MGCN: Medical Relation Extraction Based on GCN

With the progress of society and the improvement of living standards, people pay more and more attention to personal health, and WITMED (Wise Information Technology of med) has occupied an important position. The relationship prediction work in the medical field has high requirements on the interpretability of the method, but the relationship between medical entities is complex, and the existing methods are difficult to meet the requirements. This paper proposes a novel medical information relation extraction method MGCN, which combines contextual information to provide global interpretability for relation prediction of medical entities. The method uses Co-occurrence Graph and Graph Convolutional Network to build up a network of relations between entities, uses the Open-world Assumption to construct potential relations between associated entities, and goes through the Knowledge-aware Attention mechanism to give relation prediction for the entity pair of interest. Experiments were conducted on a public medical dataset CTF, MGCN achieved the score of 0.831, demonstrating its effectiveness in medical relation extraction

Evaluation of Tunable Data Compression in Energy-Aware Wireless Sensor Networks

Energy is an important consideration in wireless sensor networks. In the current compression evaluations, traditional indices are still used, while energy efficiency is probably neglected. Moreover, various evaluation biases significantly affect the final results. All these factors lead to a subjective evaluation. In this paper, a new criterion is proposed and a series of tunable compression algorithms are reevaluated. The results show that the new criterion makes the evaluation more objective. Additionally it indicates the situations when compression is unnecessary. A new adaptive compression arbitration system is proposed based on the evaluation results, which improves the performance of compression algorithms

Layer Construction of Three-Dimensional Z2 Monopole Charge Nodal Line Semimetals and prediction of the abundant candidate materials

The interplay between symmetry and topology led to the concept of symmetry-protected topological states, including all non-interacting and weakly interacting topological quantum states. Among them, recently proposed nodal line semimetal states with space-time inversion ($\mathcal{PT}$) symmetry which are classified by the Stiefel-Whitney characteristic class associated with real vector bundles and can carry a nontrivial $\mathbb{Z}_2$ monopole charge have attracted widespread attention. However, we know less about such 3D $\mathbb{Z}_2$ nodal line semimetals and do not know how to construct them. In this work, we first extend the layer construction previously used to construct topological insulating states to topological semimetallic systems. We construct 3D $\mathbb{Z}_2$ nodal line semimetals by stacking of 2D $\mathcal{PT}$-symmetric Dirac semimetals via nonsymmorphic symmetries. Based on our construction scheme, effective model and combined with first-principles calculations, we predict two types of candidate electronic materials for $\mathbb{Z}_2$ nodal line semimetals, namely 14 Si and Ge structures and 108 transition metal dichalcogenides $MX_2$ ($M$=Cr, Mo, W, $X$=S, Se, Te). Our theoretical construction scheme can be directly applied to metamaterials and circuit systems. Our work not only greatly enriches the candidate materials and deepens the understanding of $\mathbb{Z}_2$ nodal line semimetal states but also significantly extends the application scope of layer construction

All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting

Recently, end-to-end text spotting that aims to detect and recognize text from cluttered images simultaneously has received particularly growing interest in computer vision. Different from the existing approaches that formulate text detection as bounding box extraction or instance segmentation, we localize a set of points on the boundary of each text instance. With the representation of such boundary points, we establish a simple yet effective scheme for end-to-end text spotting, which can read the text of arbitrary shapes. Experiments on three challenging datasets, including ICDAR2015, TotalText and COCO-Text demonstrate that the proposed method consistently surpasses the state-of-the-art in both scene text detection and end-to-end text recognition tasks.Comment: Accepted to AAAI202