Abstract: In a method for displaying messages, a system displays messages from a single user account in multiple viewports. Each viewport orders messages based on an importance score that is calculated based on the user's prior interactions with messages in his user account through that viewport. Each viewport associated with the user account orders messages using a distinct message importance model.

Abstract: A programmable lighting control system integrates time-based, sensor-based, and manual control of lighting and other loads. The system includes one or more groups of controlled lighting areas, which may be, e.g., floors of a building. Each group may have one or more lighting zones, which may be, e.g., individual rooms or offices on a building floor. Each lighting zone includes occupancy and/or daylight sensors that may be wirelessly coupled to a gateway of the group. Each gateway is coupled to a network, such as, e.g., a local area network (LAN). Control software, residing on a computer (e.g., a personal computer or a server) coupled to the network and accessible via the network, remotely communicates with and controls the lighting zones either individually, groupwise, or globally. Each lighting zone can also be locally controlled at the gateway and can function independently of the control software and the gateway.

Abstract: A social network model, based on data relevant to a user, is used for semantic processing to enable improved entity recognition among text accessed by the user. An entity extraction module of the server, with reference to a general training corpus, general gazetteers, user-specific gazetteers, and entity models, parses text to identify entities. The entities may be, for example, people, organizations, or locations. A social network module of the server builds the social network model implicit in the data accessed by the user. The social network model includes the relationships between entities and an indication of the strength of each relationship. The social network module is also used to disambiguate names and unify entities based on the social network model.

Abstract: Disclosed are systems, apparatuses, and methods for implementing a competitive BCM learning rule used in a neural network. Such a method includes identifying a maximally responding neuron with respect to a feature of an input signal. The maximally responding neuron is the neuron in a group that has a response with respect to the feature of the input signal that is greater than a response of each other neuron in the group. Such a method also includes applying a learning rule to weaken the response of each other neuron with respect to the feature of the input signal. The learning rule may also strengthen the response of the maximally responding neuron with respect to the feature of the input signal.

Abstract: Apparatus and methods for learning in response to temporally-proximate features. In one implementation, an image processing apparatus utilizes bi-modal spike timing dependent plasticity in a spiking neuron network. Based on a response by the neuron to a frame of input, the bi-modal plasticity mechanism is used to depress synaptic connections delivering the present input frame and to potentiate synaptic connections delivering previous and/or subsequent frames of input. The depression of near-contemporaneous input prevents the creation of a positive feedback loop and provides a mechanism for network response normalization.

Abstract: Cost factors are utilized and may be estimated to determine split points in a DFA-NFA hybrid. The cost factors may comprise NFA start states, DFA backup factor, DFA-NFA token frequency, DFA steps to match, and NFA states to match. Other cost factors may be used as necessary. The cost factors are multiplied by tunable coefficients and summed. NFA states at minimum cost points are determined for entrance states in the NFA. A DFA is compiled from the entrance paths to the entrance states. NFA states and transitions needed only to reach entrance states may be deleted and all remaining NFA states are made available for execution by the NFA engine. An NFA representation of an NFA is examined by bounded depth-first recursion from each start state.

Abstract: A governance apparatus and a communication method for communicating within the governance apparatus. The governance apparatus includes a Government and micro grid apparatuses. The Government includes Councils such that a macro grid including an artificial intelligence and the Government is configured to respond to an alert pertaining to an event through use of the artificial intelligence and the Government. Each governmental component of the Government is either an Executive or a Parliament. Each micro grid apparatus is either a simple micro grid apparatus or a complex micro grid apparatus. Each Executive is a unique processor disposed in a unique simple micro grid apparatus. Each Parliament is a unique processor disposed in a unique complex micro grid apparatus. Each unique processor is a Council.

Abstract: Systems, methods, and other embodiments associated with similarity analysis using tri-point arbitration are described. In one embodiment, a method includes selecting a data point pair and an arbiter point from a data set. A tri-point arbitration coefficient (?TAC) is calculated for data point pairs based, at least in part, on a distance between the first and second data points and the arbiter point. A similarity metric is determined for the data set based, at least in part, on an aggregation of tri-point arbitration coefficients for data point pairs in the set of data points using the selected arbiter point.

Abstract: A method of producing dynamic controllable data composites from two or more data segments includes: building or training one or more function mappers to map between one or more extracted feature envelopes sets from the original data and one or more general parametric representations of the data; combining the extracted feature envelopes or the function mappers using two or more audio segments; and feeding the extracted feature envelopes or combined feature envelopes to the function mappers to obtain synthesis parameters to drive a synthesis process.

Abstract: Described is a system and method for analyzing competing and non-competing hypothesis. Diagnostic strength of evidence in connection with a competing hypothesis analysis is analyzed. Veracity of evidence in connection with a non-competing hypothesis analysis is analyzed.

Abstract: Systems, methods, and other embodiments associated with graphical user interface (GUI) component classification are described. One example method includes generating a first vector. The first vector may be generated based on image data describing a GUI component. The example method may also include assigning a GUI component classifier to the GUI component. Assigning the GUI component classifier may comprise comparing the first vector to members of a vector set. Members of the vector set may describe GUI elements. The example method may also include providing the GUI component classifier.

