Abstract: An MOFs/MIPs catalyst, an in situ growth preparation method for same, and applications thereof are provided. The method comprises: uniformly mixing template molecules, a functional monomer, and a pore-foaming agent and performing a prepolymerization to produce a prepolymerization reaction product; uniformly mixing a cross-linking agent, an initiator, and the prepolymerization reaction product, heating, eluting the template molecules via a Soxhlet extraction, and drying to produce an imprinted polymer; uniformly mixing dimethylformamide, 2,5-dihydroxyterephthalic acid, ferrous chloride, water, methanol, and the imprinted polymer, heating, washing, using methanol for immersion and washing, and drying to produce the MOFs/MIPs catalyst.

Abstract: Hierarchical branching deep convolutional neural networks (HD-CNNs) improve existing convolutional neural network (CNN) technology. In a HD-CNN, classes that can be easily distinguished are classified in a higher layer coarse category CNN, while the most difficult classifications are done on lower layer fine category CNNs. Multinomial logistic loss and a novel temporal sparsity penalty may be used in HD-CNN training. The use of multinomial logistic loss and a temporal sparsity penalty causes each branching component to deal with distinct subsets of categories.

Abstract: A method of displaying a navigation map includes determining a route of a vehicle. A two-dimensional footprint of a building disposed within a geographic area is received within the vehicle. The geographic area includes the route of the vehicle. Attribute information associated with the building is received. The attribute information is received within the vehicle. A visual representation of the building is rendered based upon the two-dimensional footprint and the attribute information.

Abstract: Hierarchical branching deep convolutional neural networks (HD-CNNs) improve existing convolutional neural network (CNN) technology. In a HD-CNN, classes that can be easily distinguished are classified in a higher layer coarse category CNN, while the most difficult classifications are done on lower layer fine category CNNs. Multinomial logistic loss and a novel temporal sparsity penalty may be used in HD-CNN training. The use of multinomial logistic loss and a temporal sparsity penalty causes each branching component to deal with distinct subsets of categories.

Abstract: A method of displaying a navigation map includes determining a route of a vehicle. A two-dimensional footprint of a building disposed within a geographic area is received within the vehicle. The geographic area includes the route of the vehicle. Attribute information associated with the building is received. The attribute information is received within the vehicle. A visual representation of the building is rendered based upon the two-dimensional footprint and the attribute information.

Abstract: A method of displaying a navigation map includes determining a route of a vehicle. A two-dimensional footprint of a building disposed within a geographic area is received within the vehicle. The geographic area includes the route of the vehicle. Attribute information associated with the building is received. The attribute information is received within the vehicle. A visual representation of the building is rendered based upon the two-dimensional footprint and the attribute information.

Abstract: A method of displaying a navigation map includes determining a route of a vehicle. A two-dimensional footprint of a building disposed within a geographic area is received within the vehicle. The geographic area includes the route of the vehicle. Attribute information associated with the building is received. The attribute information is received within the vehicle. A visual representation of the building is rendered based upon the two-dimensional footprint and the attribute information.