Estimating PM2.5 in the Beijing-Tianjin-Hebei Region Using MODIS AOD Products from 2014 to 2015

Fine particulate matter with a diameter less than 2.5 μm (PM2.5) has harmful impacts on regional climate, economic development and public health. The high PM2.5 concentrations in China’s urban areas are mainly caused by combustion of coal and gasoline, industrial pollution and unknown/uncertain sources. The Beijing-Tianjin-Hebei (BTH) region with a land area of 218,000 km2, which contains 13 cities, is the biggest urbanized region in northern China. The huge population (110 million, 8% of the China’s population), local heavy industries and vehicle emissions have resulted in severe air pollution. To monitor ground-level PM2.5 concentration, the Chinese government spent significant expense in building more than 1500 in-situ stations (79 stations in the BTH region). However, most of these stations are situated in urban areas. Besides, each station can only represent a limited area around that station, which leaves the vast rural land out of monitoring. In this situation, geographic information system and remote sensing can be used as complementary tools. Traditional models have used 10 km MODIS Aerosol Optical Depth (AOD) product and proved the statistical relationship between AOD and PM2.5. In 2014, the 3 km MODIS AOD product was released which made PM2.5 estimation with a higher resolution became possible. This study presents an estimation on PM2.5 distribution in the BTH region from September 2014 to August 2015 by combining the MODIS satellite data, ground measurements of PM2.5, and meteorological documents. Firstly, the 3 km and 10 km MODIS AOD products were validated with AErosol RObotic NETwork (AERONET AOD. Then the MLR and GWR models were employed respectively to estimate PM2.5 concentrations using ground measurements and two MODIS AOD products, meteorological datasets and land use information. Seasonal and regional analyses were also followed to make a comparative study on strengths and weaknesses between the 3 km and 10 km AOD products. Finally, the number of non-accidental deaths attributed to the long-term exposure of PM2.5 in the BTH region was estimated spatially. The results demonstrated that the 10 km AOD product provided results with a higher accuracy and greater coverage, although the 3 km AOD product could provide more information about the spatial variations of PM2.5 estimation. Additionally, compared with the global regression, the geographically weighed regression model was able to improve the estimation results. Finally, it was estimated that more than 30,000 people died in the BTH region during the study period attributed to the excessive PM2.5 concentrations

Circular Accessible Depth: A Robust Traversability Representation for UGV Navigation

In this paper, we present the Circular Accessible Depth (CAD), a robust traversability representation for an unmanned ground vehicle (UGV) to learn traversability in various scenarios containing irregular obstacles. To predict CAD, we propose a neural network, namely CADNet, with an attention-based multi-frame point cloud fusion module, Stability-Attention Module (SAM), to encode the spatial features from point clouds captured by LiDAR. CAD is designed based on the polar coordinate system and focuses on predicting the border of traversable area. Since it encodes the spatial information of the surrounding environment, which enables a semi-supervised learning for the CADNet, and thus desirably avoids annotating a large amount of data. Extensive experiments demonstrate that CAD outperforms baselines in terms of robustness and precision. We also implement our method on a real UGV and show that it performs well in real-world scenarios.Comment: 13 pages, 8 figure

Splitting of surface defect partition functions and integrable systems

We study Bethe/gauge correspondence at the special locus of Coulomb moduli where the integrable system exhibits the splitting of degenerate levels. For this investigation, we consider the four-dimensional pure $\mathcal{N}=2$ supersymmetric $U(N)$ gauge theory, with a half-BPS surface defect constructed with the help of an orbifold or a degenerate gauge vertex. We show that the non-perturbative Dyson-Schwinger equations imply the Schr\"odinger-type and the Baxter-type differential equations satisfied by the respective surface defect partition functions. At the special locus of Coulomb moduli the surface defect partition function splits into parts. We recover the Bethe/gauge dictionary for each summand.Comment: 34 pages, 2 figures; v2. published versio

Evaluation of Hybrid VMAT Advantages and Robustness Considering Setup Errors Using Surface Guided Dose Accumulation for Internal Lymph Mammary Nodes Irradiation of Postmastectomy Radiotherapy

