Deep LSTM with Guided Filter for Hyperspectral Image Classification

Hyperspectral image (HSI) classification has been a hot topic in the remote sensing community. A large number of methods have been proposed for HSI classification. However, most of them are based on the extraction of spectral feature, which leads to information loss. Moreover, they rarely consider the correlation among the spectrums. In this paper, we see spectral information as a sequential data which should be relevant to each other. We introduce long short-term memory (LSTM) model, which is a typical recurrent neural network (RNN), to deal with HSI classification. To tackle the problem of overfitting caused by limited labeled samples, regularization strategy is introduced. For unbalance in different classes, we improve LSTM by weighted cost function. Also, we employ guided filter to smooth the HSI that can greatly improve the classification accuracy. And we proposed a method for modeling hyperspectral sequential data, which is very useful for future research work. Finally, the experimental results show that our proposed method can improve the classification performance as compared to other methods in three popular hyperspectral datasets

Effect of 5-aminolevulinic acid on yield and quality of lettuce in sunlit greenhouse

The role of 5-aminolevulinic acid (ALA) as a precursor of chlorophyll and heme is well documented. Low concentration of exogenous ALA has been found to regulate plant growth and increase crop yield, but there is little information on how ALA influences the yield and quality of lettuce in sunlit greenhouse. Here, we report the effects of ALA on photosynthetic rate, yield and quality of lettuce in sunlit greenhouse.5-aminolevulinic acid and 5-aminolevulinic acid with nitrogen fertilizer (ALA+N) were applied by foliage and soil. The results showed that application of ALA improved the photosynthetic rate of lettuce leaves by 23.9 to 34.7% and by 35.3 to 41.6%. Moreover, exogenous ALA increased vitamin C and soluble sugar content, reduced nitrate and crude fiber content and lead to better quality and taste of lettuce.Keywords: 5-aminolevulinic acid, lettuce, plant growth, promotive effects, yield, vegetable qualit

Translational signatures and mRNA levels are highly correlated in human stably expressed genes

BACKGROUND: Gene expression is one of the most relevant biological processes of living cells. Due to the relative small population sizes, it is predicted that human gene sequences are not strongly influenced by selection towards expression efficiency. One of the major problems in estimating to what extent gene characteristics can be selected to maximize expression efficiency is the wide variation that exists in RNA and protein levels among physiological states and different tissues. Analyses of datasets of stably expressed genes (i.e. with consistent expression between physiological states and tissues) would provide more accurate and reliable measurements of associations between variations of a specific gene characteristic and expression, and how distinct gene features work to optimize gene expression. RESULTS: Using a dataset of human genes with consistent expression between physiological states we selected gene sequence signatures related to translation that can predict about 42% of mRNA variation. The prediction can be increased to 51% when selecting genes that are stably expressed in more than 1 tissue. These genes are enriched for translation and ribosome biosynthesis processes and have higher translation efficiency scores, smaller coding sequences and 3(′) UTR sizes and lower folding energies when compared to other datasets. Additionally, the amino acid frequencies weighted by expression showed higher correlations with isoacceptor tRNA gene copy number, and smaller absolute correlation values with biosynthetic costs. CONCLUSION: Our results indicate that human gene sequence characteristics related to transcription and translation processes can co-evolve in an integrated manner in order to optimize gene expression

Causal Inference in Recommender Systems: A Survey and Future Directions

Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, unfortunately, the real world is driven by causality, not just correlation, and correlation does not imply causation. For instance, recommender systems might recommend a battery charger to a user after buying a phone, where the latter can serve as the cause of the former; such a causal relation cannot be reversed. Recently, to address this, researchers in recommender systems have begun utilizing causal inference to extract causality, thereby enhancing the recommender system. In this survey, we offer a comprehensive review of the literature on causal inference-based recommendation. Initially, we introduce the fundamental concepts of both recommender system and causal inference as the foundation for subsequent content. We then highlight the typical issues faced by non-causality recommender system. Following that, we thoroughly review the existing work on causal inference-based recommender systems, based on a taxonomy of three-aspect challenges that causal inference can address. Finally, we discuss the open problems in this critical research area and suggest important potential future works.Comment: Accepted by ACM Transactions on Information Systems (TOIS