Deep Convolution and Correlated Manifold Embedded Distribution Alignment for Forest Fire Smoke Prediction

This paper proposes the deep convolution and correlated manifold embedded distribution alignment (DC-CMEDA) model, which is able to realize the transfer learning classification between and among various small datasets, and greatly shorten the training time. First, pre-trained Resnet50 network is used for feature transfer to extract smoke features because of the difficulty in training small dataset of forest fire smoke; second, a correlated manifold embedded distribution alignment (CMEDA) is proposed to register the smoke features in order to align the input feature distributions of the source and target domains; and finally, a trainable network model is constructed. This model is evaluated in the paper based on satellite remote sensing image and video image datasets. Compared with the deep convolutional integrated long short-term memory (DC-ILSTM) network, DC-CMEDA has increased the accuracy of video images by 1.50 %, and the accuracy of satellite remote sensing images by 4.00 %. Compared the CMEDA algorithm with the ILSTM algorithm, the number of iterations of the former has decreased to 10 times or less, and the algorithm complexity of CMEDA is lower than that of ILSTM. DC-CMEDA has a great advantage in terms of convergence speed. The experimental results show that DC-CMEDA can solve the problem of small sample smoke dataset detection and recognition

Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment

Wireless sensors are limited by node costs, communication efficiency, and energy consumption when wireless sensors are deployed on a large scale. The use of submodular optimization can reduce the deployment cost. This paper proposes a sensor deployment method based on the Improved Heuristic Ant Colony Algorithm-Chaos Optimization of Padded Sensor Placements at Informative and cost-Effective Locations (IHACA-COpSPIEL) algorithm and a routing protocol based on an improved Biogeography-Based Optimization (BBO) algorithm. First, a mathematical model with submodularity is established. Second, the IHACA is combined with pSPIEL-based on chaos optimization to determine the shortest path. Finally, the selected sensors are used in the biogeography of the improved BBO routing protocols to transmit data. The experimental results show that the IHACA-COpSPIEL algorithm can go beyond the local optimal solutions, and the communication cost of IHACA-COpSPIEL is 38.42%, 24.19% and 8.31%, respectively, lower than that of the greedy algorithm, the pSPIEL algorithm and the IHACA algorithm. It uses fewer sensors and has a longer life cycle. Compared with the LEACH protocol, the routing protocol based on the improved BBO extends the life cycle by 30.74% and has lower energy consumption

Advantages of direct input-to-output connections in neural networks : the Elman network for stock index forecasting

The Elman neural network (ElmanNN) is well-known for its capability of processing dynamic information, which has led to successful applications in stock forecasting. In this paper, we introduce direct input-to-output connections (DIOCs) into the ElmanNN and show that the proposed Elman neural network with DIOCs (Elman-DIOCs) significantly out-performs the original ElmanNN without such DIOCs. Four different global stock indices, i.e., the Shanghai Stock Exchange (SSE) Composite Index, the Korea Stock Price Index (KOSPI), the Nikkei 225 Index (Nikkei225), and the Standard & Poor's 500 Index (SPX), are used to demonstrate the affecacy of the Elman-DIOCs in time-series prediction. We systematically evaluate 8 models, depending whether or not there are hidden layer biases, whether or not there are output layer biases, and whether or not there are DIOCs. The experimental results show that DIOCs lead to much better prediction accuracy, while requiring fewer than a half of the hidden neurons. Take the SPX index, for example - the root mean squared error (RMSE) and the mean absolute error (MAE) of the Elman-DIOCs are improved by 44.2% and 41.1%, respectively, compared to the ElmanNN, and 65.6% and 60.8%, respectively, compared to the multi-layer perceptron (MLP). We argue that (1) DIOCs can always help to improve accuracy, while reducing network complexity and computational burden, as long as the problem at hand (either regression or classification) has linear components, and (2) most real-world applications contain linear components. Therefore DIOCs will be almost always beneficial in any types of neural networks for classification or regression. We also point out that in rare cases where the problem at hand is entirely nonlinear, DIOCs should not be used.This study is funded by Joint Research Fund for Overseas Chinese Scholars and Scholars in Hong Kong and Macao (Grant No. 61828601), Natural Science Foundation of Shanxi Province (Grant No. 201801D121141), and Provincial Program on Key Research Projects of Shanxi (Social Development Area) (Grant No. 201903D321003)

