PV-SSD: A Projection and Voxel-based Double Branch Single-Stage 3D Object Detector

LIDAR-based 3D object detection and classification is crucial for autonomous driving. However, inference in real-time from extremely sparse 3D data poses a formidable challenge. To address this issue, a common approach is to project point clouds onto a bird's-eye or perspective view, effectively converting them into an image-like data format. However, this excessive compression of point cloud data often leads to the loss of information. This paper proposes a 3D object detector based on voxel and projection double branch feature extraction (PV-SSD) to address the problem of information loss. We add voxel features input containing rich local semantic information, which is fully fused with the projected features in the feature extraction stage to reduce the local information loss caused by projection. A good performance is achieved compared to the previous work. In addition, this paper makes the following contributions: 1) a voxel feature extraction method with variable receptive fields is proposed; 2) a feature point sampling method by weight sampling is used to filter out the feature points that are more conducive to the detection task; 3) the MSSFA module is proposed based on the SSFA module. To verify the effectiveness of our method, we designed comparison experiments

Study on Characteristics of Water Flow Under Different Riverbed Structures in River Narrowing Section

In this paper, a numerical simulation research on the flow characteristics of dammed river under four kinds of riverbed structures is studied by using the turbulence model and the VOF model. The results show that: ① Due to the influence of the riverbed structure, the water flow vortex area of different riverbed structures is obviously different. The backwater and the reflux are the most obvious in a simulated bucket basin model of step-pool structure. ② The average velocity of the section of the bucket basin riverbed and the clustered riverbed is small, and the steady flow velocity is not large. Combined with the base pressure distribution of the riverbed, the pressure distribution of the bucket basin riverbed and ribbed riverbed is relatively regular. This shows that the bucket basin structure is of great help to improve the stability of riverbed. ③ The bucket basin model has better effect of energy dissipation than the other three types

Study on Characteristics of Water Flow Under Different Riverbed Structures in River Narrowing Section

In this paper, a numerical simulation research on the flow characteristics of dammed river under four kinds of riverbed structures is studied by using the turbulence model and the VOF model. The results show that: ① Due to the influence of the riverbed structure, the water flow vortex area of different riverbed structures is obviously different. The backwater and the reflux are the most obvious in a simulated bucket basin model of step-pool structure. ② The average velocity of the section of the bucket basin riverbed and the clustered riverbed is small, and the steady flow velocity is not large. Combined with the base pressure distribution of the riverbed, the pressure distribution of the bucket basin riverbed and ribbed riverbed is relatively regular. This shows that the bucket basin structure is of great help to improve the stability of riverbed. ③ The bucket basin model has better effect of energy dissipation than the other three types

An Integrative Analysis of the Immune Features of Inactivated SARS-CoV-2 Vaccine (CoronaVac)

Currently, an inactivated vaccine has been widely used with encouraging results as a prophylactic agent against COVID-19 infection, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants. However, in vitro SARS-CoV-2 vaccine-specific immune features remain elusive, hindering the promotion of a third dose of the vaccine. Here, we present a detailed in vitro immune cellular response and large-scale multi-omics analysis for peripheral blood mononuclear cells (PBMCs) from participants vaccinated with CoronaVac (Sinovac Life Sciences, Beijing, China) and recovered participants from COVID-19. The mean titers of SARS-CoV-2 serum-neutralizing antibodies were significantly increased after the boosting immunization (Day 45) compared to the unimmunized state. We observed that type-1 helper T cells (Th1) tended to dominate after the first dose of vaccine, while humoral immune responses became dominant after the second dose due to the activation of type-2 helper T cell (Th2), memory B cells, and plasmablasts. T follicular helper cells (Tfh) involved in antibody production were activated after the first dose and were maintained for the observed time points. Single-cell RNA sequencing of PBMCs revealed specific changes in cell compositions and gene expression in immunized participants. Multi-omics analysis also demonstrated that CoronaVac-specific serum proteins, plasma metabolites, and plasma lipid changes were skewed to those changes in convalescent patients. Collectively, we provide a comprehensive understanding of CoronaVac-specific in vitro immune features

DNN acceleration in vehicle edge computing with mobility-awareness: A synergistic vehicle–edge and edge–edge framework

In recent years, vehicular networks have seen a proliferation of applications and services such as image tagging, lane detection, and speech recognition. Many of these applications rely on Deep Neural Networks (DNNs) and demand low-latency computation. To meet these requirements, Vehicular Edge Computing (VEC) has been introduced to augment the abundant computation capacity of vehicular networks to complement limited computation resources on vehicles. Nevertheless, offloading DNN tasks to MEC (Multi-access Edge Computing) servers effectively and efficiently remains a challenging topic due to the dynamic nature of vehicular mobility and varying loads on the servers. In this paper, we propose a novel and efficient distributed DNN Partitioning And Offloading (DPAO), leveraging the mobility of vehicles and the synergy between vehicle–edge and edge–edge computing. We exploit the variations in both computation time and output data size across different layers of DNN to make optimized decisions for accelerating DNN computations while reducing the transmission time of intermediate data. In the meantime, we dynamically partition and offload tasks between MEC servers based on their load differences. We have conducted extensive simulations and testbed experiments to demonstrate the effectiveness of DPAO. The evaluation results show that, compared to offloaded all tasks to MEC server, DPAO reduces the latency of DNN tasks by 2.4x. DPAO with queue reservation can further reduce the task average completion time by 10%.</p