Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network

3D object detection task from lidar or camera sensors is essential for autonomous driving. Pioneer attempts at multi-modality fusion complement the sparse lidar point clouds with rich semantic texture information from images at the cost of extra network designs and overhead. In this work, we propose a novel semantic passing framework, named SPNet, to boost the performance of existing lidar-based 3D detection models with the guidance of rich context painting, with no extra computation cost during inference. Our key design is to first exploit the potential instructive semantic knowledge within the ground-truth labels by training a semantic-painted teacher model and then guide the pure-lidar network to learn the semantic-painted representation via knowledge passing modules at different granularities: class-wise passing, pixel-wise passing and instance-wise passing. Experimental results show that the proposed SPNet can seamlessly cooperate with most existing 3D detection frameworks with 1~5% AP gain and even achieve new state-of-the-art 3D detection performance on the KITTI test benchmark. Code is available at: https://github.com/jb892/SPNet.Comment: Accepted by ACMMM202

GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation

Birds-eye-view (BEV) semantic segmentation is critical for autonomous driving for its powerful spatial representation ability. It is challenging to estimate the BEV semantic maps from monocular images due to the spatial gap, since it is implicitly required to realize both the perspective-to-BEV transformation and segmentation. We present a novel two-stage Geometry Prior-based Transformation framework named GitNet, consisting of (i) the geometry-guided pre-alignment and (ii) ray-based transformer. In the first stage, we decouple the BEV segmentation into the perspective image segmentation and geometric prior-based mapping, with explicit supervision by projecting the BEV semantic labels onto the image plane to learn visibility-aware features and learnable geometry to translate into BEV space. Second, the pre-aligned coarse BEV features are further deformed by ray-based transformers to take visibility knowledge into account. GitNet achieves the leading performance on the challenging nuScenes and Argoverse Datasets. The code will be publicly available

ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects across video frames. Detection boxes serve as the basis of both 2D and 3D MOT. The inevitable changing of detection scores leads to object missing after tracking. We propose a hierarchical data association strategy to mine the true objects in low-score detection boxes, which alleviates the problems of object missing and fragmented trajectories. The simple and generic data association strategy shows effectiveness under both 2D and 3D settings. In 3D scenarios, it is much easier for the tracker to predict object velocities in the world coordinate. We propose a complementary motion prediction strategy that incorporates the detected velocities with a Kalman filter to address the problem of abrupt motion and short-term disappearing. ByteTrackV2 leads the nuScenes 3D MOT leaderboard in both camera (56.4% AMOTA) and LiDAR (70.1% AMOTA) modalities. Furthermore, it is nonparametric and can be integrated with various detectors, making it appealing in real applications. The source code is released at https://github.com/ifzhang/ByteTrack-V2.Comment: Code is available at https://github.com/ifzhang/ByteTrack-V2. arXiv admin note: text overlap with arXiv:2110.06864; substantial text overlap with arXiv:2203.06424 by other author

Soil Labile Organic Carbon Fractions and Soil Enzyme Activities After 10 Years of Continuous Fertilization and Wheat Residue Incorporation

Labile organic carbon (LOC) fractions and related enzyme activities in soils are considered to be early and sensitive indicators of soil quality changes. We investigated the influences of fertilization and residue incorporation on LOC fractions, enzyme activities, and the carbon pool management index (CPMI) in a 10-year field experiment. The experiment was composed of three treatments: (1) no fertilization (control), (2) chemical fertilizer application alone (F), and (3) chemical fertilizer application combined with incorporation of wheat straw residues (F + R). Generally, the F + R treatment led to the highest concentrations of the LOC fractions. Compared to the control treatment, the F + R treatment markedly enhanced potential activities of cellulase (CL), β-glucosidase (BG), lignin peroxidase (LiP), and manganese peroxidase (MnP), but decreased laccase (LA) potential activity. Partial least squares regression analysis suggested that BG and MnP activities had a positive impact on the light-fraction organic carbon (LFOC), permanganate-oxidizable carbon (POXC), and dissolved organic carbon (DOC) fractions, whereas laccase activity had a negative correlation with those fractions. In addition, the F + R treatment significantly increased the CPMI compared to the F and control treatments. These results indicated that combining fertilization with crop residues stimulates production of LOC and could be a useful approach for maintaining sustainable production capacity in lime concretion black soils along the Huai River region of China

Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes

Modern autonomous driving systems are typically divided into three main tasks: perception, prediction, and planning. The planning task involves predicting the trajectory of the ego vehicle based on inputs from both internal intention and the external environment, and manipulating the vehicle accordingly. Most existing works evaluate their performance on the nuScenes dataset using the L2 error and collision rate between the predicted trajectories and the ground truth. In this paper, we reevaluate these existing evaluation metrics and explore whether they accurately measure the superiority of different methods. Specifically, we design an MLP-based method that takes raw sensor data (e.g., past trajectory, velocity, etc.) as input and directly outputs the future trajectory of the ego vehicle, without using any perception or prediction information such as camera images or LiDAR. Our simple method achieves similar end-to-end planning performance on the nuScenes dataset with other perception-based methods, reducing the average L2 error by about 20%. Meanwhile, the perception-based methods have an advantage in terms of collision rate. We further conduct in-depth analysis and provide new insights into the factors that are critical for the success of the planning task on nuScenes dataset. Our observation also indicates that we need to rethink the current open-loop evaluation scheme of end-to-end autonomous driving in nuScenes. Codes are available at https://github.com/E2E-AD/AD-MLP.Comment: Technical report. Code is availabl