Optimization of submodularity and BBO-based routing protocol for wireless sensor deployment

Wireless sensors are limited by node costs, communication efficiency, and energy consumption when wireless sensors are deployed on a large scale. The use of submodular optimization can reduce the deployment cost. This paper proposes a sensor deployment method based on the Improved Heuristic Ant Colony Algorithm-Chaos Optimization of Padded Sensor Placements at Informative and cost-Effective Locations (IHACA-COpSPIEL) algorithm and a routing protocol based on an improved Biogeography-Based Optimization (BBO) algorithm. First, a mathematical model with submodularity is established. Second, the IHACA is combined with pSPIEL-based on chaos optimization to determine the shortest path. Finally, the selected sensors are used in the biogeography of the improved BBO routing protocols to transmit data. The experimental results show that the IHACA-COpSPIEL algorithm can go beyond the local optimal solutions, and the communication cost of IHACA-COpSPIEL is 38.42%, 24.19% and 8.31%, respectively, lower than that of the greedy algorithm, the pSPIEL algorithm and the IHACA algorithm. It uses fewer sensors and has a longer life cycle. Compared with the LEACH protocol, the routing protocol based on the improved BBO extends the life cycle by 30.74% and has lower energy consumption.Published versio

RCSANet: A Full Convolutional Network for Extracting Inland Aquaculture Ponds from High-Spatial-Resolution Images

Numerous aquaculture ponds are intensively distributed around inland natural lakes and mixed with cropland, especially in areas with high population density in Asia. Information about the distribution of aquaculture ponds is essential for monitoring the impact of human activities on inland lakes. Accurate and efficient mapping of inland aquaculture ponds using high-spatial-resolution remote-sensing images is a challenging task because aquaculture ponds are mingled with other land cover types. Considering that aquaculture ponds have intertwining regular embankments and that these salient features are prominent at different scales, a Row-wise and Column-wise Self-Attention (RCSA) mechanism that adaptively exploits the identical directional dependency among pixels is proposed. Then a fully convolutional network (FCN) combined with the RCSA mechanism (RCSANet) is proposed for large-scale extraction of aquaculture ponds from high-spatial-resolution remote-sensing imagery. In addition, a fusion strategy is implemented using a water index and the RCSANet prediction to further improve extraction quality. Experiments on high-spatial-resolution images using pansharpened multispectral and 2 m panchromatic images show that the proposed methods gain at least 2–4% overall accuracy over other state-of-the-art methods regardless of regions and achieve an overall accuracy of 85% at Lake Hong region and 83% at Lake Liangzi region in aquaculture pond extraction

Neisseria gonorrhoeae infects the heterogeneous epithelia of the human cervix using distinct mechanisms.

Sexually transmitted infections are a critical public health issue. However, the mechanisms underlying sexually transmitted infections in women and the link between the infection mechanism and the wide range of clinical outcomes remain elusive due to a lack of research models mimicking human infection in vivo. We established a human cervical tissue explant model to mimic local Neisseria gonorrhoeae (GC) infections. We found that GC preferentially colonize the ectocervix by activating integrin-β1, which inhibits epithelial shedding. GC selectively penetrate into the squamocolumnar junction (TZ) and endocervical epithelia by inducing β-catenin phosphorylation, which leads to E-cadherin junction disassembly. Epithelial cells in various cervical regions differentially express carcinoembryonic antigen-related cell adhesion molecules (CEACAMs), the host receptor for GC opacity-associated proteins (OpaCEA). Relatively high levels were detected on the luminal membrane of ecto/endocervical epithelial cells but very low levels intracellularly in TZ epithelial cells. CEACAM-OpaCEA interaction increased ecto/endocervical colonization and reduced endocervical penetration by increasing integrin-β1 activation and inhibiting β-catenin phosphorylation respectively, through CEACAM downstream signaling. Thus, the intrinsic properties of cervical epithelial cells and phase-variation of bacterial surface molecules both play a role in controlling GC infection mechanisms and infectivity, preferential colonization or penetration, potentially leading to asymptomatic or symptomatic infection