A complete catalogue of broad-line AGNs and double-peaked emission lines from MaNGA integral-field spectroscopy of 10K galaxies: stellar population of AGNs, supermassive black holes, and dual AGNs

We analyse the integral-field spectroscopy data for the $\approx10,000$ galaxies in final data release of the MaNGA survey. We identify 188 galaxies for which the emission lines cannot be described by single Gaussian components. These galaxies can be classified into (1) 38 galaxies with broad $H\alpha$ and [OIII] $\lambda$5007 lines, (2) 101 galaxies with broad $H\alpha$ lines but no broad [OIII] $\lambda$5007 lines, and (3) 49 galaxies with double-peaked narrow emission lines. Most of the broad line galaxies are classified as Active Galactic Nuclei (AGN) from their line ratios. The catalogue helps us further understand the AGN-galaxy coevolution through the stellar population of broad-line region host galaxies and the relation between broad lines' properties and the host galaxies' dynamical properties. The stellar population properties (including mass, age and metallicity) of broad-line host galaxies suggest there is no significant difference between narrow-line Seyfert-2 galaxies and Type-1 AGN with broad $H\alpha$ lines. We use the broad-$H\alpha$ line width and luminosity to estimate masses of black hole in these galaxies, and test the $M_{BH}-\sigma_{e}$ relation in Type-1 AGN host galaxies. Furthermore we find three dual AGN candidates supported by radio images from the VLA FIRST survey. This sample may be useful for further studies on AGN activities and feedback processes.Comment: 21 pages, 17 figures, LaTeX. Accepted by MNRA

A Data Middleware for Obtaining Trusted Price Data for Blockchain

As a trusted middleware connecting the blockchain and the real world, the blockchain oracle can obtain trusted real-time price information for financial applications such as payment and settlement, and asset valuation on the blockchain. However, the current oracle schemes face the dilemma of security and service quality in the process of node selection, and the implicit interest relationship in financial applications leads to a significant conflict of interest between the task publisher and the executor, which reduces the participation enthusiasm of both parties and system security. Therefore, this paper proposes an anonymous node selection scheme that anonymously selects nodes with high reputations to participate in tasks to ensure the security and service quality of nodes. Then, this paper also details the interest requirements and behavioral motives of all parties in the payment settlement and asset valuation scenarios. Under the assumption of rational participants, an incentive mechanism based on the Stackelberg game is proposed. It can achieve equilibrium under the pursuit of the interests of task publishers and executors, thereby ensuring the interests of all types of users and improving the enthusiasm of participation. Finally, we verify the security of the proposed scheme through security analysis. The experimental results show that the proposed scheme can reduce the variance of obtaining price data by about 55\% while ensuring security, and meeting the interests of all parties.Comment: 12 pages,8 figure

FedDCT: A Dynamic Cross-Tier Federated Learning Scheme in Wireless Communication Networks

With the rapid proliferation of Internet of Things (IoT) devices and the growing concern for data privacy among the public, Federated Learning (FL) has gained significant attention as a privacy-preserving machine learning paradigm. FL enables the training of a global model among clients without exposing local data. However, when a federated learning system runs on wireless communication networks, limited wireless resources, heterogeneity of clients, and network transmission failures affect its performance and accuracy. In this study, we propose a novel dynamic cross-tier FL scheme, named FedDCT to increase training accuracy and performance in wireless communication networks. We utilize a tiering algorithm that dynamically divides clients into different tiers according to specific indicators and assigns specific timeout thresholds to each tier to reduce the training time required. To improve the accuracy of the model without increasing the training time, we introduce a cross-tier client selection algorithm that can effectively select the tiers and participants. Simulation experiments show that our scheme can make the model converge faster and achieve a higher accuracy in wireless communication networks

Robo3D: Towards Robust and Reliable 3D Perception against Corruptions

The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets often contain data that are meticulously cleaned. Such configurations, however, cannot reflect the reliability of perception models during the deployment stage. In this work, we present Robo3D, the first comprehensive benchmark heading toward probing the robustness of 3D detectors and segmentors under out-of-distribution scenarios against natural corruptions that occur in real-world environments. Specifically, we consider eight corruption types stemming from adversarial weather conditions, external disturbances, and internal sensor failure. We uncover that, although promising results have been progressively achieved on standard benchmarks, state-of-the-art 3D perception models are at risk of being vulnerable to corruptions. We draw key observations on the use of data representations, augmentation schemes, and training strategies, that could severely affect the model's performance. To pursue better robustness, we propose a density-insensitive training framework along with a simple flexible voxelization strategy to enhance the model resiliency. We hope our benchmark and approach could inspire future research in designing more robust and reliable 3D perception models. Our robustness benchmark suite is publicly available.Comment: 33 pages, 26 figures, 26 tables; code at https://github.com/ldkong1205/Robo3D project page at https://ldkong.com/Robo3

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

Detection and differentiation of Borrelia burgdorferi sensu lato in ticks collected from sheep and cattle in China

<p>Abstract</p> <p>Background</p> <p>Lyme disease caused by <it>Borrelia burgdorferi </it>sensu lato complex is an important endemic zoonosis whose distribution is closely related to the main ixodid tick vectors. In China, isolated cases of Lyme disease infection of humans have been reported in 29 provinces. Ticks, especially ixodid ticks are abundant and a wide arrange of <it>Borrelia </it>natural reservoirs are present. In this study, we developed a reverse line blot (RLB) to identify <it>Borrelia </it>spp. in ticks collected from sheep and cattle in 7 Provinces covering the main extensive livestock regions in China.</p> <p>Results</p> <p>Four species-specific RLB oligonucleotide probes were deduced from the spacer region between the 5S-23S rRNA gene, along with an oligonucleotide probe which was common to all. The species specific probes were shown to discriminate between four genomic groups of <it>B. burgdorferi </it>sensu lato i.e. <it>B. burgdorferi </it>sensu stricto, <it>B. garinii, B. afzelii</it>, and <it>B. valaisiana</it>, and to bind only to their respective target sequences, with no cross reaction to non target DNA. Furthermore, the RLB could detect between 0.1 pg and 1 pg of <it>Borrelia </it>DNA.</p> <p>A total of 723 tick samples (<it>Haemaphysalis, Boophilus, Rhipicephalus </it>and <it>Dermacentor</it>) from sheep and cattle were examined with RLB, and a subset of 667 corresponding samples were examined with PCR as a comparison. The overall infection rate detected with RLB was higher than that of the PCR test.</p> <p>The infection rate of <it>B. burgdoreri </it>sensu stricto was 40% in south areas; while the <it>B. garinii infection rate </it>was 40% in north areas. The highest detection rates of <it>B. afzelii </it>and <it>B. valaisiana </it>were 28% and 22%, respectively. Mixed infections were also found in 7% of the ticks analyzed, mainly in the North. The proportion of <it>B. garinii </it>genotype in ticks was overall highest at 34% in the whole investigation area.</p> <p>Conclusion</p> <p>In this study, the RLB assay was used to detect <it>B. burgdorferi </it>sensu lato in ticks collected from sheep and cattle in China. The results showed that <it>B. burdorferi senso stricto </it>and <it>B. afzelii </it>were mainly distributed in the South; while <it>B. garinii </it>and <it>B. valaisiana </it>were dominant in the North. <it>Borrelia </it>spirochaetes were detected in <it>Rhipicephalus </it>spp for the first time. It is suggested that the <it>Rhipicephalus </it>spps might play a role in transmitting <it>Borrelia </it>spirochaetes.</p