3D single object tracking in LiDAR point clouds (LiDAR SOT) plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching. However, LiDAR point clouds are usually textureless and incomplete, which hinders effective appearance matching. Besides, previous methods greatly overlook the critical motion clues among targets. In this work, beyond 3D Siamese tracking, we introduce a motion-centric paradigm to handle LiDAR SOT from a new perspective. Following this paradigm, we propose a matching-free two-stage tracker M 2 -Track. At the 1 st -stage, M 2 -Track localizes the target within successive frames via motion transformation. Then it refines the target box through motion-assisted shape completion at the 2 nd -stage. Due to the motion-centric nature, our method shows its impressive generalizability with limited training labels and provides good differentiability for end-to-end cycle training. This inspires us to explore semi-supervised LiDAR SOT by incorporating a pseudo-label-based motion augmentation and a self-supervised loss term. Under the fully-supervised setting, extensive experiments confirm that M 2 -Track significantly outperforms previous state-of-the-arts on three large-scale datasets while running at 57FPS (∼ 8%, ∼ 17% and ∼ 22% precision gains on KITTI, NuScenes, and Waymo Open Dataset respectively). While under the semi-supervised setting, our method performs on par with or even surpasses its fully-supervised counterpart using fewer than half labels from KITTI. Further analysis verifies each component's effectiveness and shows the motion-centric paradigm's promising potential for auto-labeling and unsupervised domain adaptation. The code is available at https://github.com/Ghostish/Open3DSOT.

Numerous methods have been described that allow the visualization of the data matrix. But all suffer from a common problem - observing the data points in the context of the attributes is either impossible or inaccurate. We describe a method that allows these types of comprehensive layouts. We achieve it by combining two similarity matrices typically used in isolation - the matrix encoding the similarity of the attributes and the matrix encoding the similarity of the data points. This combined matrix yields two of the four submatrices needed for a full multi-dimensional scaling type layout. The remaining two submatrices are obtained by creating a fused similarity matrix - one that measures the similarity of the data points with respect to the attributes, and vice versa. The resulting layout places the data objects in direct context of the attributes and hence we call it the data context map. It allows users to simultaneously appreciate (1) the similarity of data objects, (2) the similarity of attributes in the specific scope of the collection of data objects, and (3) the relationships of data objects with attributes and vice versa. The contextual layout also allows data regions to be segmented and labeled based on the locations of the attributes. This enables, for example, the map's application in selection tasks where users seek to identify one or more data objects that best fit a certain configuration of factors, using the map to visually balance the tradeoffs.

Mumps presents a serious threat to public health in China. We conducted a descriptive analysis to identify the epidemiological characteristics of mumps in Shandong Province. Spatial autocorrelation and space-time scan analyses were utilized to detect spatial-temporal clusters. From 2005 to 2014, 115745 mumps cases were reported in Shandong, with an average male-to-female ratio of 1.94. Mumps occurred mostly in spring (32.17% of all reported cases) and in children aged 5 to 9 (40.79% of all reported cases). The Moran’s I test was significant and local indicators of spatial autocorrelation (LISA) analysis revealed significant spatial clusters with high incidence. The results showed that the mid-west of Shandong Province and some coastal regions (Qingdao City and Weihai City) were high-risk areas, particularly in the center of the Jining City and the junction of Dongying City, Binzhou City and Zibo City. The results could assist local and national public health agencies in formulating better public health strategic planning and resource allocation.

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