliable backup during outages, keeping essential devices running without any cut off period.

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One challenge is having enough training data. Another is that the training data needs to be free of contamination. For a model trained up till 1900, there needs to be no information from after 1900 that leaks into the data. Some metadata might have that kind of leakage. While it’s not possible to have zero leakage - there’s a shadow of the future on past data because what we store is a function of what we care about - it’s possible to have a very low level of leakage, sufficient for this to be interesting.

I suspect rather strongly that premodern people, too, experienced the physical world more acutely than I do, simply because my brain has been stewing in dopamine-spiking stimuli for four decades now, rarely pausing to touch the proverbial grass. Don’t get me wrong: I like the outdoors a lot. But my waking existence is spent mostly indoors in highly artificial spaces, whereas humans have typically spent the vast majority of their time outdoors in nature. Consider my ancestors, who, as best I can tell, were primarily illiterate peasants and smallhold farmers. They spent a significant proportion of their waking hours literally touching grass.

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