Abstract: A method and system collects diagnostic data for use in diagnosing existing and potential image quality issues related to a printer. Document cover sheets and/or electronic versions of the cover sheets are routinely collected, even when there has been no reported image quality issue, to preserve a historical record of the output of the printer over an extended time period. The historical record of the images on the routinely collected cover sheets and/or electronic versions of the cover sheets include data useful in diagnosing existing and potential image quality issues related to substantive pages of the documents processed by the printer.

Abstract: One embodiment of the present invention provides an energy-management system for managing energy within a predominantly closed power system. The system includes an identifying mechanism configured to identify a number of components coupled to the energy-management system, a receiving mechanism configured to receive current status information of the components, an evaluating mechanism configured to evaluate future energy need, a planning mechanism configured to plan future energy activities based on the energy need and the current status information, and a controlling mechanism configured to control operations of the components based on the planned energy activities.

Abstract: One embodiment of the present invention provides a system for clustering heterogeneous events using user-provided constraints. During operation, the system estimates, based on a probabilistic model, a distribution of events across clusters such that each cluster includes a set of events. Next, the system estimates a probability distribution for an event property associated with each cluster. The system receives heterogeneous event data, and analyzes the heterogeneous event data to determine the probability distribution of event properties of clusters and to assign events to clusters. The system receives user input specifying the user-provided constraints for specializing the probabilistic model, and performs at least one of: re-computing the assignment of events to clusters, and re-determining the probability distribution of event properties of clusters based on the user input.

Abstract: One embodiment of the present invention provides a system for clustering heterogeneous events. During operation, the system finds a partition of events into clusters such that each cluster includes a set of events. In addition, the system estimates probability distributions for various properties of events associated with each cluster. The system obtains heterogeneous event data, and analyzes the heterogeneous event data to determine the distribution of event properties associated with clusters and to assign events to clusters.

Abstract: One embodiment of the present invention provides an energy-management system for managing energy within a predominantly closed power system. During operation, the system obtains one or more control actions that control energy transitions of a plurality of components, receives transitional characteristics associated with the components, and schedules the energy transitions by generating a set of offsets. A respective offset results in a delay of at least one energy transition.

Abstract: One embodiment of the present invention provides a system for multi-domain clustering. During operation, the system collects domain data for at least two domains associated with users, wherein a domain is a source of data describing observable activities of a user. Next, the system estimates a probability distribution for a domain associated with the user. The system also estimates a probability distribution for a second domain associated with the user. Then, the system analyzes the domain data with a multi-domain probability model that includes variables for two or more domains to determine a probability distribution of each domain associated with the probability model and to assign users to clusters associated with user roles.

Abstract: A system facilitates automatically determining the hypothetical context information or the distribution of hypothetical contexts. During operation, the system receives a request from a user for one or more recommendations. The system also receives a current context substantially associated with the request. The system then determines a hypothetical context for the request, wherein the hypothetical context may be determined by considering several sources of information, including but not limited to the current context, past contexts, and relationships between the current context and past contexts. Next, the system determines one or more recommendations for the user based on the hypothetical context. Finally, the system returns the one or more recommendations to the user.

Abstract: A system and method schedules jobs in a cluster of compute nodes. A job with an unknown resource requirement profile is received. The job includes a plurality of tasks. Execution of some of the plurality of tasks is scheduled on compute nodes of the cluster with differing capability profiles. Timing information regarding execution time of the scheduled tasks is received. A resource requirement profile for the job is inferred based on the received timing information and the differing capability profiles. Execution of remaining tasks of the job is scheduled on the compute nodes of the cluster using the resource requirement profile.

Abstract: A system for compiling a machine operable diagnostic system includes a header unit, a component unit, an effects unit, a test unit, and an instantiation unit. The header unit identifies general properties of a system from a generic description in an electronic format which includes at least one category of a plurality of variations. The component unit identifies a plurality of components of a modeled system from the generic description, and adds and separates the plurality of variations to the plurality of components, and each identified component includes at least one type of failure and a probability of the at least one type of failure. The effects unit identifies from the generic description a plurality of observable effects, each observable effect corresponding to at least one type of failure for at least one component. The test unit identifies from the generic description a plurality of tests and test outcomes designed to elicit the observable effects.

Abstract: A system includes a plurality of processors programmed to perform distributed, iterative computations on aggregate statistics of a data set. Each of the processors maintains global statistics describing the data set. The processors are programmed to iteratively perform a plurality of Gibbs sampling sweeps over a corresponding subset of the data set to update the corresponding global statistics. The processors are further programmed to maintain differential vectors representing the changes made to the global statistics by local computation. These can be broadcast to other processors to create a consistent global statistic across processors. The differential vectors can be saved for a number of past generations to provide updates for slow processors, processors that have been down for some time and newly added processors to the network. Additional methods are described for efficiently compressing multiple differential vector updates in to a smaller number of equally informative updates.

