Abstract: A computer-implemented method of processing data comprising a plurality of observations associated with respective ordered input values to train a Gaussian process (GP) to model the data. The method includes initialising an ordered plurality of inducing input locations, and initialising parameters of a multivariate Gaussian distribution over a set of inducing states, each inducing state having components corresponding to a Markovian GP and one or more derivatives of the Markovian GP at a respective one of the inducing inputs. The initialised parameters include a mean vector and a banded Cholesky factor of a precision matrix for the multivariate Gaussian distribution. The method further includes iteratively modifying the parameters of the multivariate Gaussian distribution, to increase or decrease an objective function corresponding to a variational lower bound of a marginal log-likelihood of the observations under the Markovian GP.

Abstract: Checking and/or completing for data grids is described such as for grids having rows and columns of cells at least some of which contain data values such as numbers or categories. In various embodiments predictive probability distributions are obtained from an inference engine for one or more of the cells and the predictive probability distributions are used for various tasks such as to suggest values to complete blank cells, highlight cells having outlying values, identify potential errors, suggest corrections to potential errors, identify similarities between cells, identify differences between cells, cluster rows of the data grid, and other tasks. In various embodiments a graphical user interface displays a data grid and provides facilities for completing, error checking/correcting, and analyzing data in the data grid.

Abstract: A ground truth engine is described which has a memory holding a plurality of captured images depicting an articulated item. A processor of the engine is configured to access a parameterized, three dimensional (3D) model of the item. An optimizer of the ground truth engine is configured to compute ground truth values of the parameters of the 3D model for individual ones of the captured images, such that the articulated item depicted in the captured image fits the 3D model, the optimizer configured to take into account feedback data from one or more humans, about accuracy of a plurality of the computed values of the parameters.

Abstract: A ground truth engine is described which has a memory holding a plurality of captured images depicting an articulated item. A processor of the engine is configured to access a parameterized, three dimensional (3D) model of the item. An optimizer of the ground truth engine is configured to compute ground truth values of the parameters of the 3D model for individual ones of the captured images, such that the articulated item depicted in the captured image fits the 3D model, the optimizer configured to take into account feedback data from one or more humans, about accuracy of a plurality of the computed values of the parameters.

Abstract: A method is provided herein for managing a plurality of computing entities. The method includes sending a dynamic attribute dependency to one or more of the computing entities. The dynamic attribute dependency specifies a constraint for performing the management operation based on a dynamic attribute of each of the one or more computing entities. Additionally, the method includes scheduling, based on the plan, an atomic task configured to perform the management operation on each of the one or more a computing entities based on whether the constraint is resolved. The method further includes performing the atomic task if the constraint is resolved.

Abstract: Checking and/or completing for data grids is described such as for grids having rows and columns of cells at least some of which contain data values such as numbers or categories. In various embodiments predictive probability distributions are obtained from an inference engine for one or more of the cells and the predictive probability distributions are used for various tasks such as to suggest values to complete blank cells, highlight cells having outlying values, identify potential errors, suggest corrections to potential errors, identify similarities between cells, identify differences between cells, cluster rows of the data grid, and other tasks. In various embodiments a graphical user interface displays a data grid and provides facilities for completing, error checking/correcting, and analyzing data in the data grid.

Abstract: Managing groups of entities is described. In an embodiment an administrator manages operations on a plurality of entities by constructing a management scenario which defines tasks to be applied on a group of entities. In an example the management scenario includes information on dependencies between entities and information on entity attributes, for example operating system version or CPU usage. In an embodiment an entity management engine converts the tasks and dependencies in the scenario to a management plan. In an example the management plan is a list of operations and conditions to be respected in applying an operation to an entity. In an embodiment the plan can be validated to ensure there are no conflicts. In an embodiment the entity management engine also comprises a scheduler which runs tasks contained in the plan and monitors their outcome.

Abstract: An outgoing message monitor is provided. In an embodiment, outgoing messages are monitored to detect potential errors and alerts may be triggered. Using information about a message such as an email, a first classifier classifies the email into an expected class and a second classifier classifies the email into an actual class. On the basis of a comparison of the expected and actual classes an alert may be triggered. In an embodiment, the second classifier uses information derived from text content of the email which may optionally be pre-processed. The first classifier, for example, uses other information about the email such as its intended recipients, information about the presence of attachments, information about whether the email is part of a thread and other information.

Abstract: A method is provided herein for managing a plurality of computing entities. The method includes sending a dynamic attribute dependency to one or more of the computing entities. The dynamic attribute dependency specifies a constraint for performing the management operation based on a dynamic attribute of each of the one or more computing entities. Additionally, the method includes scheduling, based on the plan, an atomic task configured to perform the management operation on each of the one or more a computing entities based on whether the constraint is resolved. The method further includes performing the atomic task if the constraint is resolved.

Abstract: Managing groups of entities is described. In an embodiment an administrator manages operations on a plurality of entities by constructing a management scenario which defines tasks to be applied on a group of entities. In an example the management scenario includes information on dependencies between entities and information on entity attributes, for example operating system version or CPU usage. In an embodiment an entity management engine converts the tasks and dependencies in the scenario to a management plan. In an example the management plan is a list of operations and conditions to be respected in applying an operation to an entity. In an embodiment the plan can be validated to ensure there are no conflicts. In an embodiment the entity management engine also comprises a scheduler which runs tasks contained in the plan and monitors their outcome.

Abstract: It is required to provide an automated process for forming a visually appealing digital collage (also referred to as a tapestry or photomontage) from a plurality of input images. Input images and a collage are labeled. Labels in the collage specify which regions of the input images are used to form those collage regions. An energy function is created for the labeling which contains terms that take into account various criteria to produce a collage. For example, those criteria may relate to the amount of information in the images, similarity of the images, entropy in the images or object class information. A multi-stage optimization process is applied to the energy function which is fast and enables large numbers of input images to be used to produce a collage.

Abstract: An outgoing message monitor is provided. In an embodiment, outgoing messages are monitored to detect potential errors and alerts may be triggered. Using information about a message such as an email, a first classifier classifies the email into an expected class and a second classifier classifies the email into an actual class. On the basis of a comparison of the expected and actual classes an alert may be triggered. In an embodiment, the second classifier uses information derived from text content of the email which may optionally be pre-processed. The first classifier, for example, uses other information about the email such as its intended recipients, information about the presence of attachments, information about whether the email is part of a thread and other information.

Abstract: It is required to provide an automated process for forming a visually appealing digital collage (also referred to as a tapestry or photomontage) from a plurality of input images. Input images and a collage are labeled. Labels in the collage specify which regions of the input images are used to form those collage regions. An energy function is created for the labeling which contains terms that we design to enable us to take into account various criteria we specify to produce a good collage. For example, those criteria may relate to the amount of information in the images, similarity of the images, entropy in the images or object class information. A multi-stage optimization process is applied to the energy function which is fast and enables large numbers of input images to be used to produce a collage.