351 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
Analyzing dynamic disturbance fragmentation mechanism of surrounding rock in roadway roof
In this paper, a numerical simulation is conducted to analyze an engineering practice pertaining to the in panel 2347, the discussion is focused on the following aspects: the influence of fault mining activation on fragmentation of surrounding rock in roof, surrounding rock failure of unsupported roadway under static load alone, surrounding rock failure of unsupported roadway under dynamic load and static load combination, damage of surrounding rock in supporting roadway under dynamic load and static load combination. The results show that, for the roadway surrounding rock with obvious dynamic disturbance, the compressive stress value can eliminate the influence of the tensile stress wave generated by the reflection of the compressive stress disturbance wave on the roadway wall. In the roadway support, it should provide a certain compressive stress to the surrounding rock of the surrounding roadway wall. An anchor support surrounding rock can significantly inhibit the tensile crack, shear the crack expansion and dislocation slip of coal gang, and it can also alleviate the tensile, compressive and shear failure of the roof carbonaceous mudstone. Because the dynamic load has a significant damage to the carbonaceous mudstone between roof anchors, it is necessary to reduce the anchor spacing and row spacing or enhance the stiffness and active stress of the protective surface member
From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion
We consider the problem of Multi-view 3D Face Reconstruction (MVR) with
weakly supervised learning that leverages a limited number of 2D face images
(e.g. 3) to generate a high-quality 3D face model with very light annotation.
Despite their encouraging performance, present MVR methods simply concatenate
multi-view image features and pay less attention to critical areas (e.g. eye,
brow, nose and mouth). To this end, we propose a novel model called Deep Fusion
MVR (DF-MVR) and design a multi-view encoding to a single decoding framework
with skip connections, able to extract, integrate, and compensate deep features
with attention from multi-view images. In addition, we develop a multi-view
face parse network to learn, identify, and emphasize the critical common face
area. Finally, though our model is trained with a few 2D images, it can
reconstruct an accurate 3D model even if one single 2D image is input. We
conduct extensive experiments to evaluate various multi-view 3D face
reconstruction methods. Our proposed model attains superior performance,
leading to 11.4% RMSE improvement over the existing best weakly supervised
MVRs. Source codes are available in the supplementary materials
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
SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar
The 4D Millimeter wave (mmWave) radar is a promising technology for vehicle
sensing due to its cost-effectiveness and operability in adverse weather
conditions. However, the adoption of this technology has been hindered by
sparsity and noise issues in radar point cloud data. This paper introduces
spatial multi-representation fusion (SMURF), a novel approach to 3D object
detection using a single 4D imaging radar. SMURF leverages multiple
representations of radar detection points, including pillarization and density
features of a multi-dimensional Gaussian mixture distribution through kernel
density estimation (KDE). KDE effectively mitigates measurement inaccuracy
caused by limited angular resolution and multi-path propagation of radar
signals. Additionally, KDE helps alleviate point cloud sparsity by capturing
density features. Experimental evaluations on View-of-Delft (VoD) and
TJ4DRadSet datasets demonstrate the effectiveness and generalization ability of
SMURF, outperforming recently proposed 4D imaging radar-based
single-representation models. Moreover, while using 4D imaging radar only,
SMURF still achieves comparable performance to the state-of-the-art 4D imaging
radar and camera fusion-based method, with an increase of 1.22% in the mean
average precision on bird's-eye view of TJ4DRadSet dataset and 1.32% in the 3D
mean average precision on the entire annotated area of VoD dataset. Our
proposed method demonstrates impressive inference time and addresses the
challenges of real-time detection, with the inference time no more than 0.05
seconds for most scans on both datasets. This research highlights the benefits
of 4D mmWave radar and is a strong benchmark for subsequent works regarding 3D
object detection with 4D imaging radar
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
- …