Abstract: Constraint programming is used to solve problems that have many variables and that have many possible solutions. An interactive constraint solver application provides additional information to a user if a conflict arises between two choice-constraints so that the additional information can be used to resolve the conflict according to a model-constraint set up by an application developer. The additional information identifies a priority group, or queue, to which the constraint was added. Depending on the particulars included in the additional information, and the model-constraints, the conflict is resolved by the interactive constraint solver application.

Abstract: A method for identifying a set of interpolation point data points from training data for a sparse Gaussian process model, encompassing the following tasks: successively selecting training data points from the set of training data for acceptance into or exclusion from a set of interpolation point data points in accordance with a selection criterion; and terminating selection when a termination criterion exists; the selection criterion depending on a divergence between a target value of the selected training data point and a function value, at the selected training data point, of the Gaussian process model based on the respectively current set of interpolation point data points.

Abstract: A reinforcement learning processor specifically configured to execute reinforcement learning operations by the way of implementing an application-specific instruction set is envisaged. The application-specific instruction set incorporates ‘Single Instruction Multiple Agents (SIMA)’ instructions. SIMA type instructions are specifically designed to be implemented simultaneously on a plurality of reinforcement learning agents which interact with corresponding reinforcement learning environments. The SIMA type instructions are specifically configured to receive either a reinforcement learning agent ID or a reinforcement learning environment ID as the operand. The reinforcement learning processor uses neural network data paths to communicate with a neural network which in turn uses the actions, state-value functions, Q-values and reward values generated by the reinforcement learning processor to approximate an optimal state-value function as well as an optimal reward function.

Abstract: A decision tree methodology based uplift modeling approach, wherein one decision tree is developed simultaneously on test and control groups with the objective of maximizing the mean-difference in identified subsets within the data. The incrementality modeling approach discussed may be applied in any situation where an incrementality model is required to determine who among a group of users should be exposed to a treatment with the goal of receiving a positive response.

Abstract: A mechanism is provided in a data processing system for rating difficulty of a question. The mechanism receives an input question and generates one or more candidate answers from a corpus of knowledge using a pipeline of software engines. The pipeline of software engines generates a plurality of features extracted from the question, the one or more candidate answers, or the corpus of knowledge. The mechanism then generates a question difficulty score based on the plurality of features using a machine learning model. The machine learning model maps features to assigned weights for scaling the difficulty score.

Abstract: Determination of a user-specific likelihood of incident occurrence at a geographic location may be performed. User information, historical incident information, contextual information, and/or other information may be obtained. User information may include user demographic information, user behavior information, user social information, and/or other user related information. Historical incident information may include data relating to crime, mortality, injury, morbidity rates and may be obtained from local law enforcement, local Departments of Motor Vehicles, national security agency such as the Federal Bureau of Investigation, foreign security agency such as the Central Intelligence Agency, international criminal policy organization such as Interpol, national public health agency such as Center for Disease Control, international public health agency such as World Health Organization and/or other sources.

Abstract: Disclosed in some examples are predictive storage techniques for use in a distributed data system. The predictive storage techniques may be used to manage locally stored elements of a shared data collection, such as the storage of files on nodes of the distributed data system that are limited in local storage space. The predictive storage techniques may achieve a balance between consumption of local resources and timely access of important elements in the shared data collection. For example, the predictive storage techniques may be used for keeping or pre-caching certain items of a collection that are determined as likely to be used in local storage for convenient access, and allowing access the remaining items on request over a network.

Abstract: An approach is provided for determining associations and/or propensity among the word occurrences during a therapy interaction for supporting a procedure. The approach involves parsing captured therapy interaction data to identify one or more word occurrences. The approach also involves determining one or more associations among the one or more word occurrences using a fuzzy association analysis and deep belief networks. The approach further involves determining a propensity of the one or more associations based on a probability analysis. The approach also involves specifying the one or more associations as evidence data for supporting a therapeutic outcome, a mental state classification, or a combination thereof.

Abstract: Various aspects provide methods implemented by at least one processor executing on a mobile communication device to efficiently identify, classify, model, prevent, and/or correct the non-benign (e.g., performance degrading) conditions and/or behaviors that are related to an application operating on the device. Specifically, in various aspects, the mobile computing device may derive or extract application-specific features by performing a binary analysis of an application and may determine the application's category (e.g., a “games,” “entertainment,” or “news” category) based on the application-specific features. The mobile computing device may also obtain a classifier model associated with the application's category that includes various conditions, features, behaviors and corrective actions that may be used to quickly identify and correct non-benign behaviors (e.g., undesirable, malicious, and/or performance-degrading behaviors) occurring on the mobile computing device that are related to the application.

Abstract: One embodiment of the invention provides a method comprising defining a brainlet representing a platform-agnostic network of neurons, synapses, and axons. The method further comprises compiling the brainlet into a corelet for mapping onto neurosynaptic substrate, and mapping the corelet onto the neurosynaptic substrate. The corelet is compatible with one or more conditions related to the neurosynaptic substrate.

