Abstract: Systems and methods for generating synthetic medical data are provided. A method may include retrieving a set of authentic electronic medical records from a database. The method may further include converting the authentic set of electronic medical records to a set of numerical vectors. The method may further include training a first neural network based on a random noise generator sample, the first neural network outputting synthetic electronic medical records. The method may further include training a second neural network based on the output synthetic electronic medical records and the set of numerical vectors, the second neural network outputting a loss distribution indicating whether the output synthetic electronic medical records are classified as authentic or synthetic.

Abstract: An improved system and method for scheduling online keyword auctions over multiple time periods subject to budget constraints is provided. A linear programming model of slates of advertisements may be created for predicting the volume and order in which queries may appear throughout multiple time periods for use in allocating bidders to auctions to optimize revenue of an auctioneer. Each slate of advertisements may represent a candidate set of advertisements in order of optimal revenue to an auctioneer. Linear programming using column generation with the keyword as a constraint and a bidder's budget as a constraint may be applied for each time period to generate a column that may be added to a linear programming model of slates of advertisements. Upon receiving a query request, a slate of advertisements for the time period may be output for sending to a web browser for display.

Abstract: An improved system and method for scheduling online keyword auctions subject to budget constraints is provided. A linear programming model of slates of advertisements may be created for predicting the volume and order in which queries may appear throughout the day for use in allocating bidders to auctions to optimize revenue of an auctioneer. Each slate of advertisements may represent a candidate set of advertisements in order of optimal revenue to an auctioneer. Linear programming using column generation with the keyword as a constraint and a bidder's budget as a constraint may be applied to generate a column that may be added to a linear programming model of slates of advertisements to determine optimal revenue to an auctioneer. Upon receiving a query request, a slate of advertisements that may provide optimal revenue to the auctioneer may be output for sending to a web browser for display.

Abstract: Methods and apparatus for selecting advertisements to display to a user requesting a primary webpage is provided. Keywords related to the primary webpage are determined using internal information of the primary webpage and/or external information provided in neighboring webpages. The external information may include anchor text metadata of hyperlinks on neighboring webpages that link to the primary webpage or include the number of such hyperlinks having a same particular anchor text. Other internal and/or external information may be used to determine a list of keywords related to the primary webpage. One or more of keywords on the list are selected to represent the primary webpage according to one or more objectives. One or more advertisements are selected to be served to the user using the selected keywords. Machine learning techniques may be used to develop a model that automatedly determines keywords representing a webpage.

Abstract: An improved system and method for scheduling online keyword auctions subject to budget constraints is provided. A linear programming model of slates of advertisements may be created for predicting the volume and order in which queries may appear throughout the day for use in allocating bidders to auctions to optimize revenue of an auctioneer. Each slate of advertisements may represent a candidate set of advertisements in order of optimal revenue to an auctioneer. Linear programming using column generation with the keyword as a constraint and a bidder's budget as a constraint may be applied to generate a column that may be added to a linear programming model of slates of advertisements to determine optimal revenue to an auctioneer. Upon receiving a query request, a slate of advertisements that may provide optimal revenue to the auctioneer may be output for sending to a web browser for display.

Abstract: Automatic classification is applied in two stages: classification and ranking. In the first stage, a categorization engine classifies incoming documents to topics. A document may be classified to a single topic or multiple topics or no topics. For each topic, a raw score is generated for a document and that raw score is used to determine whether the document should be at least preliminarily classified to the topic. In the second stage, for each document assigned to a topic (i.e., for each document-topic association) the categorization engine generates confidence scores expressing how confident the algorithm is in this assignment. The confidence score of the assigned document is compared to the topic's (configurable) threshold. If the confidence score is higher than this configurable threshold, the document is placed in the topic's Published list. If not, the document is placed in the topic's Proposed list, where it awaits approval by a knowledge management expert.