Legal Judgment Prediction (LJP) is the task of automatically predicting a law case's judgment results given a text describing its facts, which has excellent prospects in judicial assistance systems and convenient services for the public. In practice, confusing charges are frequent, because law cases applicable to similar law articles are easily misjudged. For addressing this issue, the existing method relies heavily on domain experts, which hinders its application in different law systems. In this paper, we present an end-to-end model, LADAN, to solve the task of LJP. To distinguish confusing charges, we propose a novel graph neural network to automatically learn subtle differences between confusing law articles and design a novel attention mechanism that fully exploits the learned differences to extract compelling discriminative features from fact descriptions attentively. Experiments conducted on realworld datasets demonstrate the superiority of our LADAN.

Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs, and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (LSTMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods. * Corresponding Authors † Nuo Xu and Pinghui Wang contributed equally to this work. Molecular formula a combined medication scheme Whether Allopurinol would increase the risk of a hypersensitivity reaction to Amoxicillin ? Bad scheme Not bad scheme No Allopurinol Amoxicillin Interaction prediction Represent as graphs Yes N O arXiv:1905.09558v1 [cs.LG]

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Legal Judgment Prediction (LJP) is the task of automatically predicting a law case's judgment results given a text describing its facts, which has excellent prospects in judicial assistance systems and convenient services for the public. In practice, confusing charges are frequent, because law cases applicable to similar law articles are easily misjudged. For addressing this issue, the existing method relies heavily on domain experts, which hinders its application in different law systems. In this paper, we present an end-to-end model, LADAN, to solve the task of LJP. To distinguish confusing charges, we propose a novel graph neural network to automatically learn subtle differences between confusing law articles and design a novel attention mechanism that fully exploits the learned differences to extract compelling discriminative features from fact descriptions attentively. Experiments conducted on realworld datasets demonstrate the superiority of our LADAN.

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The electrothermal aging process of cross-linked polyethylene (XLPE) insulation has been an essential factor for the reliability of high-voltage (HV) cables, but the underlying mechanism remains a puzzle. In this paper, the dielectric properties in the terahertz domain of sliced XLPE samples from an HVDC cable are investigated at an electrothermal aging duration up to 1500 h. It is found that with the increase in aging time, the real part and imaginary part of the complex permittivity decrease and increase, respectively. To explain the dielectric phenomenon, chemical changes and crystallinity are characterized by Fourier transform infrared spectrum (FTIR) and differential scanning calorimetry (DSC), respectively. It is proven that declined crystallinity is positively correlated with the real part of complex permittivity by using effective medium theory. Furthermore, by comparing the terahertz dielectric response of unaged and aged XLPE samples in different temperatures, the appearance of small polar molecules has been proven to be responsible for the rise of the imaginary part of complex permittivity.

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