Abstract: A process of sequestering CO2 is generally described. The process involves the use of geopolymeric precursors to which the CO2 is added. The process for a solid, cementitious material comprising geopolymer(s) and CO2.

Abstract: A technology is presented for controlling the distribution of a data item. A data set is stored at a data storage (16) and comprises a first file identifier and a first encrypted data item generated by an encryption using a first public key. A blockchain comprises the first file identifier paired with a first recipient identifier identifying one or more allowed first recipients, each having the first recipient identifier and a first private key matching the first public key. The method is performed by a second terminal (12) being an allowed first recipient and the method comprises: identifying (102) the first file identifier in the blockchain using the first recipient identifier, sending (106) a request containing the first file identifier to the data storage (16) for downloading of the first encrypted data item, receiving (116) the first encrypted data item from the data storage (16), and decrypting (118) the first encrypted data item using the first private key.

Abstract: The present disclosure relates to a method and attention neural network for automatically learning embeddings for various latent aspects of textual claims and documents performed in an attention neural network comprising one or more latent aspect models for guiding an attention mechanism of the neural network, wherein the method comprises the steps of inserting a claim document pair, in each of the latent aspect models and a latent aspect vector to select significant sentences to form document representations for each respective latent aspect of the latent aspect vector, concatenating the document representations to establish an overall document representation, calculating a class probability distribution by means of the overall document representation, and classifying the claim of document as true or false using the class probability distribution.