Abstract: A self configuring knowledge representation system and method is presented which self configures based on the external stimuli in order to answer the query in a way best suited to the user. The domain knowledge representation here is by way of graphs that allows the knowledge to self configure based on the query intent to give “specific” to the query answers.

Abstract: In exemplary implementations of this invention, mobile application (app) installations by users of one or more networks are predicted. Using network data gathered by smartphones, multiple “candidate” graphs (including a call log graph) are calculated. The “candidate” graphs are weighted by an optimization vector and then summed to calculate a composite graph. The composite graph is used to predict the conditional probabilities that the respective users will install an app, depending in part on whether the user's neighbors have previously installed the app. Exogenous factors, such as the app's quality, may be taken into account by creating a virtual candidate graph. The conditional probabilities may be used to select a subset of the users. Signals may be sent to the subset of users, including to recommend an app. Also, the probability of successful “trend ignition” may be predicted from network data.

Abstract: A processing device of an information processing system is operative to obtain observed feedback data, to construct a model that accounts for both the observed feedback data and additional feedback data that is missing from the observed feedback data, to optimize one or more parameters of the model using a training objective function, and to generate a list of recommended items for a given user based on the optimized model. In illustrative embodiments, the missing feedback data comprises data that is missing not at random (MNAR), and the model comprises a matrix factorization model. The processing device may implement a recommender system comprising a training module coupled to a recommendation module.

Abstract: A system and methods for providing a Control User Interface (CUI) for controlling a Networked Device (ND) with a Remote Controller (RC) are provided. A Service Provider (SP) receives capability information of the RC and CUI information from the ND, generates a reconfigured CUI based on a location of the RC from which to control the ND, and transmits the reconfigured CUI to the ND. The ND receives the reconfigured CUI from the SP and transmits the reconfigured CUI to the RC. The RC receives the reconfigured CUI from the ND and controls the ND with the reconfigured CUI.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting user interests. In one aspect, a method includes training a prediction model to generate a category prediction of a next query from a set of queries, the category prediction specifying categories to which a next query belongs, the prediction model comprising a plurality of prediction functions that each generate a category prediction of a next query, wherein the prediction functions include two or more of a time-based prediction functions that generate a category prediction based on the category data and a difference of submission times of the queries, a rank-based prediction functions that generate a category prediction based on the category data and a rank order by which the queries were received, and a category-based prediction function that generates a category prediction based on the category data.

Abstract: Certain examples provide systems, methods, and apparatus to model clinical data objects in a content-based clinical system. An example system includes a processor and a memory to implement a constraint definition language processor to define a detailed clinical model to express a clinical concept as a standardized and reusable set of clinical knowledge. The constraint definition language processor is to use a compiled, context-free constraint definition language to define the detailed clinical model as a compiled object. The constraint definition language processor is to provide the compiled object as content for at least one of a clinical content database and a clinical information system. Content represents one or more parameters to instruct a clinical application.

Abstract: Systems and methods are provided for generating a forecasting model based on a set of measured values. Consistent with certain embodiments, the forecasting model may include a seasonal function and a trend function. Further, consistent with other embodiments, the computer-implemented systems and methods may include computing the forecasting model by minimizing an error function representing the error between the forecasting model and the measured values. In addition, consistent with yet other embodiments, the computer-implemented systems and methods may include a process for outlier detection and removal based on a computation of residuals. Still further, consistent with some embodiments, systems and methods may be provided to filter the seasonality and/or the trend components to eliminate sudden variations in the model predictions resulting from sudden, large variations in the measurement values.

Abstract: A method is described for applying distributed stochastic optimization techniques of evolutionary computation using a plurality of servers and a plurality of clients machines being connected via a computer network such as the Internet. The stochastic optimization techniques of evolutionary computation seek to optimize a populations of individuals against one or more predetermined fitness criteria when applied to solving solve the network routing problem coupled with one or more information retrieval problems. The field of evolutionary computation encompasses stochastic optimization techniques, such as randomized search strategies, in the form of evolutionary strategies (ES), evolutionary programming (EP), genetic algorithms (GA), classifier systems, evolvable hardware (EHW), and genetic programming (GP). The stochastic optimization component objectives of the multimedia resource discovery and retrieval systems includes maximization of resource utilization and of overall LAN throughput.

Abstract: This invention is related to systems and methods of analyzing sensory ability data. One embodiment of the present invention includes a method comprising the steps of receiving data from a remote location. The data is comprised of sensory ability data and demographic data associated with a subject. The data may then be stored. Further, the method includes identifying a potential evaluation level associated with the subject. The evaluation level is identified, at least in part, utilizing a sports tree function. The method also includes retrieving peer data associated with the potential evaluation level. Additionally, the method includes determining when the peer data is statistically powerful for use in generating a comparative profile of the sensory ability data associated with the subject. Additional embodiments develop training programs based on one or more training program functions.