Abstract: A method for treating an oil-containing medium including oil or grease or both oil and grease includes the steps of combining algae with an oil-containing medium and allowing the algae to engulf or uptake at least a portion of the oil or grease in the oil-containing medium. A mixture for treating oil or grease or both oil and grease includes an oil-containing medium including oil or grease or both oil and grease and phagotrophic algae that engulf or uptake at least a portion of the oil or grease in the oil-containing medium.

Abstract: The disclosure relates in some cases to a technology for selecting one or more promotions to be presented to online customers using Bayesian bandits and affinity-based dynamic user clustering In some embodiments, a computer-implemented method determines a set of offers is determined, and computes affinity scores measuring affinities of users to items included in the offers. The method builds an affinity score distribution for the offers and identifies clusters of affinity scores for the offers using the corresponding affinity score distribution.

Abstract: A computer-implemented method and system are described for learning a vector representation for unique identification codes. An example method may include generating a unique identification code list using one or more virtual interaction contexts, the unique identification code list being a list of unique identification codes, selecting a target unique identification code in the unique identification code list, and determining, from the unique identification code list, an input set of unique identification codes using the target unique identification code, the input set including the target unique identification code and one or more context unique identification codes. Some implementations may further include inputting the input set of unique identification codes into a semantic neural network model, the semantic neural network model including one or more weight matrices, and modifying the one or more weight matrices using the input set of unique identification codes.

Abstract: A computer-implemented method and system are described for learning a vector representation for unique identification codes. An example method may include generating a unique identification code list using one or more virtual interaction contexts, the unique identification code list being a list of unique identification codes, selecting a target unique identification code in the unique identification code list, and determining, from the unique identification code list, an input set of unique identification codes using the target unique identification code, the input set including the target unique identification code and one or more context unique identification codes. Some implementations may further include inputting the input set of unique identification codes into a semantic neural network model, the semantic neural network model including one or more weight matrices, and modifying the one or more weight matrices using the input set of unique identification codes.

Abstract: The disclosure relates in some cases to a technology for selecting one or more promotions to be presented to online customers using Bayesian bandits and affinity-based dynamic user clustering In some embodiments, a computer-implemented method determines a set of offers is determined, and computes affinity scores measuring affinities of users to items included in the offers. The method builds an affinity score distribution for the offers and identifies clusters of affinity scores for the offers using the corresponding affinity score distribution.

Abstract: A method for reducing the free fatty acid content of a feedstock includes the steps of providing a free-fatty-acid-containing feedstock, treating the free-fatty-acid-containing feedstock to reduce the free fatty acid content thereof, where the step of treating includes combining at least one of an algae and a coagulant to the free-fatty-acid-containing feedstock, and producing a product from the treated feedstock.