A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction

In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spatial and temporal factors and other external factors such as weather. To address this problem, we propose a Spatio-TEmporal Fuzzy neural Network (STEF-Net) to accurately predict passenger demands incorporating the complex interactions of all known important factors. We design an end-to-end learning framework with different neural networks modeling different factors. Specifically, we propose to capture spatio-temporal feature interactions via a convolutional long short-term memory network and model external factors via a fuzzy neural network that handles data uncertainty significantly better than deterministic methods. To keep the temporal relations when fusing two networks and emphasize discriminative spatio-temporal feature interactions, we employ a novel feature fusion method with a convolution operation and an attention layer. As far as we know, our work is the first to fuse a deep recurrent neural network and a fuzzy neural network to model complex spatial-temporal feature interactions with additional uncertain input features for predictive learning. Experiments on a large-scale real-world dataset show that our model achieves more than 10% improvement over the state-of-the-art approaches.Comment: https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.1

Squeezed light from multi-level closed-cycling atomic systems

Amplitude squeezing is calculated for multi-level closed-cycling atomic systems. These systems can last without atomic population inversion in any atomic bases. Maximum squeezing is obtained for the parameters in the region of lasing without inversion. A practical four-level system and an ideal three-level system are presented. The latter system is analyzed in some detail and the mechanism of generating amplitude squeezing is discussed

Bis(μ-4-hydroxy­benzoato-κ2 O:O′)bis­[triaqua­bis(4-hydroxy­benzoato)-κO;κ2 O,O′-terbium(III)] deca­hydrate

The title dinuclear compound, [Tb2(C7H5O3)6(H2O)6]·10H2O, lies on a center of inversion and the two TbIII atoms are bridged by two 4-hydroxy­benzoate anions; each metal atom is further coordinated by one monodentate anion and chelated by the third anion. The eight-coordinate geometry approximates a square anti­prism. Hydrogen bonds of the O—H⋯O type connect the uncoordinated water mol­ecules to the dinuclear species, forming a three-dimensional network