ObjectivesSetup error is a key factor affecting postmastectomy radiotherapy (PMRT) and irradiation of the internal mammary lymph nodes is the most investigated aspect for PMRT patients. In this study, we evaluated the robustness, radiobiological, and dosimetric benefits of the hybrid volumetric modulated arc therapy (H-VMAT) planning technique based on the setup error in dose accumulation using a surface-guided system for radiation therapy.MethodsWe retrospectively selected 32 patients treated by a radiation oncologist and evaluated the clinical target volume (CTV), including internal lymph node irradiation (IMNIs), and considered the planning target volume (PTV) margin to be 5 mm. Three different planning techniques were evaluated: tangential-VMAT (T-VMAT), intensity-modulated radiation therapy (IMRT), and H-VMAT. The interfraction and intrafraction setup errors were analyzed in each field and the accumulated dose was evaluated as the patients underwent daily surface-guided monitoring. These parameters were included while evaluating CTV coverage, the dose required for the left anterior descending artery (LAD) and the left ventricle (LV), the normal tissue complication probability (NTCP) for the heart and lungs, and the second cancer complication probability (SCCP) for contralateral breast (CB).ResultsWhen the setup error was accounted for dose accumulation, T-VMAT (95.51%) and H-VMAT (95.48%) had a higher CTV coverage than IMRT (91.25%). In the NTCP for the heart, H-VMAT (0.04%) was higher than T-VMAT (0.01%) and lower than IMRT (0.2%). However, the SCCP (1.05%) of CB using H-VMAT was lower than that using T-VMAT (2%) as well as delivery efficiency. And T-VMAT (3.72) and IMRT (10.5).had higher plan complexity than H-VMAT (3.71).ConclusionsIn this study, based on the dose accumulation of setup error for patients with left-sided PMRT with IMNI, we found that the H-VMAT technique was superior for achieving an optimum balance between target coverage, OAR dose, complication probability, plan robustness, and complexity

CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP

Contrastive Language-Image Pre-training (CLIP) achieves promising results in 2D zero-shot and few-shot learning. Despite the impressive performance in 2D, applying CLIP to help the learning in 3D scene understanding has yet to be explored. In this paper, we make the first attempt to investigate how CLIP knowledge benefits 3D scene understanding. We propose CLIP2Scene, a simple yet effective framework that transfers CLIP knowledge from 2D image-text pre-trained models to a 3D point cloud network. We show that the pre-trained 3D network yields impressive performance on various downstream tasks, i.e., annotation-free and fine-tuning with labelled data for semantic segmentation. Specifically, built upon CLIP, we design a Semantic-driven Cross-modal Contrastive Learning framework that pre-trains a 3D network via semantic and spatial-temporal consistency regularization. For the former, we first leverage CLIP's text semantics to select the positive and negative point samples and then employ the contrastive loss to train the 3D network. In terms of the latter, we force the consistency between the temporally coherent point cloud features and their corresponding image features. We conduct experiments on SemanticKITTI, nuScenes, and ScanNet. For the first time, our pre-trained network achieves annotation-free 3D semantic segmentation with 20.8% and 25.08% mIoU on nuScenes and ScanNet, respectively. When fine-tuned with 1% or 100% labelled data, our method significantly outperforms other self-supervised methods, with improvements of 8% and 1% mIoU, respectively. Furthermore, we demonstrate the generalizability for handling cross-domain datasets. Code is publicly available https://github.com/runnanchen/CLIP2Scene.Comment: CVPR 202

LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion

LiDAR-camera fusion methods have shown impressive performance in 3D object detection. Recent advanced multi-modal methods mainly perform global fusion, where image features and point cloud features are fused across the whole scene. Such practice lacks fine-grained region-level information, yielding suboptimal fusion performance. In this paper, we present the novel Local-to-Global fusion network (LoGoNet), which performs LiDAR-camera fusion at both local and global levels. Concretely, the Global Fusion (GoF) of LoGoNet is built upon previous literature, while we exclusively use point centroids to more precisely represent the position of voxel features, thus achieving better cross-modal alignment. As to the Local Fusion (LoF), we first divide each proposal into uniform grids and then project these grid centers to the images. The image features around the projected grid points are sampled to be fused with position-decorated point cloud features, maximally utilizing the rich contextual information around the proposals. The Feature Dynamic Aggregation (FDA) module is further proposed to achieve information interaction between these locally and globally fused features, thus producing more informative multi-modal features. Extensive experiments on both Waymo Open Dataset (WOD) and KITTI datasets show that LoGoNet outperforms all state-of-the-art 3D detection methods. Notably, LoGoNet ranks 1st on Waymo 3D object detection leaderboard and obtains 81.02 mAPH (L2) detection performance. It is noteworthy that, for the first time, the detection performance on three classes surpasses 80 APH (L2) simultaneously. Code will be available at \url{https://github.com/sankin97/LoGoNet}.Comment: Accepted by CVPR202

