Vehicle Detection of Multi-source Remote Sensing Data Using Active Fine-tuning Network

Vehicle detection in remote sensing images has attracted increasing interest in recent years. However, its detection ability is limited due to lack of well-annotated samples, especially in densely crowded scenes. Furthermore, since a list of remotely sensed data sources is available, efficient exploitation of useful information from multi-source data for better vehicle detection is challenging. To solve the above issues, a multi-source active fine-tuning vehicle detection (Ms-AFt) framework is proposed, which integrates transfer learning, segmentation, and active classification into a unified framework for auto-labeling and detection. The proposed Ms-AFt employs a fine-tuning network to firstly generate a vehicle training set from an unlabeled dataset. To cope with the diversity of vehicle categories, a multi-source based segmentation branch is then designed to construct additional candidate object sets. The separation of high quality vehicles is realized by a designed attentive classifications network. Finally, all three branches are combined to achieve vehicle detection. Extensive experimental results conducted on two open ISPRS benchmark datasets, namely the Vaihingen village and Potsdam city datasets, demonstrate the superiority and effectiveness of the proposed Ms-AFt for vehicle detection. In addition, the generalization ability of Ms-AFt in dense remote sensing scenes is further verified on stereo aerial imagery of a large camping site

More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification

Classification and identification of the materials lying over or beneath the Earth's surface have long been a fundamental but challenging research topic in geoscience and remote sensing (RS) and have garnered a growing concern owing to the recent advancements of deep learning techniques. Although deep networks have been successfully applied in single-modality-dominated classification tasks, yet their performance inevitably meets the bottleneck in complex scenes that need to be finely classified, due to the limitation of information diversity. In this work, we provide a baseline solution to the aforementioned difficulty by developing a general multimodal deep learning (MDL) framework. In particular, we also investigate a special case of multi-modality learning (MML) -- cross-modality learning (CML) that exists widely in RS image classification applications. By focusing on "what", "where", and "how" to fuse, we show different fusion strategies as well as how to train deep networks and build the network architecture. Specifically, five fusion architectures are introduced and developed, further being unified in our MDL framework. More significantly, our framework is not only limited to pixel-wise classification tasks but also applicable to spatial information modeling with convolutional neural networks (CNNs). To validate the effectiveness and superiority of the MDL framework, extensive experiments related to the settings of MML and CML are conducted on two different multimodal RS datasets. Furthermore, the codes and datasets will be available at https://github.com/danfenghong/IEEE_TGRS_MDL-RS, contributing to the RS community

Epidemiology and clinical course of COVID-19 in Shanghai, China.

Background: Novel coronavirus pneumonia (COVID-19) is prevalent around the world. We aimed to describe epidemiological features and clinical course in Shanghai. Methods: We retrospectively analysed 325 cases admitted at Shanghai Public Health Clinical Center, between January 20 and February 29, 2020. Results: 47.4% (154/325) had visited Wuhan within 2 weeks of illness onset. 57.2% occurred in 67 clusters; 40% were situated within 53 family clusters. 83.7% developed fever during the disease course. Median times from onset to first medical care, hospitalization and negative detection of nucleic acid by nasopharyngeal swab were 1, 4 and 8 days. Patients with mild disease using glucocorticoid tended to have longer viral shedding in blood and feces. At admission, 69.8% presented with lymphopenia and 38.8% had elevated D-dimers. Pneumonia was identified in 97.5% (314/322) of cases by chest CT scan. Severe-critical patients were 8% with a median time from onset to critical disease of 10.5 days. Half required oxygen therapy and 7.1% high-flow nasal oxygen. The case fatality rate was 0.92% with median time from onset to death of 16 days. Conclusion: COVID-19 cases in Shanghai were imported. Rapid identification, and effective control measures helped to contain the outbreak and prevent community transmission

Analytical Determination of Static Deflection Shape of an Asymmetric Extradosed Cable-Stayed Bridge Using Ritz Method

A practical method to analyze the mechanical behavior of the asymmetric extradosed cable-stayed (AECS) bridge is provided in this paper. The work includes the analysis of the equivalent membrane tension of the cables, the ratio of side-span cable force to middle-span cable force, and the deflection of the main girder subject to uniformly distributed load. The Ritz method is a simple and efficient way to solve composite structures, such as the AECS bridge, compared with the traditional force method, displacement method, or finite element method. The theoretical results obtained from the Ritz method are in good agreement with that from the finite element analysis, which shows the accuracy of this approach. Then, a parametric study of AECS bridges is carried out by using the proposed equations directly, instead of using the traditional finite element modeling process, which requires a lot of modeling work. As a result, reasonable values of very important parameters are suggested, which helps the readers reach a better understanding of the mechanical behavior of AECS bridges. More importantly, it helps the designers to enhance the efficiency in the stage of conceptual design

Non-destructive monitoring of netted muskmelon quality based on its external phenotype using Random Forest.

