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Why playing is no laughing matter for otters
Play behavior is not all'marshmallow science,' and more play can equal better health. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. From by Heide Island, PhD, to be published on 4/28/26 by Avery, an imprint of Penguin Publishing Group, a division of Penguin Random House, LLC. From behind a stand of frozen lupine, Patches, Crest, and Slash emerge onto the wetland. Moonshine reflects off the newly fallen snow, illuminating the predawn hour with a supernatural brightness. They halt beside a corrugated metal culvert, side by side, until Patches lurches forward and leaps onto the bank of Admirals Lake. Her landing fractures the frozen lakeshore, stamping an otter-sized divot. The two girls follow behind her, each landing with a loud crunch, leaving star-shaped bull's-eyes in the ice. The otters are out early, exploiting the cold; an icy lake makes for sluggish fish.
Generalized Data Weighting via Class-level Gradient Manipulation
Label noise and class imbalance are two major issues coexisting in real-world datasets. To alleviate the two issues, state-of-the-art methods reweight each instance by leveraging a small amount of clean and unbiased data. Yet, these methods overlook class-level information within each instance, which can be further utilized to improve performance. To this end, in this paper, we propose Generalized Data Weighting (GDW) to simultaneously mitigate label noise and class imbalance by manipulating gradients at the class level. To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately.