Multi-View 3D Object Detection Network for Autonomous Driving

This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes. We encode the sparse 3D point cloud with a compact multi-view representation. The network is composed of two subnetworks: one for 3D object proposal generation and another for multi-view feature fusion. The proposal network generates 3D candidate boxes efficiently from the bird's eye view representation of 3D point cloud. We design a deep fusion scheme to combine region-wise features from multiple views and enable interactions between intermediate layers of different paths. Experiments on the challenging KITTI benchmark show that our approach outperforms the state-of-the-art by around 25% and 30% AP on the tasks of 3D localization and 3D detection. In addition, for 2D detection, our approach obtains 10.3% higher AP than the state-of-the-art on the hard data among the LIDAR-based methods.Comment: To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 201

TECHNIQUES TO IMPROVE THE HIT RATE OF UNICAST NODE-TO-NODE (N2N) DELIVERY IN CHANNEL-HOPPING AND MULTI-HOP LOW-POWER AND LOSSY NETWORKS (LLNS)

In a large scale wireless mesh network, such as a Wireless Smart Utility Network (Wi-SUN), there can be many losses. For example, a transmitter typically does not know the availability of a receiver when transmitting, which can create losses. In another example, link quality measurements, such as expected transmission count (ETX), may not be well represented for every channel. In one instance, this proposal provides an improved scheduling technique that leverages Broadcast Interval (BI) information to determine receive (RX) and transmit (TX) plans for child nodes. Child nodes can select an appropriate parent node according to received schedules, which can facilitate download traffic propagation. In another instance, this proposal provides for the ability to facilitate fine management of ETX evaluation to improve the successful rate of packet delivery. Techniques of this proposal not only leverage a central control method, but also introduce a free contention mechanism through which a central node can announce acceptable time slots to sub-nodes. The central node may avoid assigning smaller slots for each sub-node in order to allow the sub-nodes to compete for communications. Thus, techniques herein may combine various advantages for both deterministic networks and mesh networks

HIGH EFFICIENCY SLEEP SCHEDULE FOR BATTERY BASED ENDPOINTS IN LOW-POWER AND LOSSY NETWORKS

Techniques are described herein for a high efficiency sleep schedule method to save energy for Wireless Mesh Network (WMN). Nodes are not distinguished as Full Function Device (FFD) or Reduced Function Device (RFD), as the strategy depends on hop count and the parent node’s sleep schedule. This is an improvement over current WMNs, which do not account for power management for Battery Based Endpoint (BBP) devices

Finite automata for testing uniqueness of Eulerian trails

We investigate the condition under which the Eulerian trail of a digraph is unique, and design a finite automaton to examine it. The algorithm is effective, for if the condition is violated, it will be noticed immediately without the need to trace through the whole trail

Optimizing plant transporter expression in Xenopus oocytes

BACKGROUND: Rapid improvements in DNA synthesis technology are revolutionizing gene cloning and the characterization of their encoded proteins. Xenopus laevis oocytes are a commonly used heterologous system for the expression and functional characterization of membrane proteins. For many plant proteins, particularly transporters, low levels of expression can limit functional activity in these cells making it difficult to characterize the protein. Improvements in synthetic DNA technology now make it quick, easy and relatively cheap to optimize the codon usage of plant cDNAs for Xenopus. We have tested if this optimization process can improve the functional activity of a two-component plant nitrate transporter assayed in oocytes. RESULTS: We used the generally available software (http://www.kazusa.or.jp/codon/; http://genomes.urv.es/OPTIMIZER/) to predict a DNA sequence for the plant gene that is better suited for Xenopus laevis. Rice OsNAR2.1 and OsNRT2.3a DNA optimized sequences were commercially synthesized for Xenopus expression. The template DNA was used to synthesize cRNA using a commercially available kit. Oocytes were injected with cRNA mixture of optimized and original OsNAR2.1 and OsNRT2.3a. Oocytes injected with cRNA obtained from using the optimized DNA template could accumulate significantly more NO(3)(-) than the original genes after 16 h incubation in 0.5 mM Na(15)NO(3). Two-electrode voltage clamp analysis of the oocytes confirmed that the codon optimized template resulted in significantly larger currents when compared with the original rice cDNA. CONCLUSION: The functional activity of a rice high affinity nitrate transporter in oocytes was improved by DNA codon optimization of the genes. This methodology offers the prospect for improved expression and better subsequent functional characterization of plant proteins in the Xenopus oocyte system

Iron and nickel doped CoSe2 as efficient non precious metal catalysts for oxygen reduction

Iron and nickel doped CoSe2 were prepared by solvothermal method, and they were proved to be ternary chalcogenides by series of physical characterization. The effects of the iron and nickel contents on the oxygen reduction reaction were investigated by electrochemical measurements, and the highest activities were obtained on Co0.7Fe0.3Se2 and Co0.7Ni0.3Se2, respectively. Both Co0.7Fe0.3Se2 and Co0.7Ni0.3Se2 presented four-electron pathway. Furthermore, Co0.7Fe0.3Se2 exhibited more positive cathodic peak potential (0.564 V) and onset potential (0.759 V) than these of Co0.7Ni0.3Se2 (0.558 V and 0.741 V). And Co0.7Fe0.3Se2 displayed even superior stability and better tolerance to methanol, ethanol and ethylene glycol crossover effects than the commercial Pt/C (20 wt% Pt)

Nitroglycerine-induced nitrate tolerance compromises propofol protection of the endothelial cells against TNF-α: the role of PKC-β2 and NADPH oxidase

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Case Report Primary thyroid spindle cell tumors: spindle cell variant of papillary thyroid carcinoma?

Abstract: Primary thyroid spindle cell tumors or spindle cell component in the thyroid tumors are very rare. The spindle tumor cells were positive for thyroid papillary carcinoma markers. So these tumors were diagnosed as spindle cell variant of papillary thyroid carcinoma (PTC). To further delineate clinico-pathological features of primary thyroid spindle cell tumors and discuss differential diagnosis, we reported a 67-year-old man with a mass in the right thyroid without clinical symptom. Microscopy revealed that an encapsulated tumor with lot criss spindle cells arranged in bundles. Nuclear grooves were easy to see and rare displayed pseudoinclusions. Immunohistochemical studied showed that the spindle cells were all strong positive for TTF-1, Pax-8, thyroglobulin. Rare follicular were seen in the periphery of the tumor near the thyroid tissue. The cells formed follicular but the spindle tumor cells were positive for pan-keratins. The pathological diagnosis was primary thyroid spindle cell tumors, suspected spindle cell variant of PTC. Primary thyroid spindle cell tumors were presence and without the unified name. The further reports and more discussion were need about these tumors

SC2Net: a novel segmentation-based classification network for detection of COVID-19 in chest X-ray images.

The pandemic of COVID-19 has become a global crisis in public health, which has led to a massive number of deaths and severe economic degradation. To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial. As the popularly used real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test can be lengthy and inaccurate, chest screening with radiography imaging is still preferred. However, due to limited image data and the difficulty of the early-stage diagnosis, existing models suffer from ineffective feature extraction and poor network convergence and optimisation. To tackle these issues, a segmentation-based COVID-19 classification network, namely SC2Net, is proposed for effective detection of the COVID-19 from chest x-ray (CXR) images. The SC2Net consists of two subnets: a COVID-19 lung segmentation network (CLSeg), and a spatial attention network (SANet). In order to supress the interference from the background, the CLSeg is first applied to segment the lung region from the CXR. The segmented lung region is then fed to the SANet for classification and diagnosis of the COVID-19. As a shallow yet effective classifier, SANet takes the ResNet-18 as the feature extractor and enhances highlevel feature via the proposed spatial attention module. For performance evaluation, the COVIDGR 1.0 dataset is used, which is a high-quality dataset with various severity levels of the COVID-19. Experimental results have shown that, our SC2Net has an average accuracy of 84.23% and an average F1 score of 81.31% in detection of COVID-19, outperforming several state-of-the-art approaches