Abstract: A mechanism is provided in a data processing system for partial ingestion of content. The mechanism receives new content to be ingested into a corpus of information. The mechanism applies a plurality of sub-pipelines of annotation engines against the new content in order of effectiveness. The plurality of sub-pipelines include all annotation engines of an ingestion pipeline. Each sub-pipeline within the plurality of sub-pipelines generates one or more intermediate output objects. The mechanism provides access to the one or more intermediate output objects.

Abstract: Disclosed in some examples are systems, methods, and machine readable mediums which allow for the automatic creation of a skills hierarchy. The skills hierarchy comprises an organization of a standardized list of skills into a hierarchy that describes category relationships between the skills in the hierarchy. The category relationships may include no relationships, parent relationships, and child relationships. A skill may be considered a parent of another skill if the parent skill describes a broader category of skill that includes the child. Other relationships such as grandparent (e.g., a parent's parent), great-grandparent, grandchild, great grandchild and so on may be defined inferentially as well. In some examples, the constructed hierarchy may be organized with broader skills at higher levels and narrower skills at lower levels.

Abstract: A synaptic time-multiplexed (STM) neuromorphic network includes a neural fabric that includes nodes and switches to define inter-nodal connections between selected nodes of the neural fabric. The STM neuromorphic network further includes a neuromorphic controller to form subsets of a set of the inter-nodal connections representing a fully connected neural network. Each subset is formed during a different time slot of a plurality of time slots of a time multiplexing cycle of the STM neuromorphic network. In combination, the inter-nodal connection subsets implement the fully connected neural network. A method of synaptic time multiplexing a neuromorphic network includes providing the neural fabric and forming the subsets of the set of inter-nodal connections.

Abstract: Electrical usage of devices in a building may be monitored to assist a user in labeling devices. In some implementations, a user may provide information about a first device, and this information may be used to obtain a plurality of mathematical models that each correspond to a device or a class of devices. The mathematical models may be used to process an electrical signal, and a score may be generated for each mathematical model. A mathematical model may be selected from the plurality of mathematical models using the scores, such as selecting a mathematical model with a highest score. The first device may be associated with the selected mathematical model.

Abstract: A targeted advertising system selects an asset (e.g., ad) for a current user of a user equipment device (e.g., a digital set top box in a cable network). The system can first operate in a learning mode to receive user inputs and develop evidence that can characterize multiple users of the user equipment device audience. In a working mode, the system can process current user inputs to match a current user to one of the identified users of that user equipment device audience. Fuzzy logic and/or stochastic filtering may be used to improve development of the user characterizations, as well as matching of the current user to those developed characterizations. In this manner, targeting of assets can be implemented not only based on characteristics of a household but based on a current user within that household.

Abstract: It is determined that a population of candidate solutions for an optimization problem has prematurely converged during a metaheuristic optimization run. A cause for premature convergence of the population is determined based, at least in part, on an analysis of the metaheuristic optimization run. A first cataclysm strategy of a plurality of cataclysm strategies is selected based, at least in part, on one of the cause of the premature convergence and a history of the metaheuristic optimization run. A cataclysm is simulated based, at least in part, on the first cataclysm strategy.

Abstract: A logic decoder circuit capable of performing Boolean or fuzzy logic has one or more logic components configured to perform a logic function of an AND-NOT gate, each AND-NOT gate comprising a circuit that performs a conjunction of an excitatory input X and an inhibitory input Y to obtain an output X-AND-NOT-Y substantially linearly for given ranges for X and Y. Certain preferred designs are provided for an X-AND-NOT-Y logic gate and a family of fast fuzzy decoders composed of such gates.

Abstract: Methods and apparatus are provided involving adaptive content inspection. In one embodiment, a content inspection processor may identify information with respect to input data and provide the information to a host controller. The host controller may adapt search criteria or other parameters and provide the adapted parameter to the content inspection processor. Other embodiments may include a content inspection processor having integrated feedback, such that results data is fed back to the content inspection processor. The results data may be processed before being provided to the content inspection processor.

Abstract: A knowledge-based system under uncertainties and/or incompleteness, referred to as augmented knowledge base (AKB) is provided, including constructing, reasoning, analyzing and applying AKBs by creating objects in the form E?A, where A is a rule in a knowledgebase and E is a set of evidences that supports the rule A. A reasoning scheme under uncertainties and/or incompleteness is provided as augmented reasoning (AR).

Abstract: A system that labels an unlabeled message of a social stream. The system including a memory device storing instructions to execute a training model, the training model being trained based on labeled messages, and partitioned into a plurality of class partitions, each of which comprise statistical information and a class label, and a Central Processing Unit (CPU) that computes a confidence for each of the class partitions based on information of an unlabeled message and the statistical information of a respective class partition, and that labels the unlabeled message according to respective confidences of the class partitions.