The internal phenotypes of netted muskmelon (Cucumis melo L. var. eticulates Naud.) are always associated with its external phenotypes. In this study, the parameters of external phenotypic traits were extracted from muskmelon images captured by machine vision, and the internal phenotypes of interest to us were measured. Pearson analysis showed that most external phenotypic traits were highly correlated with these internal phenotypes in muskmelon fruit. In this study, we used the random forest algorithm to predict muskmelon fruit internal phenotypes based on the significantly associated external parameters. Carotenoids, sucrose, and total soluble solid (TSS) were the three most accurately monitored internal phenotypes with prediction R-squared (R2) values of 0.947 (root-mean-square error (RMSE) = 0.019 mg/100 g), 0.918 (RMSE = 3.233 mg/g), and 0.916 (RMSE = 1.089%), respectively. Further, a simplified model was constructed and validated based on the top 10 external phenotypic parameters associated with each internal phenotype, and these parameters were filtered with the varImp function from the random forest package. The top 10 external phenotypic parameters correlated with each internal phenotype used in the simplified model were not identical. The results showed that the simplified models also accurately monitored the melon internal phenotypes, despite that the predicted R2 values decreased 0.3% to 7.9% compared with the original models. This study improved the efficiency and accuracy of real-time fruit quality monitoring for greenhouse muskmelon

Hydrocarbon migration pathways in the Neogene of Laixi structural belt, southern Bohai Sea, China

Abstract: Characteristics, distribution, temporal and spatial configuration, and effectiveness of hydrocarbon migration pathways have been investigated through geochemical and formation pressure analysis etc, to further reveal hydrocarbon accumulation patterns in the Neogene of the Laixi structural belt, southern Bohai Sea of China. Firstly, three factors, faults, carrier rocks (beds) and mud caprocks of the transporting framework in the Neogene were examined to find out its feature and space configuration and made static state evaluation. It is found that besides the Guantao Formation – lower Minghuazhen Formation No. V oil layers, the No. I – III oil layers in the lower Minghuazhen Formation are also the dominant oil and gas carrier beds. Cutted by the adjustment faults and sealed by mudstone capcocks, the two carrier layers are combined into a “layered cake” type hydrocarbon migration framework. Then the hydrocarbon passage pathways were tracked dynamically and described using nitrogen compounds concentration, formation pressure, fluorescence logging and sand bodies description, which shows that the “T03-T02-T01 mudstone cap layers” separate the Neogene hydrocarbon migration pathways into upper and lower units. The massive sandy conglomerate reservoirs in the Guantao Formation – lower Minghuazhen Formation No. V oil layers are the carrier bed of the lower hydrocarbon migration pathway, while the north-south trending overlapped and connected channel sandbodies in lower Minghuazhen Formation No. I – III oil layers are the carrier bed of the upper unit. Hydrocarbon migrated laterally along the two passage ways, accompanying with differential accumulation. Key words: southern Bohai Sea, Laixi structural belt, hydrocarbon passage framework, dominant migration pathway, hydrocarbon migration pattern, accumulation patter

VPPIPP and IPPVPP: two hexapeptides innovated to exert antihypertensive activity.

In this study, two hexapeptides of IPPVPP and VPPIPP were innovated by using two commercial antihypertensive peptides IPP and VPP as two domains cis-linked and trans-linked, respectively. The IPPVPP and VPPIPP were chemically synthesized and evaluated for the antihypertensive activity in vitro/vivo. The in vitro ACE-inhibitory study showed that VPPIPP (34.71 ± 4.38%) has a significantly stronger activity than that of IPPVPP (13.17 ± 0.25%) at a treatment concentration of 10 µmol/L, but it was weaker than the commercial IPP (56.97 ± 2.40%) (P<0.05). However, VPPIPP, IPPVPP, and IPP lowered the systolic blood pressure by 21 ± 0.9%, 17.4 ± 1.3% and 17.5 ± 0.9%, respectively, in rats at 1.5 mg/kg body weight dosage. The result was consistent with the mRNA level of sarcoplasmic reticulum Ca(2+), Mg(2+) -ATPase Gene (SERCA 2a) in rat hearts. Additionally, VPPIPP and IPPVPP showed no negative impact on blood glycometabolism. The results suggested that the two hexapeptides could be potent bioactive peptides in functional foods for people with high blood pressure

Microdeformation of Three-Dimensional Cultured Fibroblasts Induces Gene Expression and Morphological Changes

Background: Vacuum-assisted closure induces microdeformations of the wound surface and accelerates healing of complex wounds; however, a thorough understanding of the biology of cellular mechanotransduction is lacking. We hypothesized that fibroblast shape and function can be altered in an in vitro vacuum-assisted closure device. Methods: A 3-dimensional fibrin matrix with cultured murine fibroblasts and an intervening polyurethane foam was exposed to 125 mm Hg suction and compared with similar wells without suction. We measured fibroblast proliferation and morphology using fluorescence microscopy and gene expression change using real-time reverse-transcriptase polymerase chain reaction at 24, 48, and 72 hours. Results: Wells exposed to suction induced significant proliferation of fibroblasts and morphologic changes visible by larger, rounder, and notable dendrite-like branching and process extensions. Type 1 collagen alpha 1 (COL1A1), fibroblast growth factor 2 (FGF2, bFGF), and transforming growth factor beta 1 (TGF[beta]1) were all up-regulated after 48 hours of exposure to suction. Smooth muscle actin alpha 2 (Acta2, [alpha]-SMA) was up-regulated after 72 hours. Conclusions: Microdeformations produced by the combination of polyurethane foam and suction are associated with increased fibroblast proliferation and up-regulation of gene expressions in fibroblasts