Rethinking Range View Representation for LiDAR Segmentation

LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point- or voxel-based methods as they often yield better performance than the traditional range view representation. In this work, we unveil several key factors in building powerful range view models. We observe that the "many-to-one" mapping, semantic incoherence, and shape deformation are possible impediments against effective learning from range view projections. We present RangeFormer -- a full-cycle framework comprising novel designs across network architecture, data augmentation, and post-processing -- that better handles the learning and processing of LiDAR point clouds from the range view. We further introduce a Scalable Training from Range view (STR) strategy that trains on arbitrary low-resolution 2D range images, while still maintaining satisfactory 3D segmentation accuracy. We show that, for the first time, a range view method is able to surpass the point, voxel, and multi-view fusion counterparts in the competing LiDAR semantic and panoptic segmentation benchmarks, i.e., SemanticKITTI, nuScenes, and ScribbleKITTI.Comment: ICCV 2023; 24 pages, 10 figures, 14 tables; Webpage at https://ldkong.com/RangeForme

UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase

Point-, voxel-, and range-views are three representative forms of point clouds. All of them have accurate 3D measurements but lack color and texture information. RGB images are a natural complement to these point cloud views and fully utilizing the comprehensive information of them benefits more robust perceptions. In this paper, we present a unified multi-modal LiDAR segmentation network, termed UniSeg, which leverages the information of RGB images and three views of the point cloud, and accomplishes semantic segmentation and panoptic segmentation simultaneously. Specifically, we first design the Learnable cross-Modal Association (LMA) module to automatically fuse voxel-view and range-view features with image features, which fully utilize the rich semantic information of images and are robust to calibration errors. Then, the enhanced voxel-view and range-view features are transformed to the point space,where three views of point cloud features are further fused adaptively by the Learnable cross-View Association module (LVA). Notably, UniSeg achieves promising results in three public benchmarks, i.e., SemanticKITTI, nuScenes, and Waymo Open Dataset (WOD); it ranks 1st on two challenges of two benchmarks, including the LiDAR semantic segmentation challenge of nuScenes and panoptic segmentation challenges of SemanticKITTI. Besides, we construct the OpenPCSeg codebase, which is the largest and most comprehensive outdoor LiDAR segmentation codebase. It contains most of the popular outdoor LiDAR segmentation algorithms and provides reproducible implementations. The OpenPCSeg codebase will be made publicly available at https://github.com/PJLab-ADG/PCSeg.Comment: ICCV 2023; 21 pages; 9 figures; 18 tables; Code at https://github.com/PJLab-ADG/PCSe

Development of a nested PCR assay for specific detection of Metschnikowia bicuspidata infecting Eriocheir sinensis

In recent years, the “milky disease” caused by Metschnikowia bicuspidata has seriously affected the Eriocheir sinensis culture industry. Discovering and blocking the transmission route has become the key to controlling this disease. The existing polymerase chain reaction (PCR) detection technology for M. bicuspidata uses the ribosomal DNA (rDNA) sequence, but low sensitivity and specificity lead to frequent false detections. We developed a highly specific and sensitive nested PCR method to detect M. bicuspidata, by targeting the hyphally regulated cell wall protein (HYR) gene. This nested HYR-PCR produced a single clear band, showed no cross-reaction with other pathogens, and was superior to rDNA-PCR in specificity and sensitivity. The sensitivity of nested HYR-PCR (6.10 × 101 copies/μL) was greater than those of the large subunit ribosomal RNA gene (LSU rRNA; 6.03 × 104 copies/μL) and internal transcribed spacer (ITS; 6.74 × 105 copies/μL) PCRs. The nested HYR-PCR also showed a higher positivity rate (71.1%) than those obtained with LSU rRNA (16.7%) and ITS rDNA (24.4%). In conclusion, we developed a new nested HYR-PCR method for the specific and sensitive detection of M. bicuspidata infection. This will help to elucidate the transmission route of M. bicuspidata and to design effective management and control measures for M. bicuspidata disease