Hyperspectral image (HSI) consists of hundreds of continuous narrow bands with high redundancy, resulting in the curse of dimensionality and an increased computation complexity in HSI classification. Many clustering-based band selection approaches have been proposed to deal with such a problem. However, a few of them consider the spectral and spatial relationship simultaneously. In this letter, we proposed a novel clustering-based band selection approach using deep subspace clustering (DSC). The proposed approach combines the subspace clustering task into a convolutional autoencoder by treating it as a self-expressive layer, enabling it to be trained end to end. The resulting network can fully extract the interaction of spectral bands based on using spatial information and nonlinear feature transformation. We compared the results of the proposed method with existing band selection methods for three widely used HSI data sets, showing that the proposed method is able to accurately select an informative band subset with remarkable classification accuracy.

Background: West African countries experienced the highest under-5 mortality rate (U5MR), the lowest life expectancy, and the poorest economic development. This study aimed to explore the spatiotemporal trend of U5MR and spatial spillover effects of health resources and services to provide a basis for establishing health policies and international cooperative mechanisms in West Africa.Methods: We retrieved data from the World Health Organization’s Global Health Observatory, the United Nations Human Development Report, and the Global Burden of Disease Study 2019. Joinpoint regression analysis and Moran’s I method were used to examine the temporal trend and spatial dependence of U5MR, respectively. Spatial regression analysis was used to examine the spatial spillover effects.Results: The long-term downward trends in U5MR were divided into multiple segments by two or three change points in 2013, 2014, or 2015, and the annual percentage change after 2015 was higher than before 2015. Global Moran’s I was positive, significantly indicating positive spatial autocorrelation, which increased from 0.2850 (p = 0.0210) to 0.3597 (p = 0.0080). Based on spatial regression analysis, human development index (HDI), physicians density, nurses and midwives density, health center density, percentage of infants lacking immunization for diphtheria and measles, and coverage rate of at least one antenatal visit had negative spatial spillover effects on U5MR. HDI had the strongest negative correlation (β = −0.0187 to −0.1054, p < 0.0001). Current health expenditure (CHE) per capita had positive spatial spillover effects on U5MR.Conclusion: This study revealed the spatiotemporal trend of U5MR in West African countries and spatial spillover effects of health resources and services. Promoting economic development, increasing health human resources, health expenditure, vaccination rate, antenatal care coverage, and the proportion of health professionals attending births not only reduced the local U5MR but also exerted spatial spillover effects on adjacent countries. The West African Health Organization may consider regional spillover mechanisms to develop regional health policy and intervention cooperation mechanisms, which will contribute to achieving the sustainable development goal on U5MR, Africa Agenda 2063, and universal health coverage.

The recently proposed learned bloom filter (LBF) opens a new perspective on how to reconstruct bloom filters with machine learning. However, the LBF has a massive time cost and does not apply to multidimensional spatial data. In this paper, we propose a prefix‐based and adaptive learned bloom filter (PA‐LBF) for spatial data, which efficiently supports the insertion and deletion. The proposed PA‐LBF is divided into three parts: (1) the prefix‐based classification. The Z‐order space‐filling curve is used to extract data, prefix it, and classify it. (2) The adaptive learning process. The multiple independent adaptive sub‐LBFs are designed to train the suffixes of data, combined with part 1, to reduce the false positive rate (FPR), query, and learning process time consumption. (3) The backup filter uses CBF. Two kinds of backup CBF are constructed to meet the situation of different insertion and deletion frequencies. Experimental results prove the validity of the theory and show that the PA‐LBF reduces the FPR by 84.87%, 79.53%, and 43.01% with the same memory usage compared with the LBF on three real‐world spatial datasets. Moreover, the time consumption of PA‐LBF can be reduced to 5× and 2.05× that of the LBF on the query and learning process, respectively.

In wireless networks, communication links may be subject to random fatal impacts: for example, sensor networks under sudden power losses or cognitive radio networks with unpredictable primary user spectrum occupancy. Under such circumstances, it is critical to quantify how fast and reliably the information can be collected over attacked links. For a single channel subject to random attacks, named as a dying channel, we model it as a block-fading (BF) channel with a finite and random channel length. For this channel, we first study the outage capacity and the outage probability when the data frame length is fixed and uniform power allocation is assumed. Furthermore, we discuss the optimization over the frame length and/or the power allocation over the constituting data blocks to minimize the outage probability. In addition, we extend the results from the single dying channel to the parallel multi-channel case where each sub-channel is a dying channel, and investigate the asymptotic behavior of the overall outage probability as the number of sub-channels goes to infinity with two different attack models: the independent-attack case and the m-dependent-attack case. It is shown that the asymptotic outage probability diminishes to zero for both cases as the number of sub-channels increases if the rate per unit cost is less than a certain threshold. The outage exponents are also studied to reveal how fast the outage probability improves with the number of sub-channels.