Early detection of COPD based on graph convolutional network and small and weakly labeled data

Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation

Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning. Due to the complexity of road traffic flow data, traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy. In this study, a combined flow direction level traffic flow prediction graph convolutional network (GCN) and long short-term memory (LSTM) model based on spatiotemporal characteristics is proposed. First, a GCN model is employed to capture the topological structure of the data graph and extract the spatial features of road networks. Additionally, due to the capability to handle long-term dependencies, the long-term memory is used to predict the time series of traffic flow and extract the time features. The proposed model is evaluated using real-world data, which are obtained from the intersection of Liuquan Road and Zhongrun Avenue in the Zibo High-Tech Zone of China. The results show that the developed combined GCN-LSTM flow direction level traffic flow prediction model can perform better than the single models of the LSTM model and GCN model, and the combined ARIMA-LSTM model in traffic flow has a strong spatiotemporal correlation.

UK COPD treatment: failing to progress

Multimodal emotion recognition in conversation (MERC) involves predicting the emotion category of a conversation on the basis of textual, acoustic, and visual modalities. Information from these diverse modalities can reinforce each other to enhance the accuracy of emotion prediction. However, some information modalities may be absent in real-world applications and information from various modalities may be difficult to integrate. Therefore, a suitable strategy is required to compensate for missing modalities by using information from the available modalities and prioritizing important information. Consequently, this study developed a graph convolutional network (GCN) model with a feature compensation module and dual-channel second-order pooling module for MERC. This model initially uses a GCN to compensate for missing features by aggregating features corresponding to the same utterance node. Subsequently, it applies dual-channel second-order pooling to sift through and integrate all features. Empirical evaluations of the proposed model against other baseline models on two benchmark datasets, namely the IEMOCAP and MELD datasets, indicated that the proposed model outperformed the other models; this finding underscores the effectiveness of the proposed model in recognizing the emotions expressed in multimodal dialogue data.

ABSTRACTLarge‐area, high‐resolution organic light‐emitting diode (OLED) displays require high‐performance oxide thin‐film transistors (TFTs) with excellent mobility and stability, as well as little threshold voltage shift, especially for G8.5+ OLED production lines. Our previous work demonstrated rare‐earth doped oxides are ideal channel layer materials for TFTs due to their superior mobility and stability. However, scaling up TFT production from pilot lines to mass manufacturing poses significant challenges, largely because of the complexity of multi‐element composite materials and intricate processes. In this study, an end‐to‐end graph convolutional network (GCN) model is employed to optimize TFT production processes. By encoding process parameters as graph nodes and features, the GCN effectively predicts critical drain current–gate voltage (I–V) characteristics, enabling precise calculation of electron mobility, subthreshold slope, and threshold voltage. The model achieves a mean absolute percentage error of 19.9% on validation data. Based on the optimized parameters, a TFT device with properties including a mobility of 37.8 cm2/Vs, a threshold voltage of 0.9 V, and a subthreshold slope of 0.62 V/dec was successfully produced. These results highlight the potential of GCN‐based models to address the complexities of TFT mass production, providing a powerful tool for process optimization and performance enhancement.

Skeleton-based action recognition aims to project skeleton sequences to action categories, where skeleton sequences are derived from multiple forms of pre-detected points. Compared with earlier methods that focus on exploring single-form skeletons via Graph Convolutional Networks (GCNs), existing methods tend to improve GCNs by leveraging multi-form skeletons due to their complementary cues. However, these methods (either adapting structure of GCNs or model ensemble) require the co-existence of all forms of skeletons during both training and inference stages, while a typical situation in real life is the existence of only partial forms for inference. To tackle this issue, we present Adaptive Cross-Form Learning (ACFL), which empowers well-designed GCNs to generate complementary representation from single-form skeletons without changing model capacity. Specifically, each GCN model in ACFL not only learns action representation from the single-form skeletons, but also adaptively mimics useful representations derived from other forms of skeletons. In this way, each GCN can learn how to strengthen what has been learned, thus exploiting model potential and facilitating action recognition as well. Extensive experiments conducted on three challenging benchmarks, i.e., NTU-RGB+D 120, NTU-RGB+D 60 and UAV-Human, demonstrate the effectiveness and generalizability of the proposed method. Specifically, the ACFL significantly improves various GCN models (i.e., CTR-GCN, MS-G3D, and Shift-GCN), achieving a new record for skeleton-based action recognition.

Exploiting node-feature bipartite graph in graph convolutional networks

Entity linking, which maps named entity mentions in a document into the proper entities in a given knowledge graph, has been shown to be able to significantly benefit from modeling the entity relatedness through Graph Convolutional Networks (GCN). Nevertheless, existing GCN entity linking models fail to take into account the fact that the structured graph for a set of entities not only depends on the contextual information of the given document but also adaptively changes on different aggregation layers of the GCN, resulting in insufficiency in terms of capturing the structural information among entities. In this paper, we propose a dynamic GCN architecture to effectively cope with this challenge. The graph structure in our model is dynamically computed and modified during training. Through aggregating knowledge from dynamically linked nodes, our GCN model can collectively identify the entity mappings between the document and the knowledge graph, and efficiently capture the topical coherence among various entity mentions in the entire document. Empirical studies on benchmark entity linking data sets confirm the superior performance of our proposed strategy and the benefits of the dynamic graph structure.

