R3^3-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos

Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called rotatable region-based residual network (R$^3$-Net), to detect multi-oriented vehicles in aerial images and videos. More specially, R$^3$-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor (BAR anchor) strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In R-DN, a novel rotatable position sensitive pooling (R-PS pooling) is designed to keep the position and orientation information simultaneously while downsampling the feature maps of R-RoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image datasets, namely DLR 3K Munich Dataset and VEDAI Dataset, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://youtu.be/xCYD-tYudN0

Topology searching algorithm for multi-port hybrid circuit breakers based on graph theory

Due to the significant advantages of low cost, integrated multi-port hybrid DC circuit breakers (M-HCBs) with a reduced number of devices have attracted extensive attention for fault blocking in multi-terminal VSC-HVDC system. However, the current topology exploration by researchers is random, uncertain, and time-consuming. In order to provide more new cost-effective topologies, this paper innovatively proposes a topology searching algorithm for the IGBT-type M-HCB and uses the concept of ‘roadblock’ to simplify the adjacency matrix. It can be used to significantly save time spent on proposing a new M-HCB topology because viable topologies can be quickly and rigorously carried out from a large number of directed graphs using computers. Performance characteristics of all derived topologies are simultaneously obtained, and a comparison can be easily conducted to meet the needs of actual application scenarios. A three-port M-HCB example-specific application is given, along with some detailed output topologies that prove the validity and feasibility of the proposed method

A low-cost current flow controlling interline hybrid circuit breaker combined with SCR and H-bridge sub-module

A massive number of DC circuit breaker is usually necessary to be installed to protect HVDC grids from DC faults, this will lead to high capital costs because large number of expensive IGBT-in-series are used. In this paper, an interline hybrid circuit breaker is proposed by the combination of SCR string and a small number of H-bridge modules (SCR-IHCB). The proposed SCR-IHCB has the capacity of blocking DC fault of two adjacent lines respectively by sharing only one main breaker branch (MB) mainly composed of low-cost SCR string and H-bridge module instead of IGBT-in-series string. The interline current flow control function is also integrated. Hence it has advantages of simple and compact topology, economical design compared with typical IGBT based HCB solutions. The operation process of the proposed SCR-IHCB is discussed in detail, and the performance is verified by MATLAB Simulink simulation and scale-down prototype experiment

MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, RGBW Joint Remosaic and Denoise, one of the five tracks, working on the interpolation of RGBW CFA to Bayer at full resolution, is introduced. The participants were provided with a new dataset including 70 (training) and 15 (validation) scenes of high-quality RGBW and Bayer pairs. In addition, for each scene, RGBW of different noise levels was provided at 0dB, 24dB, and 42dB. All the data were captured using an RGBW sensor in both outdoor and indoor conditions. The final results are evaluated using objective metrics including PSNR, SSIM, LPIPS, and KLD. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022.Comment: ECCV 2022 Mobile Intelligent Photography and Imaging (MIPI) Workshop--RGBW Sensor Re-mosaic Challenge Report. MIPI workshop website: http://mipi-challenge.org/. arXiv admin note: substantial text overlap with arXiv:2209.07060, arXiv:2209.07530, arXiv:2209.0705

A modular network model of aging

Many fundamental questions on aging are still unanswered or are under intense debate. These questions are frequently not addressable by examining a single gene or a single pathway, but can best be addressed at the systems level. Here we examined the modular structure of the protein–protein interaction (PPI) networks during fruitfly and human brain aging. In both networks, there are two modules associated with the cellular proliferation to differentiation temporal switch that display opposite aging-related changes in expression. During fly aging, another couple of modules are associated with the oxidative–reductive metabolic temporal switch. These network modules and their relationships demonstrate (1) that aging is largely associated with a small number, instead of many network modules, (2) that some modular changes might be reversible and (3) that genes connecting different modules through PPIs are more likely to affect aging/longevity, a conclusion that is experimentally validated by Caenorhabditis elegans lifespan analysis. Network simulations further suggest that aging might preferentially attack key regulatory nodes that are important for the network stability, implicating a potential molecular basis for the stochastic nature of aging

MIPI 2023 Challenge on RGBW Remosaic: Methods and Results

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for an in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). With the success of the 1st MIPI Workshop@ECCV 2022, we introduce the second MIPI challenge, including four tracks focusing on novel image sensors and imaging algorithms. This paper summarizes and reviews the RGBW Joint Remosaic and Denoise track on MIPI 2023. In total, 81 participants were successfully registered, and 4 teams submitted results in the final testing phase. The final results are evaluated using objective metrics, including PSNR, SSIM, LPIPS, and KLD. A detailed description of the top three models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2023/.Comment: CVPR 2023 Mobile Intelligent Photography and Imaging (MIPI) Workshop--RGBW Sensor Remosaic Challenge Report. Website: https://mipi-challenge.org/MIPI2023/. arXiv admin note: substantial text overlap with arXiv:2209.08471, arXiv:2209.07060, arXiv:2209.07530, arXiv:2304.1008

