Abstract: Techniques regarding the synthesis of one or more polymers of a target polymer class are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a recommendation component that can generate a recommended chemical reactor control setting for inverse synthesis of a polymer based on a target polymer characteristic and reactor training data.

Abstract: A method is described that utilizes non-negative matrix factorization to predict susceptibility of a microorganism to an antimicrobial drug. A sparse adjacency matrix is constructed from existing ground truth datasets that include antibiogram data and other data associated with microorganisms. The rows of the adjacency matrix correspond to biosamples, and the columns correspond to instances of metadata and drugs associated with one or more of the biosamples. The elements of the adjacency matrix are assigned non-zero numerical values or zero depending on whether a known association exists. The adjacency matrix is then factored using a selected number of latent factors, thereby producing a reconstruction matrix approximating the adjacency matrix. The values of the reconstruction matrix are used to predict antimicrobial susceptibility of a biosample ID to a drug when antibiogram data are lacking.

Abstract: This disclosure generally covers systems and methods that determine demographic labels for a user or a group of users by using digital inputs within a predictive model for demographic classification. In particular, the disclosed systems and methods use a unique combination of classification algorithms to determine demographic labels for users as a potential audience of digital content items. When applying the combination of classification algorithms, the disclosed systems and methods use a first classification algorithm to determine user-level-latent features for each user within a group of users based on demographic-label statistics associated with particular digital content items. The disclosed systems and methods then use the user-level-latent features and session-level features (from sessions of each user consuming the digital content items) as inputs in a second classification algorithm to determine a demographic label for each user within the group of users.

Abstract: Techniques regarding the synthesis of one or more polymers of a target polymer class are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a recommendation component that can generate a recommended chemical reactor control setting for inverse synthesis of a polymer based on a target polymer characteristic and reactor training data.

Abstract: A method is described that utilizes non-negative matrix factorization to predict susceptibility of a microorganism to an antimicrobial drug. A sparse adjacency matrix is constructed from existing ground truth datasets that include antibiogram data and other data associated with microorganisms. The rows of the adjacency matrix correspond to biosamples, and the columns correspond to instances of metadata and drugs associated with one or more of the biosamples. The elements of the adjacency matrix are assigned non-zero numerical values or zero depending on whether a known association exists. The adjacency matrix is then factored using a selected number of latent factors, thereby producing a reconstruction matrix approximating the adjacency matrix. The values of the reconstruction matrix are used to predict antimicrobial susceptibility of a biosample ID to a drug when antibiogram data are lacking.

Abstract: This disclosure generally covers systems and methods that determine demographic labels for a user or a group of users by using digital inputs within a predictive model for demographic classification. In particular, the disclosed systems and methods use a unique combination of classification algorithms to determine demographic labels for users as a potential audience of digital content items. When applying the combination of classification algorithms, the disclosed systems and methods use a first classification algorithm to determine user-level-latent features for each user within a group of users based on demographic-label statistics associated with particular digital content items. The disclosed systems and methods then use the user-level-latent features and session-level features (from sessions of each user consuming the digital content items) as inputs in a second classification algorithm to determine a demographic label for each user within the group of users.