Abstract: A recommender system determines a probability threshold for an activity-prediction model, and uses the probability threshold to predict whether a user is performing a target activity. To determine the probability threshold, the system computes a set of activity probabilities based on contextual information for a set of historical activities, and based on an activity-prediction model for a target activity. The system then compares a set of probability thresholds with the set of activity probabilities to determine a prediction success rate for each probability threshold. The system computes a utility score for each probability threshold based on the prediction success rates and a utility function, and selects a probability threshold whose utility score is optimal amongst the utility scores of the set of thresholds and greater than or equal to a baseline utility score. The system then assigns the probability threshold to the activity-prediction model.

Abstract: An activity-modeling system computes an amount of time that a user is expected to spend when performing activities of a certain type. During operation, the system can obtain a plurality of location events associated with the user, such that a respective location event indicates a time at which a user logged his location while performed an activity related to the activity type. The system selects, from the plurality of location events, a set of location events associated with the activity type. The system determines an activity start-time and an activity end-time for the activity type from the set of location events, and computes an activity-duration time for the activity type based on the determined activity start-time and the activity end-time.

Abstract: A method for detecting a persistent change in a dynamically varying scene includes: obtaining a set of reference images of the scene; transforming the reference images into an abstract feature space; classifying pixels of the reference images in the abstract feature space; generating a stable reduced-reference image based on the classifications of corresponding pixels; obtaining a set of test images of the scene; transforming the test images into the abstract feature space; classifying pixels of the test images in the abstract feature space; generating a stable test image based on the classifications of corresponding pixels; and comparing the stable reduced-reference and test images to one another to detect a difference therein, the difference corresponding to a persistent change in the dynamically varying scene occurring between when the reference images and the test images were obtained.

Abstract: A camera system (10) includes: a camera (12) that obtains a test image (200); and an image processor (30). The image processor (30): analyzes said test image (200) to detect an object (22) appearing in the test image (200); determines a location where the detected object (22) appears in the test image (200); compares the determined location with a reference location; and determines if the camera (12) is one of properly aligned or misaligned based upon a result of said comparison.

Abstract: A method (100) is provided for characterizing a functionality of a flash (14) from at least one image (200) captured with a camera (12) using the flash (14). The method (100) includes: analyzing the image (200) by segmenting (106) the image (200) into at least one sub-region (206, 208), and applying a metric (108) to the sub-region (206, 208) to measure an image property within the sub-region (206, 208); and determining (110) the functionality of the flash (14) in response to a result of the applied metric.

Abstract: A method (100) is provided for detecting an obstruction within a field of view of a camera (12) from an image (200) captured by the camera (12). The method (100) includes: analyzing the image (200) by applying edge detection (104) to the image (200), identifying (108) regions of the image (200) lacking edge content and comparing (112) a size of the identified regions to a threshold; and determining if there is an obstruction based upon a result of said comparison.

Abstract: A recommender system uses an activity decision tree to model the changes in a user's behavior according to a plurality of time-related features. The system determines historical activities for the user, and generates a decision tree for the user's historical activities. Each leaf node of the decision tree is associated with an activity-prediction model that computes a probability for a corresponding activity. The system selects a path of the decision tree from a root node to a leaf node of the decision tree based on a target time. The selected path traverses two or more non-leaf nodes that are each associated with a temporal decision model that compares the target time against a temporal classifier. The system then determines a probability for a user activity based on an activity-prediction model of the selected path.

Abstract: A recommender system determines a probability threshold for an activity-prediction model, and uses the probability threshold to predict whether a user is performing a target activity. To determine the probability threshold, the system computes a set of activity probabilities based on contextual information for a set of historical activities, and based on an activity-prediction model for a target activity. The system then compares a set of probability thresholds with the set of activity probabilities to determine a prediction success rate for each probability threshold. The system computes a utility score for each probability threshold based on the prediction success rates and a utility function, and selects a probability threshold whose utility score is optimal amongst the utility scores of the set of thresholds and greater than or equal to a baseline utility score. The system then assigns the probability threshold to the activity-prediction model.

Abstract: One embodiment of the present invention provides an energy-management system for managing energy within a predominantly closed power system. During operation, the system obtains one or more control actions that control energy transitions of a plurality of components, receives transitional characteristics associated with the components, and schedules the energy transitions by generating a set of offsets. A respective offset results in a delay of at least one energy transition.

Abstract: One embodiment of the present invention provides managing component driver for an energy-management system that manages energy within a predominantly closed power system. The component driver includes a receiving mechanism configured to receive current status information for a component associated with the component driver, a model-construction mechanism configured to construct a performance model for the component, and a transmitting mechanism configured to transmit the performance model to an optimization and control module.