DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving

Safety is the primary priority of autonomous driving. Nevertheless, no published dataset currently supports the direct and explainable safety evaluation for autonomous driving. In this work, we propose DeepAccident, a large-scale dataset generated via a realistic simulator containing diverse accident scenarios that frequently occur in real-world driving. The proposed DeepAccident dataset contains 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset with 40k annotated samples. In addition, we propose a new task, end-to-end motion and accident prediction, based on the proposed dataset, which can be used to directly evaluate the accident prediction ability for different autonomous driving algorithms. Furthermore, for each scenario, we set four vehicles along with one infrastructure to record data, thus providing diverse viewpoints for accident scenarios and enabling V2X (vehicle-to-everything) research on perception and prediction tasks. Finally, we present a baseline V2X model named V2XFormer that demonstrates superior performance for motion and accident prediction and 3D object detection compared to the single-vehicle model

Dynamic Voxel Grid Optimization for High-Fidelity RGB-D Supervised Surface Reconstruction

Direct optimization of interpolated features on multi-resolution voxel grids has emerged as a more efficient alternative to MLP-like modules. However, this approach is constrained by higher memory expenses and limited representation capabilities. In this paper, we introduce a novel dynamic grid optimization method for high-fidelity 3D surface reconstruction that incorporates both RGB and depth observations. Rather than treating each voxel equally, we optimize the process by dynamically modifying the grid and assigning more finer-scale voxels to regions with higher complexity, allowing us to capture more intricate details. Furthermore, we develop a scheme to quantify the dynamic subdivision of voxel grid during optimization without requiring any priors. The proposed approach is able to generate high-quality 3D reconstructions with fine details on both synthetic and real-world data, while maintaining computational efficiency, which is substantially faster than the baseline method NeuralRGBD.Comment: For the project, see https://yanqingan.github.io

Abnormal event detection in crowded scenes using sparse representation

We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given a collection of normal training examples, e.g., an image sequence or a collection of local spatio-temporal patches, we propose the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. To condense the over-completed normal bases into a compact dictionary, a novel dictionary selection method with group sparsity constraint is designed, which can be solved by standard convex optimization. Observing that the group sparsity also implies a low rank structure, we reformulate the problem using matrix decomposition, which can handle large scale training samples by reducing the memory requirement at each iteration from O(k2) to O(k) where k is the number of samples. We use the columnwise coordinate descent to solve the matrix decomposition represented formulation, which empirically leads to a similar solution to the group sparsity formulation. By designing different types of spatio-temporal basis, our method can detect both local and global abnormal events. Meanwhile, as it does not rely on object detection and tracking, it can be applied to crowded video scenes. By updating the dictionary incrementally, our method can be easily extended to online event detection. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our method.Accepted versio

Sparse reconstruction cost for abnormal event detection

We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given an over-complete normal basis set (e.g., an image sequence or a collection of local spatio-temporal patches), we introduce the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. To condense the size of the dictionary, a novel dictionary selection method is designed with sparsity consistency constraint. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. Our method provides a unified solution to detect both local abnormal events (LAE) and global abnormal events (GAE). We further extend it to support online abnormal event detection by updating the dictionary incrementally. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our algorithm. 1

Effect of arsenic trioxide chemotherapy on survival time in liver cancer patients undergoing liver transplantation

ObjectiveTo investigate whether arsenic trioxide preventive chemotherapy can prolong the survival time of patients with primary liver cancer undergoing liver transplantation. MethodsA retrospective analysis was performed for the clinical data of the patients who underwent liver transplantation in Department of Liver Transplantation in Changzheng Hospital from January to December, 2015, and among these patients, 35(observation group) received the chemotherapeutic regimen of epirubicin D1+5-fluorouracil (D1-5) and 35(control group) received the chemotherapeutic regimen of arsenic trioxide (D1-14). Hematological examinations were performed at the beginning and ending of each cycle of treatment, including routine blood test, hepatic and renal function, and tumor markers, and drug side-effects were observed. Chest CT, liver CT, or MRI was performed at the beginning of every two cycles to record tumor recurrence, survival, and death time. The chi-square test was used for comparison of categorical data between groups, the Kaplan-Meier method was used to plot survival curves, and the log-rank test was used to compare survival curves between groups. ResultsIn the observation group, 3 patients experienced a transient increase in alanine aminotransferase after the last chemotherapy; 30 patients experienced the symptoms of water-sodium retention including mild edema in the face and lower limbs in the late stage of treatment, which were improved after diuretic treatment or at the end of treatment. In the control group, 24 patients experienced varying degrees of gastrointestinal reactions such as poor appetite, nausea, and vomiting, which were improved at the end of chemotherapy. The control group had a 1-year survival rate of 85.7% (30/35), a 2-year survival rate of 47.4% (18/35), and a 3-year survival rate of 22.9% (8/35), and the observation group had a 1-year survival rate of 91.4% (32/35), a 2-year survival rate of 83.9% (26/31), and a 3-year survival rate of 57.1% (12/21). There was no significant difference in 1-year survival rate between the two groups (χ2=2.258, P＜0.05), and the observation group had significantly higher 2- and 3-year survival rates than the control group (χ2=7.786 and 6.720, both P＜0.05). Survival curves also showed that the observation group had significantly higher 2- and 3-year survival rates than the control group (χ2=6.573, P＜0.05).ConclusionArsenic trioxide has been used for a long time, and with modern and scientific administration, it can improve the survival time of patients with liver cancer undergoing liver transplantation

Abnormal event detection in crowded scenes using sparse representation

We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given a collection of normal training examples, e.g., an image sequence or a collection of local spatio-temporal patches, we propose the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. To condense the over-completed normal bases into a compact dictionary, a novel dictionary selection method with group sparsity constraint is designed, which can be solved by standard convex optimization. Observing that the group sparsity also implies a low rank structure, we reformulate the problem using matrix decomposition, which can handle large scale training samples by reducing the memory requirement at each iteration from O(k2) to O(k) where k is the number of samples. We use the columnwise coordinate descent to solve the matrix decomposition represented formulation, which empirically leads to a similar solution to the group sparsity formulation. By designing different types of spatio-temporal basis, our method can detect both local and global abnormal events. Meanwhile, as it does not rely on object detection and tracking, it can be applied to crowded video scenes. By updating the dictionary incrementally, our method can be easily extended to online event detection. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our method.Accepted versio

Self-supervised online metric learning with low rank constraint for scene categorization

Conventional visual recognition systems usually train an image classifier in a bath mode with all training data provided in advance. However, in many practical applications, only a small amount of training samples are available in the beginning and many more would come sequentially during online recognition. Because the image data characteristics could change over time, it is important for the classifier to adapt to the new data incrementally. In this paper, we present an online metric learning method to address the online scene recognition problem via adaptive similarity measurement. Given a number of labeled data followed by a sequential input of unseen testing samples, the similarity metric is learned to maximize the margin of the distance among different classes of samples. By considering the low rank constraint, our online metric learning model not only can provide competitive performance compared with the state-of-the-art methods, but also guarantees convergence. A bi-linear graph is also defined to model the pair-wise similarity, and an unseen sample is labeled depending on the graph-based label propagation, while the model can also self-update using the more confident new samples. With the ability of online learning, our methodology can well handle the large-scale streaming video data with the ability of incremental self-updating. We evaluate our model to online scene categorization and experiments on various benchmark datasets and comparisons with state-of-the-art methods demonstrate the effectiveness and efficiency of our algorithm.Accepted versio

Identification of a Novel Essential Two-Component Signal Transduction System, YhcSR, in Staphylococcus aureus

Two-component signal transduction systems play an important role in the ability of bacteria to adapt to various environments by sensing changes in their habitat and by altering gene expression. In this study, we report a novel two-component system, YhcSR, in Staphylococcus aureus which is required for bacterial growth in vitro. We found that the down-regulation of yhcSR expression by induced yhcS antisense RNA can inhibit and terminate bacterial growth. Moreover, without complementary yhcS or yhcR, no viable yhcS or yhcR gene replacement mutant was recoverable. Collectively, these results demonstrated that the YhcSR regulatory system is indispensable for S. aureus growth in culture. Moreover, induced yhcS antisense RNA selectively increased bacterial susceptibility to phosphomycin. These data suggest that YhcSR probably modulates the expression of genes critical for bacterial survival and may be a potential target for the development of novel antibacterial agents

Exploring the Microbial Dynamics of Organic Matter Degradation and Humification during Co-Composting of Cow Manure and Bedding Material Waste

The present study investigated the effects of bedding material (BM) waste on physicochemical properties, organic matter (OM) degradation, microbial community structure and metabolic function during composting. The results showed that bedding material (CK-0, S1-40%, S2-25%) optimized the composting conditions for lignocellulose and OM biodegradation. The highest OM degradation and humic substance (HS) synthesis rates were observed in the 40% BM addition group. Firmicutes was more abundant in the bedding material addition groups, whereas Proteobacteria was more abundant in the group without bedding material. Functional prediction showed higher carbohydrate and amino acid metabolism in the BM groups than that in control group. Animal and plant pathogens were almost eliminated, and saprotrophs were the dominant fungal trophic modes after 40% BM addition composting. Cellulose, hemicellulose, and organic matter had strong associations with microbial communities, such as Lysinibacillus and Corynebacterium (bacteria), compared to the associations of Aspergillus, Candida, and Sordariomycetes (fungi) (p value < 0.05). Network analysis revealed closer microbial community interactions in 40% BM addition group than in other groups. These findings provide detailed information about the coupling of material conversion, of bacterial and fungal succession during composting, and that bedding materials waste can also be used as an effective compost amendment