Knowing how much people travel is essential for transport planning. Empirical mobility traces collected from call detail records (CDRs), location-based social networks (LBSNs), and media data have been used widely to study patterns. However, these suffer sparsity, an issue that has largely overlooked. In order extend the use of low-cost accessible data, this proposes a model fills gaps in spars...

We introduce a new synthetic marine model for 3D controlled-source electromagnetic method (CSEM) surveys. The proposed includes relevant features the geophysical community such as large conductivity contrast with vertical transverse isotropy and complex bathymetry profile. In this paper, we present experimental setup several CSEM simulations in presence of resistivity unit denoting hydrocarbon ...

Accurate photovoltaic (PV) forecasting is quite crucial in planning and the regular operation of power system. Stochastic habit along with high risks PV signal uncertainty a probabilistic model required to address numerical weather prediction (NWP) underdispersion. In this study, new synthetic process based on Bayesian averaging (BMA) Ensemble Learning developed. The proposed initiated by impro...

Correctly choosing the number of topics plays an important role in successfully applying topic models to real world applications. Following the latest tensor decomposition framework by Anandkumar et al., we make the first attempt to provide theoretical analysis on the number of topics under Latent Dirichlet Allocation model. With mild conditions, our method provides accessible information on th...