Counting Process Based Dimension Reduction Methods for Censored Outcomes

We propose a class of dimension reduction methods for right censored survival data using a counting process representation of the failure process. Semiparametric estimating equations are constructed to estimate the dimension reduction subspace for the failure time model. The proposed method addresses two fundamental limitations of existing approaches. First, using the counting process formulation, it does not require any estimation of the censoring distribution to compensate the bias in estimating the dimension reduction subspace. Second, the nonparametric part in the estimating equations is adaptive to the structural dimension, hence the approach circumvents the curse of dimensionality. Asymptotic normality is established for the obtained estimators. We further propose a computationally efficient approach that simplifies the estimation equation formulations and requires only a singular value decomposition to estimate the dimension reduction subspace. Numerical studies suggest that our new approaches exhibit significantly improved performance for estimating the true dimension reduction subspace. We further conduct a real data analysis on a skin cutaneous melanoma dataset from The Cancer Genome Atlas. The proposed method is implemented in the R package "orthoDr".Comment: First versio

In Vitro Regeneration of \u3ci\u3eRudbeckia hirta\u3c/i\u3e ‘Plainview Farm’ from Leaf Tissue

Rudbeckia hirta ‘Plainview Farm’, a new multiple-layered ray flowered cultivar, shows potential for potted plant production. After years of seed germination, this specific flower morphological trait was still unstable from generation to generation. To maintain its unique features, leaf sections (0.25 cm2 ) were cultured on Murashige and Skoog (MS) medium supplemented with either BA (0.5, 1.0, or 2.0 mg·L1 ), KIN (2.5, 5, or 10 mg·L-1 ), or ZT (0.5, 1.0, or 2.0 mg·L-1 )toinduce callus and microshoots. After cultivation for 33 days, all cytokinin treatments significantly induced callus and the callus size were 1.5- to-2.4-fold bigger than those withoutcytokinin. KIN at 2.5 mg·L-1 was the best treatment for callus induction and microshoot formation. Four microshoots per explant wereproduced at KIN of 2.5 mg·L-1 . For rooting, all induced microshoots were cultured on MS medium at its one-quarter strength containing either IBA or NAA at 0.5, 1.5, or 3.0 mg·L-1 . All microshoots formed roots at 0.5 or 1.5 mg·L-1 IBA, or 0.5 mg·L-1 NAA. There were no significant differences in number of roots per shoot and length of roots among treatments. The plantlets were transplanted, acclimated in a mist system, and grown in a greenhouse. A total of 96.4% of the plants derived from tissue culture had multiple layers of ray flowers, while only 9.6% of the plants from seed propagation did. Therefore, in vitro regeneration of R. hirta ‘Plainview Farm’ was a feasible way to rapidly produce uniform plants with multiple layers of ray flowers

PSSA: PCA-domain superpixelwise singular spectral analysis for unsupervised hyperspectral image classification.

Although supervised classification of hyperspectral images (HSI) has achieved success in remote sensing, its applications in real scenarios are often constrained, mainly due to the insufficiently available or lack of labelled data. As a result, unsupervised HSI classification based on data clustering is highly desired, yet it generally suffers from high computational cost and low classification accuracy, especially in large datasets. To tackle these challenges, a novel unsupervised spatial-spectral HSI classification method is proposed. By combining the entropy rate superpixel segmentation (ERS), superpixel-based principal component analysis (PCA), and PCA-domain 2D singular spectral analysis (SSA), both the efficacy and efficiency of feature extraction are improved, followed by the anchor-based graph clustering (AGC) for effective classification. Experiments on three publicly available and five self-collected aerial HSI datasets have fully demonstrated the efficacy of the proposed PCA-domain superpixelwise SSA (PSSA) method, with a gain of 15–20% in terms of the overall accuracy, in comparison to a few state-of-the-art methods. In addition, as an extra outcome, the HSI dataset we acquired is provided freely online

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37~95.25%