Abstract: A method, computer-readable storage medium, and device for generating a character model. The method comprises: receiving an input image of a reference subject; processing the input image to generate a normalized image; identifying a set of features present in the normalized image, wherein each feature in the set of features corresponds to a portion of a head or body of the reference subject; for each feature in the set of features, processing at least a portion of the normalized image including the feature by a neural network model corresponding to the feature to generate a parameter vector corresponding to the feature; and combining the parameter vectors output by respective neural network models corresponding to respective features in the set of features to generate a parameterized character model corresponding to reference subject in the input image.

Abstract: A computer-implemented method is provided of allowing a user to automatically transform domain knowledge into a machine learning model to be used in real-time operation of video games. The method comprises providing a user interface which allows a user to define domain knowledge relating to a video game by specifying one or more labeling functions; transforming the labeling functions into executable code; labeling raw data relating to the video game using the executable code to obtain labeled data; and applying an automated machine learning module to the labeled data to obtain a machine learning model.

Abstract: A computer-implemented method for providing video game content is provided. The method comprises monitoring a request rate of requests to provide video game content; and in response to the request rate exceeding a threshold request rate: initialising at least one instance of a first machine learning model, wherein the first machine learning model is configured to provide an output which is approximate to the output of a second machine learning model from which the first machine learning model is derived, the first machine learning model being produced by a model derivation process to have a faster response time compared to the second machine learning model; and providing video game content, wherein providing the video game content comprises generating an output responsive to the specified input using the at least one instance of the first machine learning model.

Abstract: A computer-implemented method for providing video game content is provided. The method comprises maintaining a current machine learning model for each of a plurality of machine learning model branches; receiving a request to provide video game content responsive to specified input; in response to receiving the request, identifying a selected one of the machine learning model branches, wherein the machine learning model branch is selected based on an evaluation of the current machine learning model for each branch; and providing video game content responsive to the request, wherein providing the video game content comprises generating an output responsive to the specified input with the current machine learning model for the selected branch.

Abstract: This specification describes a system for generating positions of map items such as buildings, for placement on a virtual map. The system comprises: at least one processor; and a non-transitory computer-readable medium including executable instructions that when executed by the at least one processor cause the at least one processor to perform at least the following operations: receiving an input at a generator neural network trained for generating map item positions; generating, with the generator neural network, a probability of placing a map item for each subregion of a plurality of subregions of the region of the virtual map; and generating position data of map items for placement on the virtual map using the probability for each subregion.

Abstract: A system for use in video game development to generate expressive speech audio comprises a user interface configured to receive user-input text data and a user selection of a speech style. The system includes a machine-learned synthesizer comprising a text encoder, a speech style encoder and a decoder. The machine-learned synthesizer is configured to generate one or more text encodings derived from the user-input text data, using the text encoder of the machine-learned synthesizer; generate a speech style encoding by processing a set of speech style features associated with the selected speech style using the speech style encoder of the machine-learned synthesizer; combine the one or more text encodings and the speech style encoding to generate one or more combined encodings; and decode the one or more combined encodings with the decoder of the machine-learned synthesizer to generate predicted acoustic features.

Abstract: A facility for processing a relational database query is described. The facility receives the relational database query, and constructs a multidimensional database query based on the received relational database query. The facility submits the constructed multidimensional database query for execution against a multidimensional data source.

Abstract: Techniques to improve query caching performance by efficiently selecting queries stored in a cache for evaluation and increasing the cache hit rate by allowing for inexact matches. A list of candidate queries stored in the cache that potentially could be used to answer a new query is first determined. This list may include all cached queries, cached queries containing exact matches for select list items, or cached queries containing exact and/or inexact matches. Each of at least one candidate query is then evaluated to determine whether or not there is a cache hit, which indicates that the candidate query could be used to answer the new query. The evaluation is performed using a set of rules that allows for inexact matches of aggregates, if any, in the new query. A query plan is generated for the new query based on a specific candidate query with a cache hit.

Abstract: Techniques to improve query caching performance by efficiently selecting queries stored in a cache for evaluation and increasing the cache hit rate by allowing for inexact matches. A list of candidate queries stored in the cache that potentially could be used to answer a new query is first determined. This list may include all cached queries, cached queries containing exact matches for select list items, or cached queries containing exact and/or inexact matches. Each of at least one candidate query is then evaluated to determine whether or not there is a cache hit, which indicates that the candidate query could be used to answer the new query. The evaluation is performed using a set of rules that allows for inexact matches of aggregates, if any, in the new query. A query plan is generated for the new query based on a specific candidate query with a cache hit.

Abstract: A facility for processing a relational database query is described. The facility receives the relational database query, and constructs a multidimensional database query based on the received relational database query. The facility submits the constructed multidimensional database query for execution against a multidimensional data source.

Abstract: The processing of a query that is submitted to a central controlling server where the central controlling server sources data from one or more backend databases and where such data is referenced by subqueries associated with the query is disclosed.