Abstract: A medical diagnosis support apparatus includes a training data obtaining unit that obtains training data, an inference means candidate creating unit that creates a plurality of inference means candidates based on the training data, an inference performance evaluation unit that evaluates the performance of the plurality of inference means candidates based on the training data, an information validity evaluation unit that evaluates the validity of information presented by each of the plurality of inference means candidates based on the training data, and an inference means selection unit that selects an inference means from the plurality of inference means candidates based on the performance of the plurality of inference means candidates and the validity of the information presented by each of the plurality of inference means candidates.

Abstract: Systems and methods are disclosed for using an additive context model for entity disambiguation. An example method may include receiving a span of text from a document and a phrase vector for the span. The phrase vector may have a quantity of features and represent a context for the span. The method also includes determining a quantity of candidate entities from a knowledge base that have been referred to by the span. For each of the quantity of candidate entities, the method may include determining a support score for the candidate entity for each feature in the phrase vector, combining the support scores additively, and computing a probability that the span resolves to the candidate entity given the context. The method may also include resolving the span to a candidate entity with a highest probability.

Abstract: Systems, methods, and computer program products to perform an operation comprising receiving a case, generating a set of candidate answers for the case, excluding a first candidate answer from the set of candidate answers upon determining that a first attribute in the case precludes returning the first candidate answer as a valid response to the case, and processing supporting evidence for the remaining candidate answers in the set of candidate answers.

Abstract: A model updating method is provided. The model updating method that is executed by a computer includes calculating a score that indicates a degree of normality or abnormality of each of a plurality of pieces of data by using as a judgment model each of the pieces of data, predicting as a predicted condition whether each of the pieces of data is normal or abnormal according to score, judging whether or not the predicted condition is correct for each of the plurality of pieces of data, calculating the accuracy rate for the predicted conditions of a top specified number of pieces of data in order of decreasing abnormality as indicated by the score when the plurality of pieces of data are arranged in a specified order of score, and judging whether or not it is necessary to update the judgment model according to the accuracy rate.

Abstract: The present disclosure is directed to systems and methods for identifying unknown drug targets via adverse event data. An analyzer receives an identification of a first drug having one or more unknown target proteins and identifies a second drug related to the first drug. The analyzer retrieves, from an adverse event database, a first side effect profile associated with the first drug, and a second side effect profile associated with the second drug. The analyzer generates a third side effect profile comprising a subset of the first side effect profile not shared by the second side effect profile, and identifies a third drug having a fourth side effect profile comprising the third side effect profile. The analyzer retrieves a list of one or more target proteins of the third drug not targeted by the second drug, and presents the retrieved list as potential target proteins of the first drug.

Abstract: Apparatus and methods of categorizing a subterranean formation including collecting data related to the formation, performing an algorithm comprising guided Bayesian survey design, identifying data observations to collect, and collecting additional data observations related to the formation. In some embodiments, the performing comprises forming a matrix or loading a matrix or both. Apparatus and methods of categorizing a subterranean formation including collecting data related to the formation, performing an algorithm comprising guided Bayesian survey design, identifying data observations to use, and processing the data observations wherein the observations require less processing time than if no algorithm were performed.

Abstract: A component in a graph-based computation having data processing components connected by linking elements representing data flows is updated by receiving a rule specification, generating a transform for transforming data based on the rule specification, associating the transform with a component in the graph-based computation, and in response to determining that a new rule specification has been received or an existing rule specification has been edited, updating the transform associated with the component in the graph-based computation according to the new or edited rule specification. A computation is tested by receiving a rule specification including a set of rule cases, receiving a set of test cases, each test case containing a value for one or more of the potential inputs, and for each test case, identifying one of the rule cases that will generate an output given the input values of the test case.

Abstract: A reasoning engine is disclosed. Contemplated reasoning engines acquire data relating to one or more aspects of various environments. Inference engines within the reasoning engines review the acquire data, historical or current, to generate one or more hypotheses about how the aspects of the environments might be correlated, if at all. The reasoning engine can attempt to validate the hypotheses through controlling acquisition of the environment data.

Abstract: A method for generating an event includes monitoring a first neural network with a second neural network. The method also includes generating an event based on the monitoring. The event is generated at the second neural network. The event may be generated based on a spike received at the second network during the monitoring.

Abstract: A hybrid ultracapacitor-battery energy storage system is integrated with a photovoltaic system to help solve fluctuations. A fuzzy-logic-based adaptive power management system enables optimization of the power/energy distributions and a filter-based power coordination layer serving as a rudimentary step for power coordination among the hybrid storage system and a fuzzy-logic-based control adjustment layer that keeps monitoring the operation status of all the energy storage devices, taking into account their dynamic characteristics, and fine-tuning the control settings adaptively.

Abstract: A clinical predictive and monitoring system comprising a data store operable to receive and store data associated with a plurality of patients selected from medical and health data; and a number of social, behavioral, lifestyle, and economic data; at least one predictive model to identify at least one high-risk patient associated with at least one medical condition; a risk logic module operable to apply the at least one predictive model to the patient data to determine at least one risk score associated the at least one medical condition and identify at least one high-risk patient; a data presentation module operable to present notification and information to an intervention coordination team about the identified at least one high-risk patient; and an artificial intelligence tuning module adapted to automatically adjust parameters in the predictive model in response to trends in the patient data.

Abstract: A reasoning engine is disclosed. Contemplated reasoning engines acquire data relating to one or more aspects of various environments. Inference engines within the reasoning engines review the acquire data, historical or current, to generate one or more hypotheses about how the aspects of the environments might be correlated, if at all. The reasoning engine can attempt to validate the hypotheses through controlling acquisition of the environment data.

Abstract: A flow processing facility, which uses a set of artificial neurons for pattern recognition, such as a self-organizing map, in order to provide security and protection to a computer or computer system supports unified threat management based at least in part on patterns relevant to a variety of types of threats that relate to computer systems, including computer networks. Flow processing for switching, security, and other network applications, including a facility that processes a data flow to address patterns relevant to a variety of conditions are directed at internal network security, virtualization, and web connection security. A flow processing facility for inspecting payloads of network traffic packets detects security threats and intrusions across accessible layers of the IP-stack by applying content matching and behavioral anomaly detection techniques based on regular expression matching and self-organizing maps.

Abstract: The disclosure includes use of a feature-aware propagation model to identify one or more features of a product and one or more person(s), or members of a social network, to target, or user, for marketing the product having the identified features. The one or more person(s) identified using the model may be the person(s), or member(s), of a social network determined to have a maximum capability, relative to other members of the social network, for influencing the members of the social network in adopting, e.g., purchasing, a product having the identified features. In addition, parameters of the model may be determined using information about the social network, user preferences, and the products and features of the products.

Abstract: Embodiments relate to managing and executing interpreted language code. An aspect includes a parser configured to parse controlled language code against both a language grammar and for building an execution model comprising parsed known concepts. Another aspect includes a pattern matching engine configured to match patterns in the controlled language code with a patterns for a new concept. Yet another aspect includes a concept engine configured to create a new concept from the matched pattern and controlled language code and for saving the new concept into an object model and into a language grammar so that current and future parsing errors will not occur when recognizing the new concept.

Abstract: Various implementations for assigning rules and creating rules using templates are described herein. In one example implementation, a model is determined, one or more components of the model are determined, a rule from a set of one or more predefined rules is determined, and the rule is assigned to the model. The rule has one or more parameters matching the one or more components of the model.

Abstract: A user activity real-time recognition apparatus and method are provided and include a collector configured to collect a frequency-domain signal for each user activity and to generate learning data based on the frequency-domain signal. The apparatus and method also include an extractor configured to extract a user activity feature from the frequency-domain signal based on an activity feature extracting model. The activity feature extracting model is learned based on the learning data from the collector. The apparatus and method further include a classifier configured to analyze the user activity feature to classify a user activity pattern based on an activity pattern classifying model and configured to transmit the classified user activity pattern to an application device.

Abstract: A method for automatically generating test scenarios for a plurality of business rules created to form a ruleset for a business domain. A plurality of decision variables is extracted from a business object model associated with the business domain using a computer system. The plurality of business rules is parsed using a syntactic parser operating on the computer system to extract at least one condition expression and an assignment action. A collection of mutually exclusive constraint satisfaction problems is built from the decision variables, condition expressions, and assignment actions using a constraint problem generator operating on the computer system. A plurality of test scenarios is generated automatically from each identified constraint satisfaction problem in the collection using a constraint solver engine operating on the computer system.

Abstract: Various embodiments of systems and methods to dynamically retrain prediction models based on real time transaction data are described herein. In one aspect, real time application data and status data associated with an entity are obtained. The obtained application data is inputted to a prediction model to produce an assessment of a risk. The obtained status data with the assessed risk are compared. When the obtained payment status data does not match the determined risk, the prediction model is retrained.

Abstract: Certain aspects of the present disclosure provide methods and apparatus for creating tags (static or dynamic) for input/output classes of a neural network model using supervised learning. The method includes augmenting a neural network model with a plurality of neurons and training the augmented network using spike timing dependent plasticity (STDP) to determine one or more tags.