Abstract: The current disclosure provides reinforced aerogel compositions that are durable and easy to handle, have favorable performance in aqueous environments, have favorable insulation properties, and have favorable, reaction to fire, combustion and flame-resistance properties. Also provided are methods of preparing or manufacturing such reinforced aerogel compositions. In certain embodiments, the composition has a silica-based aerogel framework, reinforced with an open-cell macroporous framework, and includes one or more fire-class additives, where the silica-based aerogel framework comprises at least one hydrophobic-bound silicon and the composition or each of its components has desired properties.

Abstract: In an example embodiment, a deep learning model is used to learn embedding representations of a heterogeneous information network, where the embedding represents entity-specific properties and network environment properties. Position-aware embeddings specific to the heterogeneous information network may be used as input features of the deep learning model. Furthermore, meta-path embedding specific to the heterogeneous information network may also be used as input features of the deep learning model. Modified embedding propagation methods are further designed to explore better ways to capture network meta-path properties.

Abstract: In an example embodiment, a deep learning model is used to learn embedding representations of a heterogeneous information network, where the embedding represents entity-specific properties and network environment properties. Position-aware embeddings specific to the heterogeneous information network may be used as input features of the deep learning model. Furthermore, meta-path embedding specific to the heterogeneous information network may also be used as input features of the deep learning model. Modified embedding propagation methods are further designed to explore better ways to capture network meta-path properties.