ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction

Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes, which are inadequately capturing the intrinsic semantics of these relations. To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations. Specifically, ProtoEM extracts event relations in a two-step manner, i.e., prototype representing and prototype matching. In the first step, to capture the connotations of different event relations, ProtoEM utilizes examples to represent the prototypes corresponding to these relations. Subsequently, to capture the interdependence among event relations, it constructs a dependency graph for the prototypes corresponding to these relations and utilized a Graph Neural Network (GNN)-based module for modeling. In the second step, it obtains the representations of new event pairs and calculates their similarity with those prototypes obtained in the first step to evaluate which types of event relations they belong to. Experimental results on the MAVEN-ERE dataset demonstrate that the proposed ProtoEM framework can effectively represent the prototypes of event relations and further obtain a significant improvement over baseline models.Comment: Work in progres

Correg-YOLOv3:a method for dense buildings detection in high-resolution remote sensing images

The exploration of building detection plays an important role in urban planning, smart city and military. Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high-resolution remote sensing images, we present an effective YOLOv3 framework, corner regression-based YOLOv3 (Correg-YOLOv3), to localize dense building accurately. This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box. By extending output dimensions, the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile. Finally, we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively. The experimental results achieve high performance in precision (96.45%), recall rate (95.75%), F1 score (96.10%) and average precision (98.05%), which were 2.73%, 5.4%, 4.1% and 4.73% higher than that of YOLOv3. Therefore, the proposed algorithm effectively tackles the problem of dense building detection in high-resolution images

Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning, smart city and military. Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images, we present an effective YOLOv3 framework, corner regression-based YOLOv3 (Correg-YOLOv3), to localize dense building accurately. This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box. By extending output dimensions, the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile. Finally, we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively. The experimental results achieve high performance in precision (96.45%), recall rate (95.75%), F1 score (96.10%) and average precision (98.05%), which were 2.73%, 5.4%, 4.1% and 4.73% higher than that of YOLOv3. Therefore, our proposed algorithm effectively tackles the problem of dense building detection in high resolution images

Significant Enhancement of 5-Hydroxymethylfural Productivity from <i>D</i>-Fructose with SG(SiO₂) in Betaine:Glycerol–Water for Efficient Synthesis of Biobased 5-(Hydroxymethyl)furfurylamine

5-Hydroxymethyl-2-furfurylamine (5-HMFA) as an important 5-HMF derivative has been widely utilized in the manufacture of diuretics, antihypertensive drugs, preservatives and curing agents. In this work, an efficient chemoenzymatic route was constructed for producing 5-(hydroxymethyl)furfurylamine (5-HMFA) from biobased D-fructose in deep eutectic solvent Betaine:Glycerol–water. The introduction of Betaine:Glycerol could greatly promote the dehydration of D-fructose to 5-HMF and inhibit the secondary decomposition reactions of 5-HMF, compared with a single aqueous phase. D-Fructose (200 mM) could be catalyzed to 5-HMF (183.4 mM) at 91.7% yield by SG(SiO2) (3 wt%) after 90 min in Betaine:Glycerol (20 wt%), and at 150 °C. E. coli AT exhibited excellent bio-transamination activity to aminate 5-HMF into 5-HMFA at 35 °C and pH 7.5. After 24 h, D-fructose-derived 5-HMF (165.4 mM) was converted to 5-HMFA (155.7 mM) in 94.1% yield with D-Ala (D-Ala-to-5-HMF molar ratio 15:1) in Betaine:Glycerol (20 wt%) without removal of SG(SiO2), achieving a productivity of 0.61 g 5-HMFA/(g substrate D-fructose). Chemoenzymatic valorization of D-fructose with SG(SiO2) and E. coli AT was established for sustainable production of 5-HMFA, which has potential application

A Hand-drawn Map Retrieval Method Based on Open Area Spatial Direction Relation

To meet the needs of retrieving the geographic information intelligently,a hand-drawn map retrieval method based on open area spatial direction relation is proposed.Firstly,it is designed that a new type of open area spatial direction relation description model and calculation method based on the opening area between the hand-objects,which can be used to hand-drawn map retrieval,both can be adapted to accurately describe by open area,but also through the relaxation strategy to describe the fuzzy relationship.A hand-drawn map retrieval process based on open area spatial direction relation is proposed.The evaluation model,similarity calculation of spatial direction relation and R-tree spatial index are given.The experiment results show that the method can retrieve the top-ranking results effectively in a large scope map scene