Integrating vertex and edge features with Graph Convolutional Networks for skeleton-based action recognition

AimsThe aim of this study was to clarify the effect of β-blockers (BBs) on respiratory function and survival in patients with chronic obstructive pulmonary disease with cardiovascular disease (CVD), as well as the difference between the effects of cardioselective and noncardioselective BBs.Methods and resultsWe searched for relevant literature in four electronic databases, namely, PubMed, EMBASE, Cochrane Library, and Web of Science, and compared the differences in various survival indicators between patients with chronic obstructive pulmonary disease taking BBs and those not taking BBs. Forty-nine studies were included, with a total sample size of 670 594. Among these, 12 studies were randomized controlled trials (RCTs; seven crossover and five parallel RCTs) and 37 studies were observational (including four post hoc analyses of data from RCTs). The hazard ratios (HRs) of chronic obstructive pulmonary disease exacerbation between patients with chronic obstructive pulmonary disease who were not treated with BBs and those who were treated with BBs, cardioselective BBs, and noncardioselective BBs were 0.77 [95% confidence interval (CI) 0.67, 0.89], 0.72 [95% CI 0.56, 0.94], and 0.98 [95% CI 0.71, 1.34, respectively] (HRs <1 indicate favouring BB therapy). The HRs of all-cause mortality between patients with chronic obstructive pulmonary disease who were not treated with BBs and those who were treated with BBs, cardioselective BBs, and noncardioselective BBs were 0.70 [95% CI 0.59, 0.83], 0.60 [95% CI 0.48, 0.76], and 0.74 [95% CI 0.60, 0.90], respectively (HRs <1 indicate favouring BB therapy). Patients with Chronic obstructive pulmonary disease treated with cardioselective BBs showed no difference in ventilation effect after the use of an agonist, in comparison with placebo. The difference in mean change in forced expiratory volume in 1 s was 0.06 [95% CI −0.02, 0.14].ConclusionThe use of BBs in patients with chronic obstructive pulmonary disease is not only safe but also reduces their all-cause and in-hospital mortality. Cardioselective BBs may even reduce chronic obstructive pulmonary disease exacerbations. In addition, cardioselective BBs do not affect the action of bronchodilators. Importantly, BBs reduce the heart rate acceleration caused by bronchodilators. BBs should be prescribed freely when indicated in patients with chronic obstructive pulmonary disease and heart disease.

Open public places, such as pedestrian streets, parks, and squares, are vulnerable when the pedestrians thronged into the sidewalks. The crowd count changes dynamically over time with various external factors, such as surroundings, weekends, and peak hours, so it is essential to predict the accurate and timely crowd count. To address this issue, this study introduces graph convolutional network (GCN), a network-based model, to predict the crowd flow in a walking street. Compared with other grid-based methods, the model is capable of directly processing road network graphs. Experiments show the GCN model and its extension STGCN consistently and significantly outperform other five baseline models, namely HA, ARIMA, SVM, CNN and LSTM, in terms of RMSE, MAE and R2. Considering the computation efficiency, the standard GCN model was selected to predict the crowd. The results showed that the model obtains superior performances with higher prediction precision on weekends and peak hours, of which R2 are above 0.9, indicating the GCN model can capture the pedestrian features in the road network effectively, especially during the periods with massive crowds. The results will provide practical references for city managers to alleviate road congestion and help pedestrians make smarter planning and save travel time.

Graph Neural Networks (GNNs) have become a critical tool for learning on structured data, particularly for their ability to capture feature dependencies within multivariate datasets. This study investigates the application of Graph Convolutional Networks (GCNs) to improve classification performance on such data. We constructed a fully connected graph using cosine similarity to compare our GCN approach against standard dimensionality reduction techniques like PCA and t-SNE. In our tests, the GCN model clearly surpassed these baselines in accuracy, precision, recall, and F1-score.But the method has its own trade-offs: dense graphs are computationally costly, there are chances of overfitting on smaller datasets and the resulting embeddings may be uninterpretable. We cannot make such a claim of generality at this point because this review has only limited itself to one multivariate data set. In future endeavour we would wish to compare GCNs to more powerful classifiers, such as Random Forests, Deep Neural Networks, with greater regard to efficiency in training and resistance to noise. To summarize, this paper confirms that GNNs are an ideal option when it comes to multivariate classification, although scalability and interpretability have to be resolved in order to make it applicable in the real-life context.

Bronchodilatory effects of NVA237, a once daily long-acting muscarinic antagonist, in COPD patients

Exploring the impact of trip patterns on spatially aggregated crashes using floating vehicle trajectory data and graph Convolutional Networks

Text-oriented clinical diagnosis inference is to predict a set of diagnoses for a specific patient given its medical notes. Due to the great potential of automatic diagnosis inference, machine learning methods have began to be applied to this domain. However, existing approaches focus on performing either labor-intensive feature engineering or sequential modeling of each medical note separately, without considering the information sharing among similar patients, which is essential for evidence-based medicine, an emerging new diagnosis process. Motivated by this issue and the recently proposed graph convolutional network (GCN) for text classification, we propose to apply GCN for the text-oriented clinical diagnosis inference task. To encode the comorbidity of diagnoses into the GCN model and allow information sharing between patients, we devise a coupled graph convolutional neural networks (CGCN), where a note-dependent graph and a label-dependent graph are learned collaboratively with hyperplane projection to ensure they are in the same semantic space. The comprehensive results on two real datasets show that our method outperforms the state-of-art methods in text-oriented diagnosis inference.