Lars Rosenbaum

TL;DR: In this article, the authors systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving and provide an overview of on-board sensors on test vehicles, open datasets, and background information for object detection.

TL;DR: This work presents practical methods to capture uncertainties in a 3D vehicle detector for Lidar point clouds and shows that the epistemic uncertainty is related to the detection accuracy, whereas the aleatoric uncertainty is influenced by vehicle distance and occlusion.

TL;DR: Nontargeted metabolomics investigations led to the following recommendations for the preanalytical phase: test the blood collection tubes, avoid hemolysis, place whole blood immediately in ice water, use EDTA plasma, and preferably use nonrefrozen biobank samples.

TL;DR: From a plasma metabolomic pattern, particularly lyso-PCs are able to separate metabolically benign from malignant NAFL in humans and may highlight important pathways in the pathogenesis of fatty liver–induced insulin resistance.

TL;DR: In this article, a multi-loss function is designed to incorporate uncertainty estimations predicted by auxiliary output layers to improve the accuracy of LiDAR 3D object detection. But, the proposed method ignores to train from noisy samples, and focuses more on informative ones.