295 research outputs found
Discovery of drugs to combat covid-19 inspired by traditional Chinese medicine
Abstract Contributions from traditional knowledge and history have proven useful in recent years to advance drug discovery. In response to the emergence of covid-19, scientists revisited traditional Chinese medicine. This source of inspiration for drugs to treat this new disease is described here at three different levels: traditional Chinese medicinal herbs, traditional Chinese medical formulas, and traditional Chinese medical texts. Drug discovery inspired by traditional Chinese medicine still faces serious resistance for various reasons, including its system of formulas and clinical trial design. A perspective that includes related issues would benefit the reasonable application of traditional knowledge in drug research and development
Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise Binarization
Instance segmentation on point clouds is crucially important for 3D scene
understanding. Distance clustering is commonly used in state-of-the-art methods
(SOTAs), which is typically effective but does not perform well in segmenting
adjacent objects with the same semantic label (especially when they share
neighboring points). Due to the uneven distribution of offset points, these
existing methods can hardly cluster all instance points. To this end, we design
a novel divide and conquer strategy and propose an end-to-end network named
PBNet that binarizes each point and clusters them separately to segment
instances. PBNet divides offset instance points into two categories: high and
low density points (HPs vs.LPs), which are then conquered separately. Adjacent
objects can be clearly separated by removing LPs, and then be completed and
refined by assigning LPs via a neighbor voting method. To further reduce
clustering errors, we develop an iterative merging algorithm based on mean size
to aggregate fragment instances. Experiments on ScanNetV2 and S3DIS datasets
indicate the superiority of our model. In particular, PBNet achieves so far the
best AP50 and AP25 on the ScanNetV2 official benchmark challenge (Validation
Set) while demonstrating high efficiency
LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion
As an emerging technology and a relatively affordable device, the 4D imaging
radar has already been confirmed effective in performing 3D object detection in
autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point
clouds hinder further performance improvement, and in-depth studies about its
fusion with other modalities are lacking. On the other hand, most of the
camera-based perception methods transform the extracted image perspective view
features into the bird's-eye view geometrically via "depth-based splatting"
proposed in Lift-Splat-Shoot (LSS), and some researchers exploit other modals
such as LiDARs or ordinary automotive radars for enhancement. Recently, a few
works have applied the "sampling" strategy for image view transformation,
showing that it outperforms "splatting" even without image depth prediction.
However, the potential of "sampling" is not fully unleashed. In this paper, we
investigate the "sampling" view transformation strategy on the camera and 4D
imaging radar fusion-based 3D object detection. In the proposed model, LXL,
predicted image depth distribution maps and radar 3D occupancy grids are
utilized to aid image view transformation, called "radar occupancy-assisted
depth-based sampling". Experiments on VoD and TJ4DRadSet datasets show that the
proposed method outperforms existing 3D object detection methods by a
significant margin without bells and whistles. Ablation studies demonstrate
that our method performs the best among different enhancement settings
- …