Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling

Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has emerged as a crucial approach for domain-adaptive 3D object detection. While effective, existing DA methods suffer from a substantial drop in performance when applied to a multi-class training setting, due to the co-existence of low-quality pseudo labels and class imbalance issues. In this paper, we address this challenge by proposing a novel ReDB framework tailored for learning to detect all classes at once. Our approach produces Reliable, Diverse, and class-Balanced pseudo 3D boxes to iteratively guide the self-training on a distributionally different target domain. To alleviate disruptions caused by the environmental discrepancy (e.g., beam numbers), the proposed cross-domain examination (CDE) assesses the correctness of pseudo labels by copy-pasting target instances into a source environment and measuring the prediction consistency. To reduce computational overhead and mitigate the object shift (e.g., scales and point densities), we design an overlapped boxes counting (OBC) metric that allows to uniformly downsample pseudo-labeled objects across different geometric characteristics. To confront the issue of inter-class imbalance, we progressively augment the target point clouds with a class-balanced set of pseudo-labeled target instances and source objects, which boosts recognition accuracies on both frequently appearing and rare classes. Experimental results on three benchmark datasets using both voxel-based (i.e., SECOND) and point-based 3D detectors (i.e., PointRCNN) demonstrate that our proposed ReDB approach outperforms existing 3D domain adaptation methods by a large margin, improving 23.15% mAP on the nuScenes $\rightarrow$ KITTI task. The code is available at https://github.com/zhuoxiao-chen/ReDB-DA-3Ddet.Comment: Accepted by ICCV 2023, camera-read

Discovery of Novel Insulin Sensitizers: Promising Approaches and Targets

Insulin resistance is the undisputed root cause of type 2 diabetes mellitus (T2DM). There is currently an unmet demand for safe and effective insulin sensitizers, owing to the restricted prescription or removal from market of certain approved insulin sensitizers, such as thiazolidinediones (TZDs), because of safety concerns. Effective insulin sensitizers without TZD-like side effects will therefore be invaluable to diabetic patients. The specific focus on peroxisome proliferator-activated receptor γ- (PPARγ-) based agents in the past decades may have impeded the search for novel and safer insulin sensitizers. This review discusses possible directions and promising strategies for future research and development of novel insulin sensitizers and describes the potential targets of these agents. Direct PPARγ agonists, selective PPARγ modulators (sPPARγMs), PPARγ-sparing compounds (including ligands of the mitochondrial target of TZDs), agents that target the downstream effectors of PPARγ, along with agents, such as heat shock protein (HSP) inducers, 5′-adenosine monophosphate-activated protein kinase (AMPK) activators, 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) selective inhibitors, biguanides, and chloroquines, which may be safer than traditional TZDs, have been described. This minireview thus aims to provide fresh perspectives for the development of a new generation of safe insulin sensitizers

Exploring Active 3D Object Detection from a Generalization Perspective

To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is a promising solution that learns to select only a small portion of unlabeled data to annotate, without compromising model performance. Our empirical study, however, suggests that mainstream uncertainty-based and diversity-based active learning policies are not effective when applied in the 3D detection task, as they fail to balance the trade-off between point cloud informativeness and box-level annotation costs. To overcome this limitation, we jointly investigate three novel criteria in our framework Crb for point cloud acquisition - label conciseness}, feature representativeness and geometric balance, which hierarchically filters out the point clouds of redundant 3D bounding box labels, latent features and geometric characteristics (e.g., point cloud density) from the unlabeled sample pool and greedily selects informative ones with fewer objects to annotate. Our theoretical analysis demonstrates that the proposed criteria align the marginal distributions of the selected subset and the prior distributions of the unseen test set, and minimizes the upper bound of the generalization error. To validate the effectiveness and applicability of \textsc{Crb}, we conduct extensive experiments on the two benchmark 3D object detection datasets of KITTI and Waymo and examine both one-stage (\textit{i.e.}, \textsc{Second}) and two-stage 3D detectors (i.e., Pv-rcnn). Experiments evidence that the proposed approach outperforms existing active learning strategies and achieves fully supervised performance requiring $1\%$ and $8\%$ annotations of bounding boxes and point clouds, respectively. Source code: https://github.com/Luoyadan/CRB-active-3Ddet.Comment: To appear in ICLR 202

Formation of Fan-spine Magnetic Topology through Flux Emergence and Subsequent Jet Production

Fan-spine magnetic structure, as a fundamental three-dimensional topology in magnetic reconnection theory, plays a crucial role in producing solar jets. However, how fan-spine configurations form in the solar atmosphere remains elusive. Using the Chinese H$\alpha$ Solar Explorer (CHASE) and the Solar Dynamics Observatory (SDO), we present a case study on the complete buildup of fan-spine topology driven by flux emergence and the subsequent jet production. Two fan-spine structures and the two associated null points are present. Variations in null-point heights and locations were tracked over time during flux emergence. The north fan-spine structure is found to be created through magnetic reconnection between the newly emerged flux and the background field. Gentle reconnection persistently occurs after formation of the north fan-spine structure, resulting in weak plasma outflows. Subsequently, as flux emergence and magnetic helicity injection continue, the formation and eruption of mini-filaments after reconnection at the quasi-separatrix layer between the two nulls trigger three homologous jets. The CHASE observations reveal that the circular flare ribbon, inner bright patch, and remote brightening all exhibit redshifted signatures during these jet ejections. This work unveils the key role of flux emergence in the formation of fan-spine topology, and highlights the importance of mini-filaments for subsequent jet production.Comment: 20 pages, 7 figures accepted by the ApJ