Localized intra‐articular delivery of anti‐inflammatory proteins can reduce inflammation in osteoarthritis but poses a challenge because of raid clearance within few hours of injection. A new class of polymer is developed that forms self‐assembled nanoparticles ranging from 300 to 900 nm and demonstrates particle size dependent prolonged retention in intra‐articular joint spaces compared to bolus protein over a period of 14 d.
Self-driving cars must detect other vehicles and pedestrians in 3D to plan safe routes and avoid collisions. State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit to domain idiosyncrasies, making them fail in new environmentsa serious problem if autonomous vehicles are meant to operate freely. In this paper, we propose a novel learning approach that drastically reduces this gap by fine-tuning the detector on pseudo-labels in the target domain, which our method generates while the vehicle is parked, based on replays of previously recorded driving sequences. In these replays, objects are tracked over time, and detections are interpolated and extrapolated-crucially, leveraging future information to catch hard cases. We show, on five autonomous driving datasets, that fine-tuning the object detector on these pseudo-labels substantially reduces the domain gap to new driving environments, yielding drastic improvements in accuracy and detection reliability.
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