Abstract: The present invention relates to computer implemented methods and system for verifying hypotheses against ontologies. The methods and systems are designed to accept as inputs a set of axioms and/or assertions constituting a hypothesis, and a set of ontology axioms and/or assertions assumed to be true; determine whether the axioms and assertions constituting the hypothesis are either supported or contradicted by the axioms and assertions in the ontology, and extract the specific ontology axioms and/or assertions that either support or contradict the hypothesis. The result of these methods and of the systems implementing these methods is an indication on whether the hypothesis is supported by the ontology, contradicted by it, or neither supported nor contradicted by it, and if either supported or contradicted, the set of axioms and/or assertions from the ontology that cause the support or contradiction of the hypothesis.

Abstract: Density estimation and/or manifold learning are described, for example, for computer vision, medical image analysis, text document clustering. In various embodiments a density forest is trained using unlabeled data to estimate the data distribution. In embodiments the density forest comprises a plurality of random decision trees each accumulating portions of the training data into clusters at their leaves. In embodiments probability distributions representing the clusters at each tree are aggregated to form a forest density which is an estimate of a probability density function from which the unlabeled data may be generated. A mapping engine may use the clusters at the leaves of the density forest to estimate a mapping function which maps the unlabeled data to a lower dimensional space whilst preserving relative distances or other relationships between the unlabeled data points. A sampling engine may use the density forest to randomly sample data from the forest density.

Abstract: In one aspect, a method includes determining if a rule in a memory of an event-based analysis engine has been used by the event-based analysis engine within a predetermined period of time and moving the rule to a storage device if the rule in the memory of the event-based analysis engine has not been used by the event-based analysis engine within the predetermined period of time.

Abstract: A method and system for an agent-based evidential reasoning decision computer system for determining an adoption rate of a trend is provided. The system includes a plurality of nodes arranged in a tree structure. The plurality of nodes define an evidential reasoning algorithm where lower level nodes receive factors to be considered in the decision and each node assigns a likelihood of an outcome of the received factors, and generates an output to a subsequent higher level node or root of the tree structure. The system also includes a plurality of agent models organized in a hierarchical structure, each agent model comprising a respective set of the plurality of nodes and an output of the agent model, each agent model representing a member of a population, and an aggregator algorithm configured to combine the outputs of the plurality of agent models to generate an output representing an adoption rate.

Abstract: In an embodiment, an automaton determinization method includes: state-generating, first-transition-generating, second-transition-generating, and first-deleting. The state-generating includes generating, assigned with a first symbol, a second state newly. The first-transition-generating includes generating a second transition that leaves from the first state and enters to the second state and that is assigned with the first symbol. The second-transition-generating includes generating, regarding the first transitions, a fourth transition where a state previous to a third transition is substituted with the second state. The third transition is an outgoing transition from a next state of the first transition.

Abstract: Techniques to optimize messages sent to a user of a social networking system. In one embodiment, information about the user may be collected by the social networking system. The information may be applied to train a model for determining likelihood of a desired action by the user in response to candidate messages that may be provided for the user. The social networking system may provide to the user a message from the candidate messages with a selected likelihood of causing the desired action.

Abstract: Techniques for displaying a relationship graph are described herein. In one example, a search term may be used to obtain a plurality of documents from a network, such as the Internet. A plurality of entities, and relationships between at least some of those entities, may be extracted from the documents. In an example user interface, representations of a plurality of entities may be displayed, such as by shapes (e.g., circles) labeled to identify people or organizations. Edges (e.g., lines) may be used to connect different representations of entities and to thereby indicate a relationship between the connected entities. In a particular example, input from movement of a cursor over an edge may result in display of a description of a relationship between the connected entities. In a further particular example, size of each entity may be related to a number of connections each has with others.

Abstract: A method for analyzing data is provided. The method includes generating, using a processing device, a graph from raw data, the graph including a plurality of nodes and edges, deriving, using the processing device, at least one label for each node using a deep belief network, and identifying, using the processing device, a predetermined pattern in the graph based at least in part on the labeled nodes.

Abstract: A system and method to control of sootblowers in a fossil fueled power plant, in particular to plant applications systems using a graphical programming environment in combination with a set of rules to activate sootblowers. The system can be constrained by time limits and/or rule based time limits. Actual blower activation is typically based on the current status of key control variables in the process which alter the actual activation time within a constraints system. The system does not typically require knowledge or models of specific cleanliness relationships. The result is a system without sequences or queues that readily adapts to changing system conditions.

Abstract: Techniques are described for graph partitioning, and in particular, graph bisection. A lower bound is provided that is computed in near-linear time. These bounds may be used to determine optimum solutions to real-world graphs with many vertices (e.g., more than a million for road networks, or tens of thousands for VLSI and mesh instances). A packing lower bound technique determines lower bounds in a branch-and-bound tree, reducing the number of tree nodes. Techniques are employed to assign vertices without branching on them, again reducing the size of the tree. Decomposition is provided to translate an input graph into less complex subproblems. The decomposition boosts performance and determines the optimum solution to an input by solving subproblems independently. The subproblems can be solved independently using a branch-and-bound approach to determine the optimum bisection.

