A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation

Pairwise point cloud registration is a critical task for many applications, which heavily depends on finding correct correspondences from the two point clouds. However, the low overlap between input point clouds causes the registration to fail easily, leading to mistaken overlapping and mismatched correspondences, especially in scenes where non-overlapping regions contain similar structures. In this paper, we present a unified bird's-eye view (BEV) model for jointly learning of 3D local features and overlap estimation to fulfill pairwise registration and loop closure. Feature description is performed by a sparse UNet-like network based on BEV representation, and 3D keypoints are extracted by a detection head for 2D locations, and a regression head for heights. For overlap detection, a cross-attention module is applied for interacting contextual information of input point clouds, followed by a classification head to estimate the overlapping region. We evaluate our unified model extensively on the KITTI dataset and Apollo-SouthBay dataset. The experiments demonstrate that our method significantly outperforms existing methods on overlap estimation, especially in scenes with small overlaps. It also achieves top registration performance on both datasets in terms of translation and rotation errors.Comment: 8 pages. Accepted by ICRA-202

IKKβ Suppression of TSC1 Links Inflammation and Tumor Angiogenesis via the mTOR Pathway

SummaryTNFα has recently emerged as a regulator linking inflammation to cancer pathogenesis, but the detailed cellular and molecular mechanisms underlying this link remain to be elucidated. The tuberous sclerosis 1 (TSC1)/TSC2 tumor suppressor complex serves as a repressor of the mTOR pathway, and disruption of TSC1/TSC2 complex function may contribute to tumorigenesis. Here we show that IKKβ, a major downstream kinase in the TNFα signaling pathway, physically interacts with and phosphorylates TSC1 at Ser487 and Ser511, resulting in suppression of TSC1. The IKKβ-mediated TSC1 suppression activates the mTOR pathway, enhances angiogenesis, and results in tumor development. We further find that expression of activated IKKβ is associated with TSC1 Ser511 phosphorylation and VEGF production in multiple tumor types and correlates with poor clinical outcome of breast cancer patients. Our findings identify a pathway that is critical for inflammation-mediated tumor angiogenesis and may provide a target for clinical intervention in human cancer

The chordoma arised from ilium: A rare case report

Chordomas are malignant tumors that originate in embryonic notochordal remnants. The sacrum and skull are the most common sites; the mobile spine and other bones are extremely rare sites. We describe a 45-year-old man who presented with a lytic lesion in his left ilium. Imaging and pathology of a biopsy specimen suggested a malignant bone tumor; wide resection was accordingly performed. The morphology and immunohistochemistry of the operative specimen showed obvious characteristics of classic chordoma. To our knowledge, this is the first reported case of a chordoma originating in the ilium. Chordoma should therefore be considered in the differential diagnosis of lytic lesions in the ilium

Neighborhood Selection Synchronization Mechanism-Based Moving Source Localization Using UAV Swarm

To obtain the accurate time difference of arrival (TDOA) and frequency difference of arrival (FDOA) for passive localization in an unmanned aerial vehicle (UAV) swarm, UAV swarm network synchronization is necessary. However, most of the traditional distributed time synchronization protocols are based on iteration, which hinders efficiency improvement. High communication costs and long convergence times are often required in large-scale UAV swarm networks. This paper presents a neighborhood selection-all selection (NS-AS) synchronization mechanism-based moving source localization method for UAV swarms. First, the NS-AS synchronization mechanism is introduced, to model the UAV swarm network synchronization process. Specifically, the UAV neighbors are first grouped by sector, and the most representative neighbors are selected from each sector for the state update calculation. When the UAV swarm network reaches a fully connected state, the synchronization mechanism is switched to select all neighbors, to improve the convergence speed. Then, the TDOA-FDOA joint localization algorithm is employed to locate the moving radiation source. Through simulation, the effectiveness of the proposed method is verified by the system convergence and localization performance under different parameters. Experimental results show that the synchronization mechanism based on NS-AS effectively improves the convergence speed of the system while ensuring the accuracy of moving radiation source localization

Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm

The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise