The Size-Weight Illusion Is Not an Illusion When Picking the Best Objects to Throw

Heaviness perception involves a misperception of weight known, since the 19th century, as the Size-Weight Illusion ^1^. The larger of two objects of equal mass is reported to be lighter than the smaller when they are lifted. The illusion has been found to be reliable and robust. It persists even when people know that the masses are equal and handle objects properly ^2^. It has been exhibited by children of only 2 years of age ^3,4^. All this suggests that the effect might be intrinsic to humans. Although different hypotheses have been advanced to account for the illusion over the 100+ years it has been studied ^5-11^, its origin remains unknown. More recently, people's perception of optimal objects for long distance throwing was found to exhibit a size-weight relation similar to the illusion ^12,13^, greater weights were picked for larger objects and they were indeed thrown to the greatest distances. Here we show that the perception of heaviness (including the illusion) and perception of optimal objects for long distance throwing are in fact equivalent. Thus, the size-weight illusion has a useful application: optimal objects for throwing are picked by a thrower as having a particular heaviness, which is the best heaviness learned when learning to throw ^14,15^. Long distance throwing is a uniquely human ability that is understood to have enabled our species to survive and even thrive during the ice ages ^16-22^. The fact that the illusion is a functional component of human throwing skill adds credence to the idea that it is intrinsic to the species

A range extension for Haplomitrium mnioides (Lindb.) R.M.Schust.

Haplomitrium mnioides (Lindb.) R.M.Schust. is reported as new to Hainan Island. A continuous distribution of H. mnioides from west (Thailand) to east (Japan) is confirmed. Habitat pictures and a distribution map are provided

SEAN: Image Synthesis with Semantic Region-Adaptive Normalization

We propose semantic region-adaptive normalization (SEAN), a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image. Using SEAN normalization, we can build a network architecture that can control the style of each semantic region individually, e.g., we can specify one style reference image per region. SEAN is better suited to encode, transfer, and synthesize style than the best previous method in terms of reconstruction quality, variability, and visual quality. We evaluate SEAN on multiple datasets and report better quantitative metrics (e.g. FID, PSNR) than the current state of the art. SEAN also pushes the frontier of interactive image editing. We can interactively edit images by changing segmentation masks or the style for any given region. We can also interpolate styles from two reference images per region.Comment: Accepted as a CVPR 2020 oral paper. The interactive demo is available at https://youtu.be/0Vbj9xFgoU