The parallax and 3D kinematics of water masers in the massive star-forming region G034.43+0.24

We report a trigonometric parallax measurement of 22 GHz water masers in the massive star-forming region G034.43+0.24 as part of the Bar and Spiral Structure Legacy (BeSSeL) Survey using the Very Long Baseline Array. The parallax is 0.330$\pm$50.018 mas, corresponding to a distance of $3.03^{+0.17}_{-0.16}$ kpc. This locates G034.43+0.24 near the inner edge of the Sagittarius spiral arm and at one end of a linear distribution of massive young stars which cross nearly the full width of the arm. The measured 3-dimensional motion of G034.43+0.24 indicates a near-circular Galactic orbit. The water masers display arc-like distributions, possibly bow shocks, associated with winds from one or more massive young stars

Transposable elements cause the loss of self-incompatibility in citrus

Self-incompatibility (SI) is a widespread prezygotic mechanism for flowering plants to avoid inbreeding depression and promote genetic diversity. Citrus has an S-RNase-based SI system, which was frequently lost during evolution. We previously identified a single nucleotide mutation in Sm-RNase, which is responsible for the loss of SI in mandarin and its hybrids. However, little is known about other mechanisms responsible for conversion of SI to self-compatibility (SC) and we identify a completely different mechanism widely utilized by citrus. Here, we found a 786-bp miniature inverted-repeat transposable element (MITE) insertion in the promoter region of the FhiS2-RNase in Fortunella hindsii Swingle (a model plant for citrus gene function), which does not contain the Sm-RNase allele but are still SC. We demonstrate that this MITE plays a pivotal role in the loss of SI in citrus, providing evidence that this MITE insertion prevents expression of the S-RNase; moreover, transgenic experiments show that deletion of this 786-bp MITE insertion recovers the expression of FhiS2-RNase and restores SI. This study identifies the first evidence for a role for MITEs at the S-locus affecting the SI phenotype. A family-wide survey of the S-locus revealed that MITE insertions occur frequently adjacent to S-RNase alleles in different citrus genera, but only certain MITEs appear to be responsible for the loss of SI. Our study provides evidence that insertion of MITEs into a promoter region can alter a breeding strategy and suggests that this phenomenon may be broadly responsible for SC in species with the S-RNase system

β-Elemene-induced autophagy protects human gastric cancer cells from undergoing apoptosis

<p>Abstract</p> <p>Background</p> <p>β-Elemene, a compound found in an herb used in traditional Chinese medicine, has shown promising anti-cancer effects against a broad spectrum of tumors. The mechanism by which β-elemene kills cells remains unclear. The aim of the present study is to investigate the anti-tumor effect of β-elemene on human gastric cancer cells and the molecular mechanism involved.</p> <p>Results</p> <p>β-Elemene inhibited the viability of human gastric cancer MGC803 and SGC7901 cells in a dose-dependent manner. The suppression of cell viability was due to the induction of apoptosis. A robust autophagy was observed in the cells treated with β-elemene; it was characterized by the increase of punctate LC3 dots, the cellular morphology, and the increased levels of LC3-II protein. Further study showed that β-elemene treatment up-regulated Atg5-Atg12 conjugated protein but had little effect on other autophagy-related proteins. PI3K/Akt/mTOR/p70S6K1 activity was inhibited by β-elemene. Knockdown of Beclin 1 with small interfering RNA, or co-treatment with the autophagy inhibitor, 3-methyladenine or chlorochine enhanced significantly the antitumor effects of β-elemene.</p> <p>Conclusions</p> <p>Our data provides the first evidence that β-elemene induces protective autophagy and prevents human gastric cancer cells from undergoing apoptosis. A combination of β-elemene with autophagy inhibitor might thus be a useful therapeutic option for advanced gastric cancer.</p

A portable RNA sequence whose recognition by a synthetic antibody facilitates structural determination

RNA crystallization and phasing represent major bottlenecks in RNA structure determination. Seeking to exploit antibody fragments as RNA crystallization chaperones, we have used an arginine-enriched synthetic Fab library displayed on phage to obtain Fabs against the class I ligase ribozyme. We solved the structure of a Fab–ligase complex at 3.1-Å resolution using molecular replacement with Fab coordinates, confirming the ribozyme architecture and revealing the chaperone's role in RNA recognition and crystal contacts. The epitope resides in the GAAACAC sequence that caps the P5 helix, and this sequence retains high-affinity Fab binding within the context of other structured RNAs. This portable epitope provides a new RNA crystallization chaperone system that easily can be screened in parallel to the U1A RNA-binding protein, with the advantages of a smaller loop and Fabs′ high molecular weight, large surface area and phasing power.National Institutes of Health (U.S.) (GM61835