Abstract: A web-based hierarchical temporal memory (HTM) system in which one or more client devices communicate with a remote server via a communication network. The remote server includes at least a HTM server for implementing a hierarchical temporal memory (HTM). The client devices generate input data including patterns and sequences, and send the input data to the remote server for processing. The remote server (specifically, the HTM server) performs processing in order to determine the causes of the input data, and sends the results of this processing to the client devices. The client devices need not have processing and/or storage capability for running the HTM but may nevertheless take advantage of the HTM by submitting a request to the HTM server.

Abstract: A method of generating mapping dictionaries for neural networks may be provided. A method may include receiving, at a current layer, encoded activation addresses from a previous layer and encoded weight addresses. The method may also include decoding the encoded activation addresses to generate decoded activation addresses, and decoding the encoded weight addresses to generate decoded weight addresses. Further, the method may include generating original activation addresses from the decoded activation addresses and the decoded weight addresses. Moreover, the method may include matching the original activation addresses to a mapping dictionary to generate encoded activation addresses for the current layer.

Abstract: A holistic hospital patient care and management system comprises a data store to receive and store patient data including clinical and non-clinical data; a plurality of RFID sensors to detect a plurality of RFID tags associated with patients to enable real-time tracking of location; at least one predictive model including a plurality of weighted risk variables and risk thresholds in consideration of the clinical and non-clinical data and configured to identify at least one medical condition associated with the patients; a risk logic module configured to apply at least one predictive model to the clinical and non-clinical data to determine at least one risk score associated with each patient; a patient monitoring logic module configured to receive location data of the patients and determine patient real-time location and status; and a user interface module configured to display patient medical condition, risk score, location, and status information to authorized personnel.

Abstract: Systems, methods, and computer program products are provided for space-time complexity speedup for computer algorithms in terms of processing time as a function of dataset size and type, showing serial, sub-linear, linear and superlinear speedup. Serial and parallel effects can be processed and calculated from a decomposed algorithm's sequential functional components, allowing the identification and use of the parallelizable components.

Abstract: A technique for transforming questions for a question answering (QA) system includes clustering received questions by contextual accordance into a question set. A base lexical answer type (LAT) for the question set is determined. LATs of discordant questions in the question set are transformed to the base LAT. Answers for the questions in the question set are aggregated. The aggregated answers are then displayed.

Abstract: A method for acquiring an input value such that an output value differs before and after refactoring is not known and an information processing apparatus searching for such an input value that inequivalent output values can be brought about for the equivalent input values among multiple target programs. The apparatus including: an acquisition section acquiring, for each of the multiple target programs, an input/output constraint showing a condition to be satisfied by an input value and an output value; a constraint condition generating section generating a constraint condition which becomes true when the multiple input/output constraints for the multiple target programs are satisfied, input values for the multiple target programs are equivalent, and output values for the multiple target programs are not equivalent; and a constraint releasing section giving the constraint condition to a constraint solver to obtain an input value satisfying the constraint condition.

Abstract: Systems, device and techniques are disclosed for an information discovery system. An element of data may be retrieved. A knowledge point may be extracted from the element of data. The knowledge point may include an aspect of the element of data. The element of data and the knowledge point may be linked with a traversable link. The knowledge point may further be linked to a second element of data. Natural language processing analysis, linguistic analysis, sentiment analysis, and metadata analysis, may be used to determine the aspect of the element of data.

Abstract: Example systems and methods of developing a learning model are presented. In one example, a sample data set to train a first learning algorithm is accessed. A number of states for each input of the sample data set is determined. A subset of the inputs is selected, and the sample data set is partitioned into a number of partitions equal to a combined number of states of the selected inputs. A second learning algorithm is created for each of the partitions, wherein each second learning algorithm receives the unselected inputs. Each of the second learning algorithms is assigned to a processor and trained using the samples of the partition corresponding to that algorithm. Decision logic is generated to direct each of a plurality of operational data units as input to one of the second learning algorithms based on states of the selected inputs of the operational data unit.

Abstract: Semantic entity relation detection classifier training implementations are presented that are generally used to train a semantic entity relation detection classifier to identify relations expressed in a natural language query. In one general implementation, queries are found in a search query click log that exhibit relations and entity types found in a semantic knowledge graph. Explicit relations are inferred from the found queries and an explicit relations data set is generated that includes queries associated with the inferred explicit relations. In addition, implicit relations are inferred from the found queries and an implicit relations data set is generated that includes queries associated with the inferred implicit relations. A semantic entity relation detection classifier is then trained using the explicit and implicit data sets.

Abstract: Quantum processor based techniques minimize an objective function for example by operating the quantum processor as a sample generator providing low-energy samples from a probability distribution with high probability. The probability distribution is shaped to assign relative probabilities to samples based on their corresponding objective function values until the samples converge on a minimum for the objective function. Problems having a number of variables and/or a connectivity between variables that does not match that of the quantum processor may be solved. Interaction with the quantum processor may be via a digital computer. The digital computer stores a hierarchical stack of software modules to facilitate interacting with the quantum processor via various levels of programming environment, from a machine language level up to an end-use applications level.

