Gate Feature Interaction Network for Relation Prediction in Knowledge Graph

Recently,many knowledge graph embedding models for knowledge graph completion have been proposed,ranging from the initial translation-based model such as TransE to recent CNN-based models such as ConvE.These models fill in the missing relations between entities by focusing on capturing the representation features to further complete the existing knowledge graph(KG).However,the above KG-based relation prediction research ignores the interaction information among entities in KG.To solve this problem,this work proposes a novel model called Gate Feature Interaction Network(GFINet)with a weighted loss function that takes the benefit of interaction information and deep expressive features together.Specifically,the proposed GFINet consists of a gate convolution block and an interaction attention module,corresponding to catching deep expressive features and interaction information based on these valid features respectively.Our method establishes state-of-the-art experimental results on the standard datasets for knowledge graph completion.In addition,we make ablation experiments to verify the effectiveness of the gate convolution block and the interaction attention module.

Application of Question and Answering on Virtual Human Dialogue:a Review and Prediction

Nowadays,virtual human(VH) is becoming a hot research topic in virtualization.VH dialogue can be categorized as an application of natural language processing(NLP) technology,since it is relational to question and answering(QA) technologies.In order to integrate these technologies,this paper reviews some important work on VH dialogue,and predicts some research points on the view of QA technologies.

Support principles of NPR bolt/cable and control techniques of large-deformation disasters

Deep mining has been paid much more attention because of the depletion of shallow mining resources.Traditional bolts could be invalid to accommodate large displacement and deformation in geomaterials.Consequently, alternative support and reinforcement bolts need to be studied and their constitutive models also need to be developed to help understanding for the complex stress-strain responses of rock masses under loadings. The effect of Negative Poisson's Ratio(NPR) that is attributed to the swelling phenomenon along the lateral direction may appear in metal materials under tensional loadings. Thence NPR materials often have an advantage over NPR ones in mechanical behavior such as impact resistance, antishearing, and energy absorbed. From the characteristics of NPR materials, a series of bolt and cable supports with the effect of NPR and constant-resistance have been recently developed. We here firstly introduce the structural features of NPR support. Then the constitutive model of NPR support is presented and its corresponding equation of energy equilibrium. Its basic principle interacted on rock masses is further discussed. Finally, NPR cables are employed to support the slope of an open-pit mine. The applications show that NPR cables can ease failure within the slope and play an important role in predicting and providing early warning of slope failure, together with a monitoring system of slope stability.

Energy Balance-related Behaviors Are Related to Cardiometabolic Parameters and Predict Adiposity in 8-14-year-old Overweight Chinese Children One Year Later

To identify target energy balance-related behaviors（ERBs）,baseline data from 141overweight or obese schoolchildren（aged 8-14years old）was used to predict adiposity[body mass index（BMI）and fat percentage]one year later.The ERBs included a modified Dietary Approach to Stop Hypertension diet score（DASH score）,leisure-time physical activity（PA,days/week）,and leisure screen time（minutes/day）.Several cardiometabolic variables were measured in the fasting state, including systolic blood pressure （SBP）, diastolic blood pressure （DBP）, blood glucose （GLU）, total cholesterol （TC）, triglycerides （TG）, low-density lipoprotein （LDL-C）, and high-density lipoprotein （HDL-C）.