A Network Science perspective of Graph Convolutional Networks: A survey

The mining and exploitation of graph structural information have been the focal points in the study of complex networks. Traditional structural measures in Network Science focus on the analysis and modelling of complex networks from the perspective of network structure, such as the centrality measures, the clustering coefficient, and motifs and graphlets, and they have become basic tools for studying and understanding graphs. In comparison, graph neural networks, especially graph convolutional networks (GCNs), are particularly effective at integrating node features into graph structures via neighbourhood aggregation and message passing, and have been shown to significantly improve the performances in a variety of learning tasks. These two classes of methods are, however, typically treated separately with limited references to each other. In this work, aiming to establish relationships between them, we provide a network science perspective of GCNs. Our novel taxonomy classifies GCNs from three structural information angles, i.e., the layer-wise message aggregation scope, the message content, and the overall learning scope. Moreover, as a prerequisite for reviewing GCNs via a network science perspective, we also summarise traditional structural measures and propose a new taxonomy for them. Finally and most importantly, we draw connections between traditional structural approaches and graph convolutional networks, and discuss potential directions for future research

Child health insurance coverage: a survey among temporary and permanent residents in Shanghai

<p>Abstract</p> <p>Background</p> <p>Under the current healthcare system in China, there is no government-sponsored health insurance program for children. Children from families who move from rural and interior regions to large urban centres without a valid residency permit might be at higher risk of being uninsured due to their low socioeconomic status. We conducted a survey in Shanghai to describe children's health insurance coverage according to their migration status.</p> <p>Method</p> <p>Between 2005 and 2006, we conducted an in-person health survey of the adult care-givers of children aged 7 and under, residing in five districts of Shanghai. We compared uninsurance rates between temporary and permanent child residents, and investigated factors associated with child health uninsurance.</p> <p>Results</p> <p>Even though cooperative insurance eligibility has been extended to temporary residents of Shanghai, the uninsurance rate was significantly higher among temporary (65.6%) than permanent child residents (21.1%, adjusted odds ratio (OR): 5.85, 95% confidence interval (95% CI): 4.62–7.41). For both groups, family income was associated with having child health insurance; children in lower income families were more likely to be uninsured (OR: 1.96, 95% CI: 1.40–2.96).</p> <p>Conclusion</p> <p>Children must rely on their parents to make the insurance purchase decision, which is constrained by their income and the perceived benefits of the insurance program. Children from migrant families are at even higher risk for uninsurance due to their lower socioeconomic status. Government initiatives specifically targeting temporary residents and providing health insurance benefits for their children are urgently needed.</p

An Efficient License Plate Detection Approach With Deep Convolutional Neural Networks in Unconstrained Scenarios

License plate (LP) detection is a crucial task for Automatic License Plate Recognition (ALPR) systems. Most existing LP detection networks can detect License plates, but their accuracy suffers when license plates (LPs) are tilted or deformed due to perspective distortion. This is because these detectors can only detect the region where the LP is located, and even the most advanced object detectors struggle in unconstrained scenarios. To address this problem, we propose a lightweight Deformation Planar Object Detection Network (DPOD-NET), which can correct the deformed LPs of various vehicles (e.g., car, truck, electric motorcycle, bus) by detecting the LP corner points. Accordingly, the distortion associated with perspective is mitigated when we adjust the LP to a frontal parallel view through the LP corners. To optimize small errors between the predicted and true values of the LP corner points, we propose an LPWing loss function. Compared with the commonly used L1 function, the LPWing loss is derivable at the zero position, and the gradient will be bigger when errors are smaller. This enables the model to converge faster at the position where the error is close to zero, resulting in better convergence when the error between the true values and predicted values is small. In addition, the paper presents a stochastic multi-scale image detail boosting strategy, which effectively augments the dataset. Finally, to objectively evaluate the effectiveness of LP corner detection approaches, we present a dataset (LPDE-4K) including various LP types (e.g., color, country, illumination, distortion). We test the performance on various datasets, and our approach outperforms other existing state-of-the-art approaches in terms of higher accuracy and lower computational cost

Encoding edge type information in graphlets

Graph embedding approaches have been attracting increasing attention in recent years mainly due to their universal applicability. They convert network data into a vector space in which the graph structural information and properties are maximumly preserved. Most existing approaches, however, ignore the rich information about interactions between nodes, i.e., edge attribute or edge type. Moreover, the learned embeddings suffer from a lack of explainability, and cannot be used to study the effects of typed structures in edge-attributed networks. In this paper, we introduce a framework to embed edge type information in graphlets and generate a Typed-Edge Graphlets Degree Vector (TyE-GDV). Additionally, we extend two combinatorial approaches, i.e., the colored graphlets and heterogeneous graphlets approaches to edge-attributed networks. Through applying the proposed method to a case study of chronic pain patients, we find that not only the network structure of a patient could indicate his/her perceived pain grade, but also certain social ties, such as those with friends, colleagues, and healthcare professionals, are more crucial in understanding the impact of chronic pain. Further, we demonstrate that in a node classification task, the edge-type encoded graphlets approaches outperform the traditional graphlet degree vector approach by a significant margin, and that TyE-GDV could achieve a competitive performance of the combinatorial approaches while being far more efficient in space requirements

Organic silicone sol-gel polymer as a noncovalent carrier of receptor proteins for label-free optical biosensor application

Optical biosensing techniques have become of key importance for label-free monitoring of biomolecular interactions in the current proteomics era. Together with an increasing emphasis on high-throughput applications in functional proteomics and drug discovery, there has been demand for facile and generally applicable methods for the immobilization of a wide range of receptor proteins. Here, we developed a polymer platform for microring resonator biosensors, which allows the immobilization of receptor proteins on the surface of waveguide directly without any additional modification. A sol-gel process based on a mixture of three precursors was employed to prepare a liquid hybrid polysiloxane, which was photopatternable for the photocuring process and UV imprint. Waveguide films were prepared on silicon substrates by spin coating and characterized by atomic force microscopy for roughness, and protein adsorption. The results showed that the surface of the polymer film was smooth (rms = 0.658 nm), and exhibited a moderate hydrophobicity with the water contact angle of 97 degrees. Such a hydrophobic extent could provide a necessary binding strength for stable immobilization of proteins on the material surface in various sensing conditions. Biological activity of the immobilized Staphylococcal protein A and its corresponding biosensing performance were demonstrated by its specific recognition of human Immunoglobulin G. This study showed the potential of preparing dense, homogeneous, specific, and stable biosensing surfaces by immobilizing receptor proteins on polymer-based optical devices through the direct physical adsorption method. We expect that such polymer waveguide could be of special interest in developing low-cost and robust optical biosensing platform for multidimensional arrays

Microstructure and electrical conductivity of (Y,Sr)CoO3-delta thin films tuned by the film-growth temperature

Epitaxial films composed of (Y,Sr)CoO3-δ and nano-scale Y2O3 columns are successfully grown on (La0.289Sr0.712)(Al0.633Ta0.356)O3(001) substrates at 900 °C. The microstructural and electrical properties of the composite films are investigated and compared with those of the single-phase films prepared at 800 °C. In the composite films oxygen vacancies are detectable, which occur alternately in the stacking CoO2-δ planes of (Y,Sr)CoO3-δ. In addition, it is found that a large number of misfit dislocations distribute at the interfaces between the Y2O3 columns and the (Y,Sr)CoO3-δ film matrix. The measured resistivity of the composite films is significantly lower than that of the (Y,Sr)CoO3-δ single-phase films. Our results indicate that the electrical properties of the perovskite-based cobaltates films can be tuned by changing the microstructure through controlling the film-growth temperature