7,723 research outputs found
Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting
Spatio-temporal sequence forecasting is one of the fundamental tasks in
spatio-temporal data mining. It facilitates many real world applications such
as precipitation nowcasting, citywide crowd flow prediction and air pollution
forecasting. Recently, a few Seq2Seq based approaches have been proposed, but
one of the drawbacks of Seq2Seq models is that, small errors can accumulate
quickly along the generated sequence at the inference stage due to the
different distributions of training and inference phase. That is because
Seq2Seq models minimise single step errors only during training, however the
entire sequence has to be generated during the inference phase which generates
a discrepancy between training and inference. In this work, we propose a novel
curriculum learning based strategy named Temporal Progressive Growing Sampling
to effectively bridge the gap between training and inference for
spatio-temporal sequence forecasting, by transforming the training process from
a fully-supervised manner which utilises all available previous ground-truth
values to a less-supervised manner which replaces some of the ground-truth
context with generated predictions. To do that we sample the target sequence
from midway outputs from intermediate models trained with bigger timescales
through a carefully designed decaying strategy. Experimental results
demonstrate that our proposed method better models long term dependencies and
outperforms baseline approaches on two competitive datasets.Comment: ECAI 2020 Accepted, preprin
Predictive spatio-temporal modelling with neural networks
Hongbin Liu studied the predictive spatio-temporal modelling using Neural Networks. Predictive spatio-temporal modelling is a challenge task due to the complex non-linear spatio-temporal dependencies, data sparsity and uncertainty.
Hongbin Liu investigated the modelling difficulties and proposed three novel models to tackle the difficulties for three common spatio-temporal datasets. He also conducted extensive experiments on several real-world datasets for various spatio-temporal prediction tasks, such as travel mode classification, next-location prediction, weather forecasting and meteorological imagery prediction. The results show our proposed models consistently achieve exceptional improvements over state-of-the-art baselines
Strong Cosmic Censorship in Charged de Sitter spacetime with Scalar Field Non-minimally Coupled to Curvature
We examine the stability and the strong cosmic censorship in the
Reissner-Nordstrom-de Sitter (RN-dS) black hole by investigating the evolution
of a scalar field non-minimally coupled to the curvature. We find that when the
coupling parameter is negative, the RN-dS black hole experiences instability.
The instability disappears when the coupling parameter becomes non-negative.
With the increase of the coupling parameter, the violation of the strong cosmic
censorship occurs at a larger critical charge ratio. But such an increase of
the critical charge is suppressed by the increase of the cosmological constant.
Different from the minimal coupling situation, it is possible to accommodate
in the near extremal black hole when the scalar field is
non-minimally coupled to curvature. The increase of the cosmological constant
can allow to be satisfied for even smaller value of the coupling
parameter. The existence of implies that the resulting curvature
can continuously cross the Cauchy horizon.Comment: 14 pages, 4 figures, 5 table
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