Robust Adaptive LCMV Beamformer Based On An Iterative Suboptimal Solution

The main drawback of closed-form solution of linearly constrained minimum variance (CF-LCMV) beamformer is the dilemma of acquiring long observation time for stable covariance matrix estimates and short observation time to track dynamic behavior of targets, leading to poor performance including low signal-noise-ratio (SNR), low jammer-to-noise ratios (JNRs) and small number of snapshots. Additionally, CF-LCMV suffers from heavy computational burden which mainly comes from two matrix inverse operations for computing the optimal weight vector. In this paper, we derive a low-complexity Robust Adaptive LCMV beamformer based on an Iterative Suboptimal solution (RAIS-LCMV) using conjugate gradient (CG) optimization method. The merit of our proposed method is threefold. Firstly, RAIS-LCMV beamformer can reduce the complexity of CF-LCMV remarkably. Secondly, RAIS-LCMV beamformer can adjust output adaptively based on measurement and its convergence speed is comparable. Finally, RAIS-LCMV algorithm has robust performance against low SNR, JNRs, and small number of snapshots. Simulation results demonstrate the superiority of our proposed algorithms

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

Distribution discrepancy is an intrinsic challenge in existing fingerprint-based indoor positioning system(s) (FIPS) due to real-time environmental variations; thus, the positioning model needs to be reconstructed frequently based on newly collected training data. However, it is expensive or impossible to collect adequate training samples to reconstruct the fingerprint database. Fortunately, transfer learning has proven to be an effective solution to mitigate the distribution discrepancy, enabling us to update the positioning model using newly collected training data in real time. However, in practical applications, traditional transfer learning algorithms no longer act well to feature space heterogeneity caused by different types or holding postures of fingerprint collection devices (such as smartphones). Moreover, current heterogeneous transfer methods typically require enough accurately labeled samples in the target domain, which is practically expensive and even unavailable. Aiming to solve these problems, a heterogeneous transfer learning framework based on co-occurrence data (HTL-CD) is proposed for FIPS, which can realize higher positioning accuracy and robustness against environmental changes without reconstructing the fingerprint database repeatedly. Specifically, the source domain samples are mapped into the feature space in the target domain, then the marginal and conditional distributions of the source and target samples are aligned in order to minimize the distribution divergence caused by collection device heterogeneity and environmental changes. Moreover, the utilized co-occurrence fingerprint data enables us to calculate correlation coefficients between heterogeneous samples without accurately labeled target samples. Furthermore, by resorting to the adopted correlation restriction mechanism, more valuable knowledge will be transferred to the target domain if the source samples are related to the target ones, which remarkably relieves the “negative transfer" issue. Real-world experimental performance implies that, even without accurately labeled samples in the target domain, the proposed HTL-CD can obtain at least 17.15% smaller average localization errors (ALEs) than existing transfer learning-based positioning methods, which further validates the effectiveness and superiority of our algorithm

Trends in prevalence and incidence of chronic respiratory diseases from 1990 to 2017

BACKGROUND: Chronic respiratory diseases (CRDs) are leading causes of morbidity worldwide. However, the spatial and temporal trends in prevalence and incidence of CRDs have not been estimated. METHODS: Based on data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2017, we analyzed the prevalence and incidence trends of CRDs from 1990 to 2017 according to age, sex, region and disease pattern. Furthermore, the correlations between the incidence and the World Bank income levels, sociodemographic index (SDI), and human development index (HDI) levels were analyzed to assess the factors affecting incidence. RESULT: The total number of CRD cases increased by 39.5% from 1990 to 2017, nevertheless, the age-standardized prevalence rate (ASPR) and age-standardized incidence rate (ASIR) showed decreasing trends. The ASIRs of CRD, chronic obstructive pulmonary disease (COPD), pneumoconiosis, and asthma decreased, whereas the ASIR of interstitial lung disease and pulmonary sarcoidosis increased during the past 27 years. Significant differences between males and females in the incidence rates of pneumoconiosis, interstitial lung disease and pulmonary sarcoidosis were observed. Elderly people especially suffered from CRDs, except for asthma. For COPD, the ASIR decreased from low-SDI regions to high-SDI regions. The ASIR of interstitial lung disease and pulmonary sarcoidosis in the high-SDI region was highest and have increased mostly. The ASIRs for pneumoconiosis and asthma were inversely related to the HDI. CONCLUSIONS: In 2017, CRDs were still the leading causes of morbidity worldwide. A large proportion of the disease burden was attributed to asthma and COPD. The incidence rates of all four types of CRDs varied greatly across the world. Statistically significant correlation was found between the ASIR and SDI/HDI

OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning

In an indoor positioning system (IPS), transfer learning (TL) methods are commonly used to predict the location of mobile devices under the assumption that all training instances of the target domain are given in advance. However, this assumption has been criticized for its shortcomings in dealing with the problem of signal distribution variations, especially in a dynamic indoor environment. The reasons are: collecting a sufficient number of training instances is costly, the training instances may arrive online, the feature spaces of the target and source domains may be different, and negative knowledge may be transferred in the case of a redundant source domain. In this work, we proposed an online heterogeneous transfer learning (OHetTLAL) algorithm for IPS-based RSS fingerprinting to improve the positioning performance in the target domain by fusing both source and target domain knowledge. The source domain was refined based on the target domain to avoid negative knowledge transfer. The co-occurrence measure of the feature spaces (Cmip) was used to derive the homogeneous new feature spaces, and the features with higher weight values were selected for training the classifier because they could positively affect the location prediction of the target. Thus, the objective function was minimized over the new feature spaces. Extensive experiments were conducted on two real-world scenarios of datasets, and the predictive power of the different modeling techniques were evaluated for predicting the location of a mobile device. The results have revealed that the proposed algorithm outperforms the state-of-the-art methods for fingerprint-based indoor positioning and is found robust to changing environments. Moreover, the proposed algorithm is not only resilient to fluctuating environments but also mitigates the model’s overfitting problem

Two-stage study of lung cancer risk modification by a functional variant in the 3 '-untranslated region of SMAD5 based on the bone morphogenetic protein pathway

Increasing evidence supports a key role for the bone morphogenetic protein (BMP) signaling pathway in lung vasculogenesis and angiogenesis. Genetic variations in BMP genes have been found to be correlated with cancer risk. In particular, the mutation in the 3'-untranslated region of BMPs may significantly affect gene function, leading to cancer susceptibility. The aim of the present study was to determine whether genetic variations in the components of the BMP family are associated with lung cancer risk. A total of 314 tag single-nucleotide polymorphisms were identified in 18 genes, which are considered to either compose or regulate BMPs, and their association with lung cancer risk was evaluated in a two-stage case-control study with 4,680 cases and controls. A consistently significant association of SMAD5 rs12719482 with elevated lung cancer risk was observed in the three types of sources of populations (adjusted additive model in the combined population: Odds ratio=1.32, 95% confidence interval: 1.16-1.51). The lung cancer risk statistically significantly increased with the increasing number of variant alleles of SMAD5 rs12719482 in a dose-dependent pattern (P for trend=4.9×10-5). Consistent evidence was identified for a significant interaction between the rs12719482 and cigarette smoking, performed as either a continuous or discrete variable. These findings indicated that SMAD5 rs12719482 may be a possible candidate marker for susceptibility to lung cancer in the Chinese population.National Natural Science Foundation of China [81520108001, 81220108001, 81170052]; 973 Key Scheme of China [2015CB553406]; National Key Research and Development Project [2016YFC0903700]; Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2014); Guangdong Province Universities and Colleges Key Grant for Innovative Research [cxzd1142]; Guangzhou Municipal Research Project [201607020030]; Guangzhou Department of Education Innovative Team [13C08]; Guangzhou Department of Education Yangcheng Scholarship [12A001S]; Guangdong Natural Science Foundation [2016A030313593]; Guangzhou Municipal University Research Projects [1201430298]6 month embargo; published online: 6 November 2017This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Enhanced Tolerance to Chilling Stress in OsMYB3R-2 Transgenic Rice Is Mediated by Alteration in Cell Cycle and Ectopic Expression of Stress Genes1[W][OA]

MYB transcription factors play central roles in plant responses to abiotic stresses. How stress affects development is poorly understood. Here, we show that OsMYB3R-2 functions in both stress and developmental processes in rice (Oryza sativa). Transgenic plants overexpressing OsMYB3R-2 exhibited enhanced cold tolerance. Cold treatment greatly induced the expression of OsMYB3R-2, which encodes an active transcription factor. We show that OsMYB3R-2 specifically bound to a mitosis-specific activator cis-element, (T/C)C(T/C)AACGG(T/C)(T/C)A, a conserved sequence that was found in promoters of cyclin genes such as OsCycB1;1 and OsKNOLLE2. In addition, overexpression of OsMYB3R-2 in rice led to higher transcript levels of several G2/M phase-specific genes, including OsCycB1;1, OsCycB2;1, OsCycB2;2, and OsCDC20.1, than those in OsMYB3R-2 antisense lines or wild-type plants in response to cold treatment. Flow cytometry analysis revealed an increased cell mitotic index in overexpressed transgenic lines of OsMYB3R-2 after cold treatment. Furthermore, resistance to cold stress in the transgenic plants overexpressing OsCycB1;1 was also enhanced. The level of cellular free proline was increased in the overexpressed rice lines of OsMYB3R-2 and OsCycB1;1 transgenic plants compared with wild-type plants under the cold treatment. These results suggest that OsMYB3R-2 targets OsCycB1;1 and regulates the progress of the cell cycle during chilling stress. OsCPT1, which may be involved in the dehydration-responsive element-binding factor 1A pathway, showed the same transcription pattern in response to cold as did OsCycB1;1 in transgenic rice. Therefore, a cold resistance mechanism in rice could be mediated by regulating the cell cycle, which is controlled by key genes including OsMYB3R-2