Abstract: Technology is disclosed for communicating surgical information. The technology can receive data from one or more sensors coupled to one or more tools in operation by a user, generate a virtual model of a four-dimensional virtual real-time space, receive motion history data for the user's previous movements, generate a prediction of movement of the first tool in each of the four dimensions in relation to the first vital entity and a prediction score, and output an indication of the prediction score.

Abstract: A computerized method, system for, and computer-readable medium operable to select an algorithm for generating models configured to identify similar user identifiers. A first plurality of models generated by a first algorithm is received. A plurality of lists of similar user identifiers is generated. User queries associated with user identifiers on the plurality of lists of similar user identifiers are identified. Predicted click-through rates for the user queries is received. An average predicted click-through rate is computed for each model based on the predicted click-through rates. A weighted average predicted click-through rate associated with the first plurality of models is computed. The weighted average predicted click-through rate for the first plurality of models can be compared to a weighted average predicted click-through rate for a second plurality of models generated by a second algorithm. The algorithm for generating models is selected based on the comparison.

Abstract: An incremental forward-chaining reasoning rule based system is used where data tuples and rules can be inserted and deleted in runtime. The example method does not require storing or calculating a dependency tree in order to re-reason new implicit tuples. All tuples are associated with an inference counter to provide an efficient delete operation for tuples, without the need of re-calculate existing reasoning results.

Abstract: Systems, methods, and other embodiments associated with data sources adapted for parallel inference on triples associated with a semantic model are described. One example method includes creating a source table that is partitioned on triple predicate and stores triples for entailment. The source table may store compact triple identifiers that have been mapped to triple identifiers from the semantic model.

Abstract: A convex regularized loss function is minimized respective to a prediction tensor of order K to generate an optimized prediction tensor of order K where K>2. The convex regularized loss function comprises a linear combination of (i) a loss function comparing the prediction tensor and an observation tensor of order K representing a set of observations and (ii) a regularization parameter including a K-th order matrix norm decomposition of the tensor trace norm of the prediction tensor. In some such embodiments, the observation tensor of order K represents a set of social network observations and includes at least dimensions corresponding to (1) users, (2) items, and (3) tags. The optimized prediction tensor of order K is suitably used to perform inference operations.

Abstract: A system and method for visually displaying and analyzing criminal and/or public health and safety data for geospatial and/or time variations, including the collection of incident data coupled with geographic and time data, filtering the symptom data based upon a selected time period and geographic range, and creating a visual result based upon statistical modeling including power transform and/or data normalization. According to at least one embodiment, the system for visually displaying and analyzing includes selecting and performing at least one aberration detection method and displaying the result to a user via a visual analytics arrangement.

Abstract: A method or system for classifying brain signals in a BCI. The system comprises a model building unit for building a subject-independent model using labelled brain signals from a pool of subjects.

Abstract: Illustrated is a system and method for anomaly detection in data centers and across utility clouds using an Entropy-based Anomaly Testing (EbAT), the system and method including normalizing sample data through transforming the sample data into a normalized value that is based, in part, on an identified average value for the sample data. Further, the system and method includes binning the normalized value through transforming the normalized value into a binned value that is based, in part, on a predefined value range for a bin such that a bin value, within the predefined value range, exists for the sample data. Additionally, the system and method includes identifying at least one vector value from the binned value. The system and method also includes generating an entropy time series through transforming the at least one vector value into an entropy value to be displayed as part of a look-back window.

Abstract: Disclosed are an optimal technique search method and system that can enable a more effective search for optimal techniques for problem solutions than in the past through the use of a neural network employing genetic algorithm. Provided therein are an execution unit (1) that uses a neural network employing a genetic algorithm to search for an optimal technique and which executes operations using said technique, and an evaluation unit (2) that, along with creating initial setting to transmit to said execution unit, evaluates the content of the operations of the execution unit after the operations have been executed and has the execution unit (1) execute operations a plurality of times, and thereby derives as the optimal technique the initial settings that executed the most effective operation when transmitted to the execution unit (1) out of the results derived from said plurality of operation executions.

Abstract: Techniques can construct a learner's educational context (e.g., course enrollments, subject-matter interests, and/or activity involvements) and tailor query processing using the educational context. For a given query, each concept in a set of concepts can be assigned a weight. The weight can depend on a query term in the query. For example, for a query including “North America”, a “geography” concept and a “history” concept can be determined to be related to the query, and weights can be influenced accordingly. Weights can also depend on a user's educational context (e.g., such that the “geography” weight is higher when a learner is enrolled in a geography course). A query time can also be analyzed in view of schedule data (e.g., indicating when particular topics are to be studied in a course). Weights can further depend on which concepts are recently, currently or will soon be of interest based on the schedule.