No abstract

Brain networks provide essential insights into the diagnosis of functional brain disorders, such as Alzheimer’s disease (AD). Many machine learning methods have been applied to learn from brain images or networks in Euclidean space. However, it is still challenging to learn complex network structures and the connectivity of brain regions in non-Euclidean space. To address this problem, in this paper, we exploit the study of brain network classification from the perspective of graph learning. We propose an aggregator based on extreme learning machine (ELM) that boosts the aggregation ability and efficiency of graph convolution without iterative tuning. Then, we design a graph neural network named GNEA (Graph Neural Network with ELM Aggregator) for the graph classification task. Extensive experiments are conducted using a real-world AD detection dataset to evaluate and compare the graph learning performances of GNEA and state-of-the-art graph learning methods. The results indicate that GNEA achieves excellent learning performance with the best graph representation ability in brain network classification applications.

As a variant task of time-series segmentation, trajectory segmentation is a key task in the applications of transportation pattern recognition and traffic analysis. However, segmenting trajectory is faced with challenges of implicit patterns and sparse results. Although deep neural networks have tremendous advantages in terms of high-level feature learning performance, deploying as a blackbox seriously limits the real-world applications. Providing explainable segmentations has significance for result evaluation and decision making. Thus, in this article, we address trajectory segmentation by proposing a Bayesian Encoder-Decoder Network (BED-Net) to provide accurate detection with explainability and references for the following active-learning procedures. BED-Net consists of a segmentation module based on Monte Carlo dropout and an explanation module based on uncertainty learning that provides results evaluation and visualization. Experimental results on both benchmark and real-world datasets indicate that BED-Net outperforms the rival methods and offers excellent explainability in the applications of trajectory segmentation.

Oil shale is an important unconventional oil and gas resource, but it is poorly utilized in the Dalianhe coal mining area, northeastern China. Systematic sampling and test analysis were used to evaluate the characteristics of oil shale quality. The research shows that the oil shale between the middle and lower coal seams in the coal-bearing member is a rich ore with a high oil content, medium calorific value, high ash content, medium volatile content, low moisture, and ultralow sulfur content. The oil shale has good quality and is suitable for low-temperature carbonization for oil refining or low-calorific fuel. The ash content is silicon–aluminum-rich, has low calcium–magnesium, is iron-poor, and has a high ash melting point. The middle oil shale member is a poor ore with low quality. However, there is a dense section of oil shale with a high organic matter content and good quality above the upper coal seam. It is recommended to implement combined mining of coal and oil shale in the mineshaft and to reasonably recover oil shale between the coal seams and oil shale above the upper coal seam to improve the resource utilization rate.