GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels

Evaluating the performance of graph neural networks (GNNs) is an essential task for practical GNN model deployment and serving, as deployed GNNs face significant performance uncertainty when inferring on unseen and unlabeled test graphs, due to mismatched training-test graph distributions. In this paper, we study a new problem, GNN model evaluation, that aims to assess the performance of a specific GNN model trained on labeled and observed graphs, by precisely estimating its performance (e.g., node classification accuracy) on unseen graphs without labels. Concretely, we propose a two-stage GNN model evaluation framework, including (1) DiscGraph set construction and (2) GNNEvaluator training and inference. The DiscGraph set captures wide-range and diverse graph data distribution discrepancies through a discrepancy measurement function, which exploits the outputs of GNNs related to latent node embeddings and node class predictions. Under the effective training supervision from the DiscGraph set, GNNEvaluator learns to precisely estimate node classification accuracy of the to-be-evaluated GNN model and makes an accurate inference for evaluating GNN model performance. Extensive experiments on real-world unseen and unlabeled test graphs demonstrate the effectiveness of our proposed method for GNN model evaluation.Comment: Accepted by NeurIPS 202

Classification and mapping of urban canyon geometry using Google Street View images and deep multitask learning

Urban canyon classification plays an important role in analyzing the impact of urban canyon geometry on urban morphology and microclimates. Existing classification methods using aspect ratios require a large number of field surveys, which are often expensive and laborious. Moreover, it is difficult for these methods to handle the complex geometry of street canyons, which is often required by specific applications. To overcome these difficulties, we develop a street canyon classification approach using publicly available Google Street View (GSV) images. Our method is inspired by the latest advances in deep multitask learning based on densely connected convolutional networks (DenseNets) and tailored for multiple street canyon classification, i.e., H/W-based (Level 1), symmetry-based (Level 2), and complex-geometry-based (Level 3) classifications. We conducted a series of experiments to verify the proposed method. First, taking the Hong Kong area as an example, the method achieved an accuracy of 89.3%, 86.6%, and 86.1%, respectively for the three levels. Even using the field survey data as the ground truth, it gained approximately 80% for different levels. Then, we tested our pretrained model in five other cities and compared the results with traditional methods. The transferability and effectiveness of the scheme were demonstrated. Finally, to enrich the representation of more complicated street geometry, the approach can separately generate thematic maps of street canyons at multiple levels to better facilitate microclimatic studies in high-density built environments. The developed techniques for the classification and mapping of street canyons provide a cost-effective tool for studying the impact of complex and evolving urban canyon geometry on microclimate changes

Neuroprotective effect of paeonol against isofluraneinduced neuroapoptosis and cognitive dysfunction

Purpose: To investigate whether paeonol affords neuroprotection against isoflurane-induced neurotoxicity.Methods: Separate groups of neonatal rat pups were administered paeonol (20, 40 or 80 mg/kg) from post-natal day 3 (P3) to post-natal day 15. On post-natal day 7, the pups were exposed to 6 h of isoflurane (0.75 %) anesthesia. TUNEL assay was performed to assess neuroapoptosis. Cleaved caspase-3 expressions were evaluated by immunohistochemistry and western blotting analysis. The expressions of apoptotic pathway proteins and mitogen activated protein kinases (MAPKs) were assessed by western blotting. The general behaviour of the rats was determined by open field test and elevated maze test. Y-maze test and Morris water maze tests were performed to evaluate working memory and cognition.Results: Isoflurane exposure caused (p < 0.05) severe neuronal apoptosis in the hippocampal region and enhanced caspase-3 expressions. Paeonol supplementation remarkably (p < 0.05) reduced neuronal apoptosis and modulated expressions of apoptotic proteins. The raised expressions of NF-κB, TNF-α, IL-6 and IL-1β and significantly (p < 0.05) enhanced JNK/p38 signalling cascades were inhibited by paeonol. The expression levels of ERK were not significantly (p < 0.05) changed, but there was significant improvement in the general behaviour and working memory of the rats.Conclusion: Paeonol significantly improves cognitive impairments and offers neuroprotection against isoflurane-induced apoptosis via modulating JNK/ERK/p38 MAPK and NF-κB signaling pathways.Keywords: Apoptosis, Isoflurane, Neurodegeneration, Paeonol, Cognitive impairment, Signaling pathway

Microfluidic spinning of topographical hollow fibers for the development of a 3D functional glomerulus in vitro

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Application of Hydrodynamic Model for Sedimentary Management in Alishan River

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive