From richer biodiversity and benefits for pollinators, to carbon storage in soils, while balancing hay yields for grazing livestock, the study published in Nature Communications by researchers at Lancaster University and The University of Manchester, in collaboration with the Universities of Yale and Bergen, shows that using combinations of different restoration techniques can markedly enhance the restoration of grasslands.

Estimating the pose of hand-held objects is a critical and challenging problem in robotics and computer vision. While leveraging multi-modal RGB and depth data is a promising solution, existing approaches still face challenges due to hand-induced occlusions and multimodal data fusion. In a new study, researchers developed a novel deep learning framework that addresses these issues by introducing a novel vote-based fusion module and a hand-aware pose estimation module.