This paper proposes an unsupervised bayesian algorithm for unmixing successive hyperspectral images while accounting for temporal and spatial variability of the endmembers. Each image pixel is modeled as a linear comb...
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ISBN:
(纸本)9781479988518
This paper proposes an unsupervised bayesian algorithm for unmixing successive hyperspectral images while accounting for temporal and spatial variability of the endmembers. Each image pixel is modeled as a linear combination of the end-members weighted by their corresponding abundances. Spatial endmember variability is introduced by considering the normal compositional model that assumes variable endmembers for each image pixel. A prior enforcing a smooth temporal variation of both endmembers and abundances is considered. The proposed algorithm estimates the mean vectors and covariance matrices of the endmembers and the abundances associated with each image. Since the estimators are difficult to express in closed form, we propose to sample according to the posterior distribution of interest and use the generated samples to build estimators. The performance of the proposed bayesian model and the corresponding estimation algorithm is evaluated by comparison with other unmixing algorithms on synthetic images.
With the continuous improvement of the level of industrialization, the air pollution situation in China has become more and more serious. In many places, extreme weather such as haze has appeared, which seriously thre...
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With the continuous improvement of the level of industrialization, the air pollution situation in China has become more and more serious. In many places, extreme weather such as haze has appeared, which seriously threatens people's health. Therefore, it is necessary to establish a scientific and reasonable air quality index prediction model. However, there are significant differences in air quality indices in different quarters, that is, the AQI values are significantly seasonal. Therefore, in order to improve the prediction accuracy, the data of different quarters are distinguished, and models of different quarters are established. In this paper, the principal component analysis method is used to analyze the correlation between API value and various meteorological factors, and the correlation factor is used as the input variable of neural network. The number of neurons in different quarters is determined according to the mean square error, and bayesian normalization is established. The neural network is a model of the algorithm. Finally, the corresponding model was used to predict the air quality index of the corresponding quarter in 2018 based on the winter, spring, summer and autumn air conditions of Beijing from 2014 to 2017. The results show that the forecasting accuracy of each quarter is 88.27%, 92.28%, 94.04%, and 91.01%, respectively. The prediction accuracy of most studies is 70%～90%, and the prediction accuracy is high, which has certain reference value.
Cryptographers agree that mobile configurations are an interesting new topic in the field of hardware and network information. In fact, few scholars would disagree with the construction of write-back caches. Our focus...
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Cryptographers agree that mobile configurations are an interesting new topic in the field of hardware and network information. In fact, few scholars would disagree with the construction of write-back caches. Our focus in this paper is not on whether the well-known bayesian algorithm for the emulation of architecture [1] runs in O(n) time, but rather on proposing an analysis of thin clients(Pye).
The prolongation of QT interval on electrocardiogram (ECG) is the current measure for cardiac safety that is used in drug development and drug approval. Although in thorough QT studies pharmaceutical companies need to...
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The prolongation of QT interval on electrocardiogram (ECG) is the current measure for cardiac safety that is used in drug development and drug approval. Although in thorough QT studies pharmaceutical companies need to measure QT intervals for thousands of beats, they mainly rely on experts to mark the QT interval end-points. However, selected beats of data show that the difference between two experts' marks can easily exceed 10 milliseconds. Note that for QT analyses presented to the FDA, if the maximal difference over all time points between QT measures comparing control to drug exceeds 10 milliseconds, the question of cardiac safety requires further discussion. Indeed experts appear to use the slope and curvature of the waveform differently in judging the end of the T-wave. This article developes a bayesian approach combining both slope and curvature information. We show that the difference between the automatic bayesian marks and either of the experts' marks is not statistically larger than the difference between two experts' marks, thus this approach is successful in closely approximating the experts' results in marking T-wave end, and it is much faster and cost efficient. Being algorithmic, our method offers the opportunity to be more consistent.
We developed a bayesian-based algorithm to infer gene expression states in individual samples and incorporated it into a workflow to identify tumor-associated antigens (TAAs) across 33 cancer types using RNA sequencin...
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We developed a bayesian-based algorithm to infer gene expression states in individual samples and incorporated it into a workflow to identify tumor-associated antigens (TAAs) across 33 cancer types using RNA sequencing (RNA-seq) data from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA). Our analysis identified 212 candidate TAAs, with 78 validated in independent RNA-seq datasets spanning seven cancer types. Eighteen of these TAAs were further corroborated by proteomics data, including 10 linked to liver cancer. We predicted that 38 peptides derived from these 10 TAAs would bind strongly to HLA-A02, the most common HLA allele. Experimental validation confirmed significant binding affinity and immunogenicity for 21 of these peptides. Notably, approximately 64% of liver tumors expressed one or more TAAs associated with these 21 peptides, positioning them as promising candidates for liver cancer therapies, such as peptide vaccines or T cell receptor (TCR)-T cell treatments. This study highlights the power of integrating computational and experimental approaches to discover TAAs for immunotherapy.
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