There has been a remarkable increase in prescription rates of antipsychotics in children and adolescents in recent years. Their side effects are a neglected area of research in this population, despite its vulnerability. In this cross-sectional study, we compared the presence of side effects in 60 children and adolescents who had taken antipsychotic medication for less than 1 month and 66 who had been receiving treatment with antipsychotics for more than 12 months. Mean age for the total sample was 15.62 years (SD 1.85). Groups did not differ in age, gender, or diagnosis. A total of 21.7% of short-term treatment group patients and 37.9% of longer-term treatment group patients presented mild dyskinetic movements (p = 0.004). Hyperprolactinemia was present in 78.6% and 48.5% in the short-term and longer-term treatment groups, respectively. Body mass index (p < 0.001), cholesterol levels (p < 0.001), and low-density lipoprotein-cholesterol (LDL-C) (p = 0.018) were higher in the longer-term treatment group. The use of these drugs in these populations merits careful scrutiny.
LiDAR devices have become a key sensor for autonomous vehicles perception due to their ability to capture reliable geometry information. Indeed, approaches processing LiDAR data have shown an impressive accuracy for 3D object detection tasks, outperforming methods solely based on image inputs. However, the wide diversity of on-board sensor configurations makes the deployment of published algorithms into real platforms a hard task, due to the scarcity of annotated datasets containing laser scans. We present a method to generate new point clouds datasets as captured by a real LiDAR device. The proposed pipeline makes use of multiple frames to perform an accurate 3D reconstruction of the scene in the spherical coordinates system that enables the simulation of the sweeps of a virtual LiDAR sensor, configurable both in location and inner specifications. The similarity between real data and the generated synthetic clouds is assessed through a set of experiments performed using KITTI Depth and Object Benchmarks.
The rapid development of embedded hardware in autonomous vehicles broadens their computational capabilities, thus bringing the possibility to mount more complete sensor setups able to handle driving scenarios of higher complexity. As a result, new challenges such as multiple detections of the same object have to be addressed. In this work, a siamese network is integrated into the pipeline of a well-known 3D object detector approach to suppress duplicate proposals coming from different cameras via re-identification. Additionally, associations are exploited to enhance the 3D box regression of the object by aggregating their corresponding LiDAR frustums. The experimental evaluation on the nuScenes dataset shows that the proposed method outperforms traditional NMS approaches.
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