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Showing 1 to 8 of 8 for “"spatio-temporal correlations"”.

  1. Learning Spatio-Temporal Correlations from Dynamic and Sparse Data

    The amount of spatio-temporal measurements around Earth, its atmosphere, in human-made urban settings, and in its oceans increases with affordable and smaller sensor technologies. Improved communication technologies allow sensors to be not geostationary, changing their positions and measurement …

    cau-kiel Repository record for Learning Spatio-Temporal Correlations from Dynamic and Sparse Data (opens in a new tab)

  2. Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting

    There is a recent surge in the development of spatio-temporal forecasting models in many applications, and traffic forecasting is one of the most important ones. Long-range traffic forecasting, however, remains a challenging task due to the intricate and extensive spatio-temporal correlations

    vt Repository record for Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting (opens in a new tab)

  3. Advanced space-time integration for knowledge discovery in human mobility studies

    … With the fast-growing volume of and interest in spatio-temporal mobility data, there is also an increasing need for new methods of analyzing this kind of data. Particularly, considerable effort has been made to characterize human activity-travel patterns from the spatio-temporal mobility data. …

    uiuc Repository record for Advanced space-time integration for knowledge discovery in human mobility studies (opens in a new tab)

  4. Modeling the mechanical behavior of amorphous metals by shear transformation zone dynamics

    … STZ activations elucidates some important spatio-temporal correlations which are shown to be the cause of the different macroscopic modes of deformation. The value of the mesoscale modeling framework is also shown in two specific applications to investigate phenomena observed in amorphous …

    mit Repository record for Modeling the mechanical behavior of amorphous metals by shear transformation zone dynamics (opens in a new tab)

  5. The spectral characteristics of wind-farm power output

    … holistic, physics-based approach to modeling the spatio-temporal structures of the atmospheric boundary layer, and the ways in which these structures impart themselves in wind-power variability. The following primary findings are presented. Field and laboratory experiments were performed to unravel …

    uiuc Repository record for The spectral characteristics of wind-farm power output (opens in a new tab)

  6. Statistical analysis and prediction of climate impacts on sugarcane yield in southeastern Africa

    … yield and climate parameters. Moreover, the spatio-temporal correlations and regressions were performed between the sugarcane yield index and relevant local crop drivers such as rainfall and temperature, and the global sea surface temperatures and winds through online tools. The results …

    zulu Repository record for Statistical analysis and prediction of climate impacts on sugarcane yield in southeastern Africa (opens in a new tab)

  7. Neural parameter inference for large-scale multi-agent systems

    … human migration since 1990. Both exhibit complex spatio-temporal correlations that traditional models struggle to capture. In the trade study, a deep neural network is used to fit an optimal transport model to bilateral trade flows, significantly improving upon the accuracy and flexibility of …

    cambridge Repository record for Neural parameter inference for large-scale multi-agent systems (opens in a new tab)

  8. Energy Efficient Deep Spiking Recurrent Neural Networks: A Reservoir Computing-Based Approach

    … pattern recognition and exploring the underlying spatio-temporal correlation. However, due to the vanishing/exploding gradient problem, training a fully connected RNN in many cases is very difficult or even impossible. The difficulties of training traditional RNNs, led us to reservoir computing …

    vt Repository record for Energy Efficient Deep Spiking Recurrent Neural Networks: A Reservoir Computing-Based Approach (opens in a new tab)