Spatio-Temporal combines spatial and temporal dimensions, indicating a consideration of both space and time in the analysis or representation of data. It’s commonly used in fields such as geography, physics, computer vision, and more, where understanding both the spatial and temporal aspects is crucial.

Posts

T2-Net: A Semi-supervised Deep Model for Turbulence Forecasting

/in /by

Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs. Traditional turbulence forecasting approaches heavily rely on painstakingly customized turbulence indexes, which are less effective in dynamic and complex weather conditions. The recent availability of high-resolution weather data and turbulence records allows more accurate forecasting of the turbulence in a data-driven way. However, it is a non-trivial task for developing a machine learning based turbulence forecasting system due to two challenges: (1) Complex spatio-temporal correlations, turbulence is caused by air movement with complex spatio-temporal patterns, (2) Label scarcity, very limited turbulence labels can be obtained. To this end, in this paper, we develop a unified semi-supervised framework, T2-Net, to address the above challenges. Specifically, we first build an encoder-decoder paradigm based on the convolutional LSTM to model the spatio-temporal correlations. Then, to tackle the label scarcity problem, we propose a novel Dual Label Guessing method to take advantage of massive unlabeled turbulence data. It integrates complementary signals from the main Turbulence Forecasting task and the auxiliary Turbulence Detection task to generate pseudo-labels, which are dynamically utilized as additional training data. Finally, extensive experimental results on a real-world turbulence dataset validate the superiority of our method on turbulence forecasting.

Read more

Adaptive Neural Network for Node Classification in Dynamic Networks

/in /by

Given a network with the labels for a subset of nodes, transductive node classification targets to predict the labels for the remaining nodes in the network. This technique has been used in a variety of applications such as voxel functionality detection in brain network and group label prediction in social network. Most existing node classification approaches are performed in static networks. However, many real-world networks are dynamic and evolve over time. The dynamics of both node attributes and network topology jointly determine the node labels. In this paper, we study the problem of classifying the nodes in dynamic networks. The task is challenging for three reasons. First, it is hard to effectively learn the spatial and temporal information simultaneously. Second, the network evolution is complex. The evolving patterns lie in both node attributes and network topology. Third, for different networks or even different nodes in the same network, the node attributes, the neighborhood node representations and the network topology usually affect the node labels differently, it is desirable to assess the relative importance of different factors over evolutionary time scales. To address the challenges, we propose AdaNN, an adaptive neural network for transductive node classification. AdaNN learns node attribute information by aggregating the node and its neighbors, and extracts network topology information with a random walk strategy. The attribute information and topology information are further fed into two connected gated recurrent units to learn the spatio-temporal contextual information. Additionally, a triple attention module is designed to automatically model the different factors that influence the node representations. AdaNN is the first node classification model that is adaptive to different kinds of dynamic networks. Extensive experiments on real datasets demonstrate the effectiveness of AdaNN.

Read more