Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a social search engine. In one aspect, a method includes receiving an answer to a question from an answerer; identifying an entity in the answer, in which the entity refers to a product, a service, a company, or a merchant; obtaining permission from the answerer to provide an interactive link for the entity in the answer to a resource from which the entity can be obtained; receiving a user submitted query and determining that the query is related to the entity; and responsive to determining, providing the answer, including the link to the resource, as part of search results that are responsive to the query.

Abstract: Embodiments of the invention relate to producing spike-timing dependent plasticity using electronic neurons interconnected in a crossbar array network. The crossbar array network comprises a plurality of crossbar arrays. Each crossbar array comprises a plurality of axons and a plurality of dendrites such that the axons and dendrites are transverse to one another, and multiple synapse devices, wherein each synapse device is at a cross-point junction of the crossbar array coupled between a dendrite and an axon. The crossbar arrays are spatially in a staggered pattern providing a staggered crossbar layout of the synapse devices.

Abstract: Described are variational Expectation Maximization (EM) embodiments for learning a mixture model using component-dependent data partitions, where the E-step is sub-linear in sample size while the algorithm still maintains provable convergence guarantees. Component-dependent data partitions into blocks of data items are constructed according to a hierarchical data structure comprised of nodes, where each node corresponds to one of the blocks and stores statistics computed from the data items in the corresponding block. A modified variational EM algorithm computes the mixture model from initial component-dependent data partitions and a variational R-step updates the partitions. This process is repeated until convergence. Component membership probabilities computed in the E-step are constrained such that all data items belonging to a particular block in a particular component-dependent partition behave in the same way.

Abstract: Methods and systems for Web-scale entity relationship extraction are usable to build large-scale entity relationship graphs from any data corpora stored on a computer-readable medium or accessible through a network. Such entity relationship graphs may be used to navigate previously undiscoverable relationships among entities within data corpora. Additionally, the entity relationship extraction may be configured to utilize discriminative models to jointly model correlated data found within the selected corpora.

Abstract: Methods and computer storage media are provided for generating an algorithm used to provide preliminary rankings to candidate documents. A final ranking function that provides final rankings for documents is analyzed to identify potential preliminary ranking features, such as static ranking features that are query independent and dynamic atom-isolated components that are related to a single atom. Preliminary ranking features are selected from the potential preliminary ranking features based on many factors. Using these selected features, an algorithm is generated to provide a preliminary ranking to the candidate documents before the most relevant documents are passed to the final ranking stage.

Abstract: Disclosed are systems, methods, and computer-program products for segmenting content of an input signal and applications thereof. In an embodiment, the system includes simulated neurons, a phase modulator, and an entity-identifier module. Each simulated neuron is connected to one or more other simulated neurons and is associated with an activity and a phase. The activity and the phase of each simulated neuron is set based on the activity and the phase of the one or more other simulated neurons connected to each simulated neuron. The phase modulator includes individual modulators, each configured to modulate the activity and the phase of each of the plurality of simulated neurons based on a modulation function. The entity-identifier module is configured to identify one or more distinct entities (e.g., objects, sound sources, etc.) included in the input signal based on the one or more distinct collections of simulated neurons that have substantially distinct phases.

Abstract: The technology described herein includes a system and/or a method for determining confidence of object identification. The technology includes selecting at least one confidence rule from a plurality of confidence rules based on a first discrimination identification and a second discrimination identification. The technology further includes generating an object confidence weight based on one or more first identification parameters, one or more second identification parameters, and the selected at least one confidence rule. The technology further includes fusing the received set of data based on an alpha parameter, a beta parameter, and the object confidence weight. The technology further includes generating a probability of identification of a classification of the object based on the fused data.

Abstract: The disclosed embodiments provide a system that facilitates use of an application. During operation, the system obtains an activity history of interaction between the user and the application during use of the application by the user. Next, the system applies a predictive model to the activity history to predict a probability of a user action in the application. Finally, the system facilitates subsequent real-time use of the application by the user based on the probability of the user action.

Abstract: A method and apparatus for spatio-temporal compressive sensing, which allows accurate reconstruction of missing values in any digital information represented in matrix or tensor form, is disclosed. The method of embodiments comprises three main components: (i) a method for finding sparse, low-rank approximations of the data of interest that account for spatial and temporal properties of the data, (ii) a method for finding a refined approximation that better satisfies the measurement constraints while staying close to the low-rank approximations obtained by SRMF, and (iii) a method for combining global and local interpolation. The approach of embodiments also provides methods to perform common data analysis tasks, such as tomography, prediction, and anomaly detection, in a unified fashion.

