Millions of metric tons of plastics are produced annually and transported from land to the oceans. Finding the fate of the plastic debris will help define the impacts of plastic pollution in the ocean. Here, we report the abundances of microplastic in the deepest part of the world's ocean. We found that microplastic abundances in hadal bottom waters range from 2.06 to 13.51 pieces per litre, several times higher than those in open ocean subsurface water. Moreover, microplastic abundances in hadal sediments of the Mariana Trench vary from 200 to 2200 pieces per litre, distinctly higher than those in most deep sea sediments. These results suggest that manmade plastics have contaminated the most remote and deepest places on the planet. The hadal zone is likely one of the largest sinks for microplastic debris on Earth, with unknown but potentially damaging impacts on this fragile ecosystem.
Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming increasingly useful. Despite the great success of the generic object detection methods trained on ground-to-ground images, a huge performance drop is observed when they are directly applied to images captured by UAVs. The unsatisfactory performance is owing to many UAV-specific nuisances, such as varying flying altitudes, adverse weather conditions, dynamically changing viewing angles, etc. Those nuisances constitute a large number of fine-grained domains, across which the detection model has to stay robust. Fortunately, UAVs will record meta-data that depict those varying attributes, which are either freely available along with the UAV images, or can be easily obtained. We propose to utilize those free meta-data in conjunction with associated UAV images to learn domain-robust features via an adversarial training framework dubbed Nuisance Disentangled Feature Transform (NDFT), for the specific challenging problem of object detection in UAV images, achieving a substantial gain in robustness to those nuisances. We demonstrate the effectiveness of our proposed algorithm, by showing state-ofthe-art performance (single model) on two existing UAVbased object detection benchmarks. The code is available at https:// github.com/ TAMU-VITA/ UAV-NDFT.
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