Judgment Prediction Based on Tensor Decomposition With Optimized Neural Networks.

In the field of smart justice, handling legal cases through artificial intelligence technology is a research hotspot. Traditional judgment prediction methods are mainly based on feature models and classification algorithms. The former is difficult to describe cases from multiple angles and capture the correlation information between different case modules, while requires a wealth of legal expertise and manual labeling. The latter is unable to accurately extract the most useful information from case documents and produce fine-grained predictions. This article proposes a judgment prediction method based on tensor decomposition with optimized neural networks, which consists of OTenr, GTend, and RnEla. OTenr represents cases as normalized tensors. GTend decomposes normalized tensors into core tensors using the guidance tensor. RnEla intervenes in a case modeling process in GTend by optimizing the guidance tensor, so that core tensors represent tensor structural and elemental information, which is most conducive to improving the accuracy of judgment prediction. RnEla consists of the similarity correlation Bi-LSTM and optimized Elastic-Net regression. RnEla takes the similarity between cases as an important factor for judgment prediction. Experimental results on real legal case dataset show that the accuracy of our method is higher than that of the previous judgment prediction methods.

RnRTD: Intelligent Approach Based on the Relationship-Driven Neural Network and Restricted Tensor Decomposition for Multiple Accusation Judgment in Legal Cases.

The use of intelligent judgment technology to assist in judgment is an inevitable trend in the development of judgment in contemporary social legal cases. Using big data and artificial intelligence technology to accurately determine multiple accusations involved in legal cases is an urgent problem to be solved in legal judgment. The key to solving these problems lies in two points, namely, (1) characterization of legal cases and (2) classification and prediction of legal case data. Traditional methods of entity characterization rely on feature extraction, which is often based on vocabulary and syntax information. Thus, traditional entity characterization often requires extensive energy and has poor generality, thus introducing a large amount of computation and limitation to subsequent classification algorithms. This study proposes an intelligent judgment approach called RnRTD, which is based on the relationship-driven recurrent neural network (rdRNN) and restricted tensor decomposition (RTD). We represent legal cases as tensors and propose an innovative RTD method. RTD has low dependence on vocabulary and syntax and extracts the feature structure that is most favorable for improving the accuracy of the subsequent classification algorithm. RTD maps the tensors, which represent legal cases, into a specific feature space and transforms the original tensor into a core tensor and its corresponding factor matrices. This study uses rdRNN to continuously update and optimize the constraints in RTD so that rdRNN can have the best legal case classification effect in the target feature space generated by RTD. Simultaneously, rdRNN sets up a new gate and a similar case list to represent the interaction between legal cases. In comparison with traditional feature extraction methods, our proposed RTD method is less expensive and more universal in the characterization of legal cases. Moreover, rdRNN with an RTD layer has a better effect than the recurrent neural network (RNN) only on the classification and prediction of multiple accusations in legal cases. Experiments show that compared with previous approaches, our method achieves higher accuracy in the classification and prediction of multiple accusations in legal cases, and our algorithm is more interpretable.

De novo sequencing and comprehensive analysis of the mutant transcriptome from purple sweet potato (Ipomoea batatas L.).

Purple sweet potatoes, rich in anthocyanin, have been widely favored in light of increasing awareness of health and food safety. In this study, a mutant of purple sweet potato (white peel and flesh) was used to study anthocyanin metabolism by high-throughput RNA sequencing and comparative analysis of the mutant and wild type transcriptomes. A total of 88,509 unigenes ranging from 200nt to 14,986nt with an average length of 849nt were obtained. Unigenes were assigned to Gene Ontology (GO), Clusters of Orthologous Group (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Functional enrichment using GO and KEGG annotations showed that 3828 of the differently expressed genes probably influenced many important biological and metabolic pathways, including anthocyanin biosynthesis. Most importantly, the structural and transcription factor genes that contribute to anthocyanin biosynthesis were downregulated in the mutant. The unigene dataset that was used to discover the anthocyanin candidate genes can serve as a comprehensive resource for molecular research in sweet potato.

Resistance to Ditylenchus destructor Infection in Sweet Potato by the Expression of Small Interfering RNAs Targeting unc-15, a Movement-Related Gene.

Stem nematode (Ditylenchus destructor) is one of most serious diseases that limit the productivity and quality of sweet potato (Ipomoea batatas), a root crop with worldwide importance for food security and nutrition improvement. Hence, there is a global demand for developing sweet potato varieties that are resistant to the disease. In this study, we have investigated the interference of stem nematode infectivity by the expression of small interfering RNAs (siRNAs) in transgenic sweet potato that are homologous to the unc-15 gene, which affects the muscle protein paramyosin of the pathogen. The production of double-stranded RNAs and siRNAs in transgenic lines with a single transgene integration event was verified by Northern blot analysis. The expression of unc-15 was reduced dramatically in stem nematodes collected from the inoculated storage roots of transgenic plants, and the infection areas of their storage roots were dramatically smaller than that of wild-type (WT). Compared with the WT, the transgenic plants showed increased yield in the stem nematode-infested field. Our results demonstrate that the expression of siRNAs targeting the unc-15 gene of D. destructor is an effective approach in improving stem nematode resistance in sweet potato, in adjunct with the global integrated pest management programs.