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SUMMARY In plants, the known microRNAs (miRNAs) are produced as 21 nucleotide (nt) duplexes from their precursors by Dicer-like 1 (DCL1). They are incorporated into Argonaute 1 (AGO1) protein to regulate target gene expression primarily through mRNA cleavage. We report here the discovery of a class of miRNAs in the model monocot rice (Oryza sativa). These(More)
The effectiveness of supervised feature selection degrades in low training data scenarios. We propose to alleviate this problem by augmenting per-task feature selection with joint feature selection over multiple tasks. Our algorithm builds on the assumption that different tasks have shared structure which could be utilized to cope with data sparsity. The(More)
Human action recognition from motion videos plays an important role in multimedia analysis. Different from the temporal cues of action series in motion videos, the motion tendency can also be revealed from the still images or key frames. Thus, if the action knowledge in related still images can be well adapted to the target motion videos, we would have a(More)
Non-invasive prenatal testing (NIPT) is currently used as a frontline screening test to identify fetuses with common aneuploidies. Occasionally, incidental NIPT results are conveyed to the clinician suggestive of fetuses with rare chromosome disease syndromes. We describe a child with trisomy 9 (T9) mosaicism where the prenatal history reported a positive(More)
Feature selection is an important step for large-scale image data analysis, which has been proved to be difficult due to large size in both dimensions and samples. Feature selection firstly eliminates redundant and irrelevant features and then chooses a subset of features that performs as efficient as the complete set. Generally, supervised feature(More)
The spatial information is the important cue for human action recognition. Different from the vector representation, the spatial structure of human action in the still images can be preserved by the tensor representation. This paper proposes a robust human action recognition algorithm by tensor representation and Tucker decomposition. In this method, the(More)
  • L Zhang, J Yao, H Sun, Lei Zhang, Jun Yao, Hai Sun +1 other
  • 2015
The calculation sequence of collision, propagation and macroscopic variables is not very clear in lattice Boltzmann method (LBM) code implementation. According to the definition, three steps should be computed on all nodes respectively, which mean three loops are needed. While the " pull " scheme makes the only one loop possible for coding, this is called(More)
In this paper, we propose a new tensor-based representation algorithm for image classification. The algorithm is realized by learning the parameter tensor for image tensors. One novelty is that the parameter tensor is learned according to the Tucker tensor decomposition as the multiplication of a core tensor with a group of matrices for each order, which(More)
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