Adipose-derived Stem Cell Conditioned Media Extends Survival time of a mouse model of Amyotrophic Lateral Sclerosis

Adipose stromal cells (ASC) secrete various trophic factors that assist in the protection of neurons in a variety of neuronal death models. In this study, we tested the effects of human ASC conditional medium (ASC-CM) in human amyotrophic lateral sclerosis (ALS) transgenic mouse model expressing mutant superoxide dismutase (SOD1(G93A)). Treating symptomatic SOD1(G93A) mice with ASC-CM significantly increased post-onset survival time and lifespan. Moreover, SOD1(G93A) mice given ASC-CM treatment showed high motor neuron counts, less activation of microglia and astrocytes at an early symptomatic stage in the spinal cords under immunohistochemical analysis. SOD1(G93A) mice treated with ASC-CM for 7 days showed reduced levels of phosphorylated p38 (pp38) in the spinal cord, a mitogen-activated protein kinase that is involved in both inflammation and neuronal death. Additionally, the levels of α-II spectrin in spinal cords were also inhibited in SOD1(G93A) mice treated with ASC-CM for 3 days. Interestingly, nerve growth factor (NGF), a neurotrophic factor found in ASC-CM, played a significant role in the protection of neurodegeneration inSOD1(G93A) mouse. These results indicate that ASC-CM has the potential to develop into a novel and effective therapeutic treatment for ALS

Regenerated woody plants influence soil microbial communities in a subtropical forest

10 páginas.- 4 figuras.- 3 tablas.- referencias.- upplementary data to this article can be found online at https://doi. org/10.1016/j.apsoil.2023.104890Forests are critical for supporting multiple ecosystem services such as climate change mitigation. Microbial diversity in soil provides important functions to maintain and regenerate forest ecosystems, and yet a critical knowledge gap remains in identifying the linkage between attributes of regenerated woody plant (RWP) communities and the diversity patterns of soil microbial communities in subtropical plantations. Here, we investigated the changes in soil microbial communities and plant traits in a nine hectare Chinese fir (Cunninghamia lanceolata; CF) plantation to assess how non-planted RWP communities regulate soil bacterial and fungal diversity, and further explore the potential mechanisms that structure their interaction. Our study revealed that soil bacterial richness was positively associated with RWP richness, whereas soil fungal richness was negatively associated with RWP basal area. Meanwhile, RWP richness was positively correlated with ectomycorrhizal (ECM) fungal richness but negatively correlated with the richness of both pathogenic and saprotrophic fungi, suggesting that the RWP-fungal richness relationship was trophic guild-specific. Soil microbial community beta diversity (i.e., dissimilarity in community composition) was strongly coupled with both RWP beta diversity and the heterogeneity of RWP basal area. Our study highlights the importance of community-level RWP plant attributes for the regulation of microbial biodiversity in plantation systems, which should be considered in forest management programs in the future.This work was funded by the National Key Research and Development Program of China (2021YFD2201301 and 2022YFF1303003), the National Natural Science Foundation of China (U22A20612), and the Key Project of Jiangxi Province Natural Science Foundation of China (20224ACB205003).Peer reviewe

Identification of the Proliferation/Differentiation Switch in the Cellular Network of Multicellular Organisms

The protein–protein interaction networks, or interactome networks, have been shown to have dynamic modular structures, yet the functional connections between and among the modules are less well understood. Here, using a new pipeline to integrate the interactome and the transcriptome, we identified a pair of transcriptionally anticorrelated modules, each consisting of hundreds of genes in multicellular interactome networks across different individuals and populations. The two modules are associated with cellular proliferation and differentiation, respectively. The proliferation module is conserved among eukaryotic organisms, whereas the differentiation module is specific to multicellular organisms. Upon differentiation of various tissues and cell lines from different organisms, the expression of the proliferation module is more uniformly suppressed, while the differentiation module is upregulated in a tissue- and species-specific manner. Our results indicate that even at the tissue and organism levels, proliferation and differentiation modules may correspond to two alternative states of the molecular network and may reflect a universal symbiotic relationship in a multicellular organism. Our analyses further predict that the proteins mediating the interactions between these modules may serve as modulators at the proliferation/differentiation switch

State Identification of ECS Turbine Bearing Based on Fractal Dimension

According to nonlinear characteristics of vibration signals measured on the turbine used in the aircraft environment control system (ECS), the ensemble empirical mode decomposition (EEMD) together with fractal dimension analysis is investigated in the paper to extract characteristic quantities for the goal of fault diagnosis of turbine bearings. Firstly, in order to filter noise signal vibration and advance signal-to-noise signals under different statements of bearings, including normal bearing, inner ring fault, outer ring fault, and cage fault, are decomposed by EEMD. Then correlation dimension of those signals phase is calculated, contrasted, and analyzed after space reconstruction. The experimental result shows that the correlation dimension, as nonlinear geometric invariants, can be used as the characteristic quantity of ECS turbine bearing on running state. Moreover, this method can accurately and effectively identify the running state of the bearing