4DRVO-Net: Deep 4D Radar-Visual Odometry Using Multi-Modal and Multi-Scale Adaptive Fusion

Four-dimensional (4D) radar--visual odometry (4DRVO) integrates complementary information from 4D radar and cameras, making it an attractive solution for achieving accurate and robust pose estimation. However, 4DRVO may exhibit significant tracking errors owing to three main factors: 1) sparsity of 4D radar point clouds; 2) inaccurate data association and insufficient feature interaction between the 4D radar and camera; and 3) disturbances caused by dynamic objects in the environment, affecting odometry estimation. In this paper, we present 4DRVO-Net, which is a method for 4D radar--visual odometry. This method leverages the feature pyramid, pose warping, and cost volume (PWC) network architecture to progressively estimate and refine poses. Specifically, we propose a multi-scale feature extraction network called Radar-PointNet++ that fully considers rich 4D radar point information, enabling fine-grained learning for sparse 4D radar point clouds. To effectively integrate the two modalities, we design an adaptive 4D radar--camera fusion module (A-RCFM) that automatically selects image features based on 4D radar point features, facilitating multi-scale cross-modal feature interaction and adaptive multi-modal feature fusion. In addition, we introduce a velocity-guided point-confidence estimation module to measure local motion patterns, reduce the influence of dynamic objects and outliers, and provide continuous updates during pose refinement. We demonstrate the excellent performance of our method and the effectiveness of each module design on both the VoD and in-house datasets. Our method outperforms all learning-based and geometry-based methods for most sequences in the VoD dataset. Furthermore, it has exhibited promising performance that closely approaches that of the 64-line LiDAR odometry results of A-LOAM without mapping optimization.Comment: 14 pages,12 figure

Cyber-Physical Codesign of Distributed Structural Health Monitoring with Wireless Sensor Networks

Our Deteriorating Civil Infrastructure Faces the Critical Challenge of Long-Term Structural Health Monitoring for Damage Detection and Localization. in Contrast to Existing Research that Often Separates the Designs of Wireless Sensor Networks and Structural Engineering Algorithms, This Paper Proposes a Cyber-Physical Co-Design Approach to Structural Health Monitoring based on Wireless Sensor Networks. Our Approach Closely Integrates (1) Flexibility-Based Damage Localization Methods that Allow a Tradeoff between the Number of Sensors and the Resolution of Damage Localization, and (2) an Energy-Efficient, Multi-Level Computing Architecture Specifically Designed to Leverage the Multi-Resolution Feature of the Flexibility-Based Approach. the Proposed Approach Has Been Implemented on the Intel Imote2 Platform. Experiments on a Physical Beam and Simulations of a Truss Structure Demonstrate the System\u27s Efficacy in Damage Localization and Energy Efficiency. © 2010 ACM

Cyber-Physical Codesign of Distributed Structural Health Monitoring with Wireless Sensor Networks

Our Deteriorating Civil Infrastructure Faces the Critical Challenge of Long-Term Structural Health Monitoring for Damage Detection and Localization. in Contrast to Existing Research that Often Separates the Designs of Wireless Sensor Networks and Structural Engineering Algorithms, This Paper Proposes a Cyber-Physical Codesign Approach to Structural Health Monitoring based on Wireless Sensor Networks. Our Approach Closely Integrates 1) Flexibility-Based Damage Localization Methods that Allow a Tradeoff between the Number of Sensors and the Resolution of Damage Localization, and 2) an Energy-Efficient, Multilevel Computing Architecture Specifically Designed to Leverage the Multiresolution Feature of the Flexibility-Based Approach. the Proposed Approach Has Been Implemented on the Intel Imote2 Platform. Experiments on a Simulated Truss Structure and a Real Full-Scale Truss Structure Demonstrate the System\u27s Efficacy in Damage Localization and Energy Efficiency

An analysis of microbiota-targeted therapies in patients with avian influenza virus subtype H7N9 infection

BACKGROUND: Selective prophylactic decontamination of the digestive tract is a strategy for the prevention of secondary nosocomial infection in patients with avian influenza virus subtype H7N9 infection. Our aim was to summarize the effectiveness of these therapies in re-establishing a stable and diverse microbial community, and reducing secondary infections. METHODS: Comprehensive therapies were dependent on the individual clinical situation of subjects, and were divided into antiviral treatment, microbiota-targeted therapies, including pro- or pre-biotics and antibiotic usage, and immunotherapy. Quantitative polymerase chain reaction and denaturing gradient gel electrophoresis (DGGE) were used for real-time monitoring of the predominant intestinal microbiome during treatment. Clinical information about secondary infection was confirmed by analyzing pathogens isolated from clinical specimens. RESULTS: Different antibiotics had similar effects on the gut microbiome, with a marked decrease and slow recovery of the Bifidobacterium population. Interestingly, most fecal microbial DGGE profiles showed the relative stability of communities under the continual suppression of the same antibiotics, and significant changes when new antibiotics were introduced. Moreover, we found no marked increase in C-reactive protein, and no cases of bacteremia or pneumonia, caused by probiotic use in the patients, which confirmed that the probiotics used in this study were safe for use in patients with H7N9 infection. Approximately 72% of those who subsequently suffered exogenous respiratory infection by Candida species or multidrug-resistant Acinetobacter baumannii and Klebsiella pneumoniae were older than 60 years. The combination of probiotics and prebiotics with antibiotics seemed to fail in these patients. CONCLUSIONS: Elderly patients infected with the influenza A (H7N9) virus are considered a high-risk group for developing secondary bacterial infection. Microbiota restoration treatment reduced the incidence of enterogenous secondary infection, but not exogenous respiratory infection. The prophylactic effects of microbiota restoration strategies for secondary infection were unsatisfactory in elderly and critically ill patients

Arbuscular mycorrhizal fungi improve selenium uptake by modulating root transcriptome of rice (Oryza sativa L.)

Although selenium (Se) is an essential trace element in humans, the intake of Se from food is still generally inadequate throughout the world. Inoculation with arbuscular mycorrhizal fungi (AMF) improves the uptake of Se in rice (Oryza sativa L.). However, the mechanism by which AMF improves the uptake of Se in rice at the transcriptome level is unknown. Only a few studies have evaluated the effects of uptake of other elements in rice under the combined effects of Se and AMF. In this study, Se combined with the AMF Funneliformis mosseae (Fm) increased the biomass and Se concentration of rice plants, altered the pattern of ionomics of the rice roots and shoots, and reduced the antagonistic uptake of Se with nickel, molybdenum, phosphorus, and copper compared with the treatment of Se alone, indicating that Fm can enhance the effect of fertilizers rich in Se. Furthermore, a weighted gene co-expression network analysis (WGCNA) showed that the hub genes in modules significantly associated with the genes that contained Se and were related to protein phosphorylation, protein serine/threonine kinase activity, membrane translocation, and metal ion binding, suggesting that the uptake of Se by the rice roots may be associated with these genes when Fm and Se act in concert. This study provides a reference for the further exploration of genes related to Se uptake in rice under Fm treatment

Apolygus lucorum genome provides insights into omnivorousness and mesophyll feeding.

peer reviewedApolygus lucorum (Miridae) is an omnivorous pest that occurs worldwide and is notorious for the serious damage it causes to various crops and substantial economic losses. Although some studies have examined the biological characteristics of the mirid bug, no reference genome is available in Miridae, limiting in-depth studies of this pest. Here, we present a chromosome-scale reference genome of A. lucorum, the first sequenced Miridae species. The assembled genome size was 1.02 Gb with a contig N50 of 785 kb. With Hi-C scaffolding, 1,016 Mb contig sequences were clustered, ordered and assembled into 17 large scaffolds with scaffold N50 length 68 Mb, each corresponding to a natural chromosome. Numerous transposable elements occur in this genome and contribute to the large genome size. Expansions of genes associated with omnivorousness and mesophyll feeding such as those related to digestion, chemosensory perception, and detoxification were observed in A. lucorum, suggesting that gene expansion contributed to its strong environmental adaptability and severe harm to crops. We clarified that a salivary enzyme polygalacturonase is unique in mirid bugs and has significantly expanded in A. lucorum, which may contribute to leaf damage from this pest. The reference genome of A. lucorum not only facilitates biological studies of Hemiptera as well as an understanding of the damage mechanism of mesophyll feeding, but also provides a basis on which to develop efficient control technologies for mirid bugs

KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs

Incorporating factual knowledge into pre-trained language models (PLM) such as BERT is an emerging trend in recent NLP studies. However, most of the existing methods combine the external knowledge integration module with a modified pre-training loss and re-implement the pre-training process on the large-scale corpus. Re-pretraining these models is usually resource-consuming, and difficult to adapt to another domain with a different knowledge graph (KG). Besides, those works either cannot embed knowledge context dynamically according to textual context or struggle with the knowledge ambiguity issue. In this paper, we propose a novel knowledge-aware language model framework based on fine-tuning process, which equips PLM with a unified knowledge-enhanced text graph that contains both text and multi-relational sub-graphs extracted from KG. We design a hierarchical relational-graph-based message passing mechanism, which can allow the representations of injected KG and text to mutually update each other and can dynamically select ambiguous mentioned entities that share the same text. Our empirical results show that our model can efficiently incorporate world knowledge from KGs into existing language models such as BERT, and achieve significant improvement on the machine reading comprehension (MRC) task compared with other knowledge-enhanced models.Comment: ICLR 2022 on DLG4NL