The rapid, recent development of image recognition technologies has led to the widespread use of convolutional neural networks (CNNs) in automated image classification and in the recognition of plant diseases. Aims: The aim of the present study was to develop a deep CNNs to identify tea plant disease types from leaf images. Materials: A CNNs model named LeafNet was developed with different sized feature extractor filters that automatically extract the features of tea plant diseases from images. DSIFT (dense scale-invariant feature transform) features are also extracted and used to construct a bag of visual words (BOVW) model that is then used to classify diseases via support vector machine(SVM) and multi-layer perceptron(MLP) classifiers. The performance of the three classifiers in disease recognition were then individually evaluated. Results: The LeafNet algorithm identified tea leaf diseases most accurately, with an average classification accuracy of 90.16%, while that of the SVM algorithm was 60.62% and that of the MLP algorithm was 70.77%. Conclusions: The LeafNet was clearly superior in the recognition of tea leaf diseases compared to the MLP and SVM algorithms. Consequently, the LeafNet can be used in future applications to improve the efficiency and accuracy of disease diagnoses in tea plants.

Training data for sentiment analysis are abundant in multiple domains, yet scarce for other domains. It is useful to leveraging data available for all existing domains to enhance performance on different domains. We investigate this problem by learning domain-specific representations of input sentences using neural network. In particular, a descriptor vector is learned for representing each domain, which is used to map adversarially trained domaingeneral Bi-LSTM input representations into domain-specific representations. Based on this model, we further expand the input representation with exemplary domain knowledge, collected by attending over a memory network of domain training data. Results show that our model outperforms existing methods on multidomain sentiment analysis significantly, giving the best accuracies on two different benchmarks.

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Background: With the rapid emergence of RNA databases and newly identified non-coding RNAs, an efficient compression algorithm for RNA sequence and structural information is needed for the storage and analysis of such data. Although several algorithms for compressing DNA sequences have been proposed, none of them are suitable for the compression of RNA sequences with their secondary structures simultaneously. This kind of compression not only facilitates the maintenance of RNA data, but also supplies a novel way to measure the informational complexity of RNA structural data, raising the possibility of studying the relationship between the functional activities of RNA structures and their complexities, as well as various structural properties of RNA based on compression.

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