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The near-surface temperature prediction (NTP) is an important spatial–temporal forecast problem, which can be used to prevent temperature crises. Most of the previous approaches fail to explicitly model the long- and short-range spatial correlations simultaneously, which is critical to making an accurate temperature prediction. In this study, both long- and short-range spatial correlations are captured to fill this gap by a novel convolution operator named Long- and Short-range Convolution (LS-Conv). The proposed LS-Conv operator includes three key components, namely, Node-based Spatial Attention (NSA), Long-range Adaptive Graph Constructor (LAGC), and Long- and Short-range Integrator (LSI). To capture long-range spatial correlations, NSA and LAGC are proposed to evaluate node importance aiming at auto-constructing long-range spatial correlations, which is named as Long-range aware Graph Convolution Network (LR-GCN). After that, the Short-range aware Convolution Neural Network (SR-CNN) accounts for the short-range spatial correlations. Finally, LSI is proposed to capture both long- and short-range spatial correlations by intra-unifying LR-GCN and SR-CNN. Upon the proposed LS-Conv operator, a new model called Long- and Short-range for NPT (LS-NTP) is developed. Extensive experiments are conducted on two real-world datasets and the results demonstrate that the proposed method outperforms state-of-the-art techniques. The source code is available on GitHub: https://github.com/xuguangning1218/LS_NTP. • We jointly incorporate long- and short-range spatial correlations in NTP prediction. • Theoretical findings proved that the proposed LS-Conv is a general version of CNN. • We develop a new spatial–temporal model named LS-NTP for the NTP task. • Extensive experiments are conducted on two real-world datasets. [ABSTRACT FROM AUTHOR]

Tropical Cyclone Intensity Estimation (TIE) is a fundamental study subject for tropical cyclone development, flood or landslide avoidance, etc. Despite considerable efforts, two main challenges remain unresolved in this critical endeavor. The first challenge is that the TIE task is frequently conducted as a coarse-grained recognition problem rather than a fine-grained one. The second challenge is that the prediction fails to consider general wind speed information. To conquer these two challenges, we offer a novel model, namely Tropical cyclone intensity estimation from a Fine-grained perspective with the Graph convolution neural Network (TFG-Net). It is composed of three key components, viz., the Backbone, the Fine-grained Tropical cyclone Features Extractor (FTFE), and the Wind Scale Transition Rule Generator (WTRG), which aim at extracting general spatial features, subtle spatial features, and general wind speed information, respectively. To validate the proposed method, extensive experiments on a well-known real-world tropical dataset named GridSat were carried out. Following the standard benchmark task setting that the model estimates the wind speed from a given satellite image, the proposed TFG-Net reaches 11.12 knots in the RMSE metric, which outperforms 33.33%, 2.54% to the traditional method and the state-of-the-art deep learning method, respectively. The code is available on GitHub: https://github.com/xuguangning1218/TI%5fEstimation and its reproductive result is available on Code Ocean: https://doi.org/10.24433/CO.6606867.v1. • We first proposed to conduct cyclone intensity estimated in fine-grained perspective. • We propose to divide predictive wind speed into general part and particular part. • Extensive experiments and analysis are conducted on the real-world dataset. [ABSTRACT FROM AUTHOR]

The class of multi-relational graph convolutional networks (MRGCNs) is a recent extension of standard graph convolutional networks (GCNs) to handle heterogenous graphs with multiple types of relationships. MRGCNs have been shown to yield results superior than traditional GCNs in various machine learning tasks. The key idea is to introduce a new kind of convolution operated on tensors that can effectively exploit correlations exhibited in multiple relationships. The main objective of this paper is to analyze the algorithmic stability and generalization guarantees of MRGCNs to confirm the usefulness of MRGCNs. Our contributions are of three folds. First, we develop a matrix representation of various tensor operations underneath MRGCNs to simplify the analysis significantly. Next, we prove the uniform stability of MRGCNs and deduce the convergence of the generalization gap to support the usefulness of MRGCNs. The analysis sheds lights on the design of MRGCNs, for instance, how the data should be scaled to achieve the uniform stability of the learning process. Finally, we provide experimental results to demonstrate the stability results. [ABSTRACT FROM AUTHOR]