Abstract: A method for a computer-aided control of a technical system is provided. The method involves use of a cooperative learning method and artificial neural networks. In this context, feed-forward networks are linked to one another such that the architecture as a whole meets an optimality criterion. The network approximates the rewards observed to the expected rewards as an appraiser. In this way, exclusively observations which have actually been made are used in optimum fashion to determine a quality function. In the network, the optimum action in respect of the quality function is modeled by a neural network, the neural network supplying the optimum action selection rule for the given control problem. The method is specifically used to control a gas turbine.

Abstract: According to one embodiment, an information terminal includes a storing unit configured to store image pickup data picked up in time series by an image pickup apparatus that picks up images of a sales floor and sales data obtained by recording sales of a commodity displayed in the sales floor and date and time of registration of the sales; a list display unit configured to list-display videos of a predetermined sales floor in time series at every predetermined time interval on the basis of the image pickup data; a totalizing unit configured to totalize, at the every time interval, list-displayed sales of the commodity of the sales floor on the basis of the sales data; and a sales display unit configured to display the totalized sales of the commodity at the every time interval.

Abstract: An information processing device includes a model learning unit that carries out learning for self-organization of internal states of a state transition prediction model which is a learning model having internal states, a transition model of the internal states, and an observation model where observed values are generated from the internal states, by using first time series data, wherein the model learning unit learns the observation model of the state transition prediction model after the learning using the first time series data, by fixing the transition model and using second time series data different from the first time series data, thereby obtaining the state transition prediction model having a first observation model where each sample value of the first time series data is observed and a second observation model where each sample value of the second time series data is observed.

Abstract: Disclosed is a tool that assesses and classifies data in a data set to and compares those assessments with nominal attributes and text attributes of data present in a new record to assign a classification to the new record. The classification assigned to the new record is provided a confidence level based on both a qualitative factor and a quantitative factor. The qualitative factor may be calculated by forming of a list of important words for each class and comparing the list to data in the new record and converting the comparison into a confidence level; the quantitative factor may be calculated by estimating the importance or weight of several factors, ratios of certain probabilities related to the most likely class and to the second most likely class, and using the importance of the factors and matchfactors to scale the resulting ratio, then transforming the resulting ratio into a confidence level.

Abstract: Two methods and systems for fast construction of poset isomorphism certificates are provided. Posets (partially-ordered sets) generalize graphs. The invented certificates are number sequences such that two posets are isomorphic if and only if their corresponding certificates coincide. The first method yields the (Omicron,Iota) poset isomorphism certificate. The minimal Phi-isomorphism certificate can be constructed by partitioning vertices of the graphs into Phi-ranked symmetry clusters and constructing a topological Phi-vertex ranking. Thus, symmetries in posets are detectable at low cost. In addition, the Phi-vertex ranking and a poset isomorphism certificate provide a pair of separate one-dimensional keys for poset encoding.

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 stores triples for entailment in a manner that is adapted for parallel inference. The source table may be partitioned by triple predicate or may store compact triple identifiers that have been mapped to triple identifiers from the semantic model.

Abstract: Information of an action of fact confirmation performed between nodes that are objects relating to fact confirmation in an application procedure is read from a predetermined storage unit that previously stores whether an action of fact confirmation is an action of operating information or an action of confirming information, and based on the read information of the action, when a fraud is committed in an action of operating information, reliability of the application procedure is calculated based on a probability of a detection of a fraud in an action of confirming information performed in a node connected to the action of operating information.

Abstract: The present invention is a computer-enabled method for combining information stored in a hierarchical classification structure, such as the International Patent Classification system, with the frequency of events resulting from human decision processes in order to generate an association index for a patent classification. The association index can then be used to quickly analyze a portfolio of patents.

Abstract: A system for generating a representation of a situation is disclosed. The system comprises one or more computer-readable media including computer-executable instructions that are executable by one or more processors to implement a method of generating a representation of a situation. The method comprises receiving input data regarding a target population. The method further comprises constructing a synthetic data set including a synthetic population based on the input data. The synthetic population includes a plurality of synthetic entities. Each synthetic entity has a one-to-one correspondence with an entity in the target population. Each synthetic entity is assigned one or more attributes based on information included in the input data. The method further comprises receiving activity data for a plurality of entities in the target population.

Abstract: Systems and methods for determining muscle force are presented. Proportional, integral, and derivative control is used to simulate muscle forces for multiple muscles contributing to a kinematic profile. The simulated muscle forces arc modified by dynamic gains that are calculated in order to achieve a muscle recruitment objective such as minimizing collective muscle effort while still achieving the kinematic motion.

Abstract: A system and method for automatic formal verification of an executable system includes an assertion monitor configured to verify a system against an assertion in the specification. The assertion monitor includes a parser configured to generate a propositional formula representing an assertion in the specification using Boolean propositions, a filter configured to generate a trace of truth assignments for the propositional symbols and a trace verifier configured to verify the assertion using the trace of truth assignments for the propositional symbols and the propositional formula.