Abstract: An information processing apparatus includes a history acquisition section configured to acquire history data including a history indicating that a plurality of selection subjects have selected selection objects; a learning processing section configured to allow a choice model to learn a preference of each selection subject for a feature and an environmental dependence of selection of each selection object in each selection environment using the history data, where the choice model uses a feature value possessed by each selection object, the preference of each selection subject for the feature, and the environmental dependence indicative of ease of selection of each selection object in each of a plurality of selection environments to calculate a selectability with which each of the plurality of selection subjects selects each selection object; and an output section configured to output results of learning by the learning processing section.

Abstract: Embodiments disclosed herein provide systems and methods for the filtering, selection and presentation of vendors accounting for both user characteristics and vendor characteristics, such that the systems and methods may be used by both customer and vendor alike to better match customer needs with the resource-constrained vendors with whom a successful sale has a higher probability of occurring. Embodiments may include filtering, selecting and/or presenting vendors to a user sorted by the probability that the particular vendor will possess the characteristics that appeal to a particular customer and therefore result in a large probability of sale and suppress presentation of those vendors that are unlikely to be selected by the customer since their characteristics are less consistent with those needed by the customer and, therefore, are unlikely to result in a sale.

Abstract: Certain examples provide systems and methods to identify placement for an electric charging station infrastructure. Certain examples provide systems and methods to generate a deployment plan for one or more electric vehicle charging stations. An example method includes gathering data for a specified geographic area and forecasting a demand for electric vehicles for the specified area. The example method includes modeling driving patterns in the specified area using available data and improving a charging infrastructure model based on the driving pattern and demand forecast information for the specified area. The example method includes generating and providing a recommendation regarding an electric vehicle charging infrastructure and deployment strategy for the specified area based on the improved charging infrastructure model.

Abstract: Presented are a system, method, and apparatus for automatic topic relevant content filtering from social media text streams using weak supervision. A computing device utilizes heuristic rules allowing topic filtering and a data stream data chunk identifier. A plurality of messages are transmitted as streaming message data from a social media network in real-time. The messages are split into a plurality of data stream data chunks according to the data stream data chunk identifier. A rule-based labeled data set L0 is built from one or more data instances in the first stream data chunk. An initial classifier is built based upon features of L0. The initial classifier is applied to a next data stream data chunk to build a labeled data set L1. A subset of representative instances S1 is selected from labeled data set L1. A first representative classifier C1 is constructed from representative instance S1.

Abstract: Respective correlation metrics between token groups of a particular text attribute of a data set and a prediction target attribute are computed. Based on the correlation metrics, a predictive token group list is created. For various observation records of the data set, values of a derived categorical attribute corresponding to the particular text attribute are determined based on matches between the particular text attribute value and the predictive token group list. A measure of the predictive utility of the particular text attribute is obtained using correlations between the categorical attribute and the prediction target attribute.

Abstract: System, method, and computer program product to determine that a question received by a deep question answering system is speculative, generate, by one or more predictive algorithms, a set of candidate answers, compute a score for each candidate answer in the set of candidate answers, and return a first candidate answer, of the set of candidate answers, as responsive to the speculative question received by the deep question answering system.

Abstract: One embodiment provides an apparatus. The apparatus includes a wearable device. The wearable device includes a knowledge base, a user interface and automatic response logic. The knowledge base includes at least one data structure. Each data structure includes a plurality of ranked possible user responses. The automatic response logic is to select one data structure of the at least one data structure in response to a received communication. The selecting is based, at least in part, on an event type and based, at least in part, on a contact identifier. The communication is received from a communication partner device via a companion device. The automatic response logic is further to provide at least one ranked possible user response from the selected data structure to a user via the user interface.

Abstract: A first data set corresponding to an evaluation run of a model is generated at a machine learning service for display via an interactive interface. The data set includes a prediction quality metric. A target value of an interpretation threshold associated with the model is determined based on a detection of a particular client's interaction with the interface. An indication of a change to the prediction quality metric that results from the selection of the target value may be initiated.

Abstract: The Quanton virtual machine approximates solutions to NP-Hard problems in factorial spaces in polynomial time. The data representation and methods emulate quantum computing on classical hardware but also implement quantum computing if run on quantum hardware. The Quanton uses permutations indexed by Lehmer codes and permutation-operators to represent quantum gates and operations. A generating function embeds the indexes into a geometric object for efficient compressed representation. A nonlinear directional probability distribution is embedded to the manifold and at the tangent space to each index point is also a linear probability distribution. Simple vector operations on the distributions correspond to quantum gate operations. The Quanton provides features of quantum computing: superpositioning, quantization and entanglement surrogates. Populations of Quantons are evolved as local evolving gate operations solving problems or as solution candidates in an Estimation of Distribution algorithm.

Abstract: A method for providing healthcare optimized cognitive insights comprising: receiving data from a plurality of data sources, at least some of the plurality of data sources comprising healthcare relevant data sources; processing the data from the plurality of data sources to provide cognitively processed insights; performing a learning operation to iteratively improve the cognitively processed insights over time; and, providing the cognitively processed healthcare relevant insights to a destination.