The present teaching relates to managing online experiments. In one example, a plurality of experiment layers is created with respect to a plurality of online users. Each experiment layer includes at least one experiment each of which includes one or more buckets associated with respective features to be experimented on. Each of the plurality of online users is assigned to a corresponding bucket in each experiment layer, such that the user is simultaneously associated with multiple experiments in different layers. User event data related to the plurality of experiment layers are collected from the plurality of online users. One or more contaminated buckets are automatically detected based on the user event data.

In some embodiments, a method receives a line item factor for a line item at a first model. The method receives a competition factor at a second model. The first model generates a first value for a representation parameter based on the line item factor. The representation parameter is used to generate a representation for estimating selection of the instance of supplemental content for the line item for delivery. The second model generates a modification factor based on the competition factor. The modification factor is configured to modify the first value for the representation parameter based on competition from other line items to select the instance of supplemental content for the line item for delivery. The method modifies the first value for the representation parameter using the modification factor to generate a second value for the representation parameter where the second value is used to generate the representation.

An example apparatus includes processor circuitry to: access first input data from meters, the meters to monitor media devices associated with a plurality of panelists, the first input data including media source data and panel data; reduce a dimensionality of the first input data to generate second input data of reduced dimensionality relative to the first input data, the dimensionality of the first input data to be reduced based on a prior probability of an audience rating associated with the plurality of panelists and an approximation of a dependency of the audience rating on at least one of the media source data and the panel data; and decode the second input data of reduced dimensionality to output a probability model parameter for a multivariate probability model, the multivariate probability model having dimensions corresponding to the first input data, the multivariate probability model to label census data.

Systems and methods for forward market purchase of attention resources are disclosed. An example transaction-enabling system may include a fleet of machines and a controller. The controller may include an artificial intelligence (AI) circuit to aggregate data for a plurality of attention-related resources, wherein each one of the plurality of attention-related resources are related to a task of the fleet of machines, an expert system circuit to configure a purchase of at least one of the plurality of attention-related resources on an attention market, and an attention acquisition circuit to automatically solicit the configured purchase of the at least one of the plurality of attention-related resources in a forward market for the at least one of the plurality of attention-related resources.

Described is a system for the placement of digital content items on a digital content item space for a point of care (POC) facility by identifying a display interface for a point of care (POC) facility, the display interface including a digital content item space to display a digital content item, identifying one or more digital content item providers for the digital content item space; accessing a selection of the one or more digital content item providers, and identifying a set of digital content item providers for the digital content item space based on the selection. The system then causes display of the digital content item on the display interface based on the identified set of digital content item providers.

Systems and methods for forward market purchase of machine resources using artificial intelligence are disclosed. An example transaction-enabling system may include a fleet of machines, each one of the fleet of machines having a resource requirement comprising at least one of a plurality of machine-related resources. The system may further include a controller including an artificial intelligence (AI) circuit to aggregate data for the plurality of machine-related resources from at least one data source comprising an external data source or an internal data source; an expert system circuit to configure a purchase of at least one of the plurality of machine-related resources; and a machine resource acquisition circuit to automatically solicit the configured purchase of the at least one of the plurality of machine-related resources in a forward market for at least one resource of the plurality of machine-related resources.

Transaction-enabled methods for providing provable access to a distributed ledger with a tokenized instruction set for polymer production processes are described. A method may include accessing a distributed ledger comprising an instruction set for a polymer production process and tokenizing the instruction set. The method may further include interpreting an instruction set access request and providing provable access to the instruction set. The method may further include providing commands to a production tool of the polymer production process and recording the transaction on the distributed ledger.

The present disclosure provides a detailed description of techniques used in methods, systems, and computer program products for associating anonymous information with personally identifiable information without sharing any personally identifiable information. A method receives a specification record comprising one or more specified demographic attributes to be used in user record selection operations, the results of which operations include user records that comprise a user identifier and at least some non-personally-identifiable information. A candidate group is formed by applying a set of rules over the retrieved user records to selectively exclude one or more user records that comprise mutually-exclusive characteristics with respect to the other user records in the candidate group. An anonymity measure is calculated over the candidate group to satisfy a threshold of anonymity. If needed to satisfy the threshold of anonymity, additional user records are added to the group before any sharing operations. Anonymity of the users is preserved.

Systems and methods for text and audio-based real-time face reenactment are provided. An example method includes receiving a target video that includes a target face, receiving a source video that includes a source face, determining, based on a parametric face model, facial expression parameters of the source face, modifying, in real time, the target face to imitate a face expression of the source face based on the facial expression parameters to generate a sequence of modified video frames, and displaying at least part of the sequence of modified video frames on a computing device during the generation of at least one frame of the sequence of modified video frames.

Systems and methods for training a predictive model for suggesting content for users. In particular, a computer device may receive a first set of data, the first set of data indicative of one or more user engagement metrics, generate a predictive model based on the first set of data using a regularized loss algorithm, the regularized loss algorithm including a loss function with a regularization term, the regularization term being a normalized logit loss to adjust the loss function, receive a second set of data, generate an output using the predictive model based on the second set of data, evaluate the output of the predictive model by determining evaluation metrics indicative of predicted performance and reliability of the predictive model, adjust strength of regularization of the regularized loss algorithm based on the evaluation metrics, and train the predictive model using the regularized loss algorithm.