CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension

We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers. The documents come from judgment documents and the questions are annotated by law experts. The CJRC dataset can help researchers extract elements by reading comprehension technology. Element extraction is an important task in the legal field. However, it is difficult to predefine the element types completely due to the diversity of document types and causes of action. By contrast, machine reading comprehension technology can quickly extract elements by answering various questions from the long document. We build two strong baseline models based on BERT and BiDAF. The experimental results show that there is enough space for improvement compared to human annotators

Research on Hyperspectral Model for Blacksoil Organic Matter Estimation in Songnen Plain, China

Models of soil reflectance for soil organic matter estimation are needed to quantify soil and vegetation spectral properties. The characteristics of Blacksoil in Songnen Plain, China, fit for quickly estimating organic matter content with hyperspectral reflectance models. Correlation analysis and multivariable statistical methods were used to build hyperspectral models for blacksoil organic matter content estimation, with spectral reflectance and its derivatives as independent variables and the Curvature and Ratio Indices introduced. Finally, the stability and capacity of the models were tested. The results are as follows: 620-810 nm bands are the main response spectrum of blacksoil organic matter, and the maximum is at 710 nm; normalizing spectral data partly eliminates the noises introduced when testing different samples; logtransforming SOM content improve the stability and prediction ability of the linear regression models; compared with other models, the multi-liner regression models are more stable and predictable; Normalized Spectra Derivate Multivariate Stepwise Linear Regression Model is the optimal one, and the Bow-curvature Linear Model is the most feasible one, the hyperspectral model can be used to measure blacksoil organic matter content quickly

Pattern Recommendation in Task-oriented Applications: A Multi-Objective Perspective [Application Notes]

Zhang X, Duan F, Zhang L, Cheng F, Jin Y, Tang K. Pattern Recommendation in Task-oriented Applications: A Multi-Objective Perspective [Application Notes]. IEEE Computational Intelligence Magazine. 2017;12(3):43-53.Task-oriented pattern mining is to find the most popular and complete pattern for task-oriented applications such as goods match recommendation and print area recommendation. In these applications, the measure support is used to capture the popularity of patterns, while the measure occupancy is adopted to capture the completeness of patterns. Existing methods for mining task-oriented patterns usually combine these two measures as one measure for optimization, and require users to set the prior parameters such as the minimum support threshold min_sup, the minimum occupancy threshold min_occ and the relative importance preference λ between support and occupancy. However, it is very difficult for users to set optimal values for these parameters especially when they do not have any prior knowledge in real applications. To overcome this challenge, we propose an evolutionary approach for pattern mining from a multi-objective perspective since support and occupancy are conflicting. Specifically, we first transform this pattern mining problem into a multi-objective optimization problem. Then we propose an effective multi-objective pattern mining evolutionary algorithm for finding optimal pattern set, which does not need to specify the prior parameters min_sup, min_occ and m. Finally, we select k best patterns from the obtained pattern set for final pattern recommendation. Experimental results on two real task-oriented applications, namely, goods match recommendation in Taobao and print area recommendation in SmartPrint, and several large synthetic datasets demonstrate the promising performance of the proposed method in terms of both effectiveness and efficiency

Transcriptomics Analyses Reveal Wheat Responses to Drought Stress during Reproductive Stages under Field Conditions

Drought is a major abiotic stress that limits wheat production worldwide. To ensure food security for the rapidly increasing world population, improving wheat yield under drought stress is urgent and relevant. In this study, an RNA-seq analysis was conducted to study the effect of drought on wheat transcriptome changes during reproductive stages under field conditions. Our results indicated that drought stress during early reproductive periods had a more severe impact on wheat development, gene expression and yield than drought stress during flowering. In total, 115,656 wheat genes were detected, including 309 differentially expressed genes (DEGs) which responded to drought at various developmental stages. These DEGs were involved in many critical processes including floral development, photosynthetic activity and stomatal movement. At early developmental stages, the proteins of drought-responsive DEGs were mainly located in the nucleus, peroxisome, mitochondria, plasma membrane and chloroplast, indicating that these organelles play critical roles in drought tolerance in wheat. Furthermore, the validation of five DEGs confirmed their responsiveness to drought under different genetic backgrounds. Functional verification of DEGs of interest will occur in our subsequent research. Collectively, the results of this study not only advanced our understanding of wheat transcriptome changes under drought stress during early reproductive stages but also provided useful targets to manipulate drought tolerance in wheat at different development stages

A parabrachial to hypothalamic pathway mediates defensive behavior

Defensive behaviors are critical for animal’s survival. Both the paraventricular nucleus of the hypothalamus (PVN) and the parabrachial nucleus (PBN) have been shown to be involved in defensive behaviors. However, whether there are direct connections between them to mediate defensive behaviors remains unclear. Here, by retrograde and anterograde tracing, we uncover that cholecystokinin (CCK)-expressing neurons in the lateral PBN (LPBCCK) directly project to the PVN. By in vivo fiber photometry recording, we find that LPBCCK neurons actively respond to various threat stimuli. Selective photoactivation of LPBCCK neurons promotes aversion and defensive behaviors. Conversely, photoinhibition of LPBCCK neurons attenuates rat or looming stimuli-induced flight responses. Optogenetic activation of LPBCCK axon terminals within the PVN or PVN glutamatergic neurons promotes defensive behaviors. Whereas chemogenetic and pharmacological inhibition of local PVN neurons prevent LPBCCK-PVN pathway activation-driven flight responses. These data suggest that LPBCCK neurons recruit downstream PVN neurons to actively engage in flight responses. Our study identifies a previously unrecognized role for the LPBCCK-PVN pathway in controlling defensive behaviors