Abstract: One embodiment provides a method comprising extracting natural language content from a piece of communication for a user, generating a representation of the piece of communication based on the natural language content extracted, and utilizing a global deep learning model and a personalized learning model for the user to assign a priority label to the piece of communication based on the representation and user behavioral information associated with recent conversations of the user. Another embodiment provides a method comprising, for each piece of communication of a set of multiple pieces of communication for multiple users, extracting natural language content from the piece communication and generating a representation of the piece of communication based on the natural language extracted, and training a deep learning neural network to predict a degree of priority of a subsequent piece of communication based on each representation generated.

Abstract: A neuromorphic device may include a pre-synaptic neuron, a row line extending from the pre-synaptic neuron in a row direction, a post-synaptic neuron, a column line extending from the post-synaptic neuron in a column direction, and a synapse coupled between the row line and the column line. The synapse may be disposed in an intersection region between the row line and the column line. The post-synaptic neuron may include a subtracting circuit.

Abstract: In a method for handling a plurality of heuristics for task selection in a genetic algorithm, a task scheduling engine generates a population of tasks associated with an overall objective, identifies multiple jobs associated with an overall objective, compiles the multiple jobs into a genome, and assigns one or more tasks to each job of the multiple jobs. The task scheduling engine also assigns a task heuristic byte defining multiple task heuristics that can be applied to the each job of the genome, randomly assigns a task heuristic from the multiple task heuristics to the each job, and determines a value score for the genome.

Abstract: In an approach to localizing tree-based convolutional neural networks, a method includes creating a first tree-based convolution layer (TBCL) corresponding to a tree, where the tree includes a first plurality of nodes and a node that has been indicated to be a first pivotal node. The first TBCL includes a second plurality of nodes and a second pivotal node having a feature vector based on node data from the first pivotal node. The method also includes creating a second TBCL corresponding to the tree. The second TBCL may include a third plurality of nodes. The method further includes determining a feature vector a third pivotal node in the third plurality of nodes based on the feature vectors from: (i) the second pivotal node, (ii) a parent node of the second pivotal node, and (iii) a child node of the second pivotal node.

Abstract: A method for generating an output comprising one or more segments includes obtaining a plurality of profiles derived from unstructured data associated with a plurality of users, wherein a given one of the profiles corresponds to a respective one of the users; repetitively executing at least one machine learning technique on the plurality of profiles, each execution producing a respective set of one or more segments from the plurality of profiles; generating a complete graph by performing pairwise comparisons between sets of segments from respective executions; applying at least one persistency graph algorithm to the complete graph to find one or more coherent clusters comprising one or more segments that are persistent across the repetitive executions of the machine learning technique; and producing the output at least in part by selecting at least one of the segments from at least one of the coherent clusters.

Abstract: A system for identifying a causal link, the system including a diagnostic generator module configured to receive a first user symptom datum, receive diagnostic training data, and generate using a supervised machine-learning process a diagnostic model that outputs a first prognosis. The system includes a prognostic chaining module configured to receive an expert input dataset, receive the first user symptom datum and the first prognosis, generate a gaussian mixture clustering model and identify a first causal link chained to the first prognosis. The system includes a causal link module configured to receive the first prognosis chained to the first causal link, receive a second prognosis chained to a second causal link, and evaluate the first causal link and the second causal link to calculate a degree of similarity between the first causal link and the second causal link.

Abstract: A method for generating an output comprising one or more segments includes obtaining a plurality of profiles derived from unstructured data associated with a plurality of users, wherein a given one of the profiles corresponds to a respective one of the users; repetitively executing at least one machine learning technique on the plurality of profiles, each execution producing a respective set of one or more segments from the plurality of profiles; generating a complete graph by performing pairwise comparisons between sets of segments from respective executions; applying at least one persistency graph algorithm to the complete graph to find one or more coherent clusters comprising one or more segments that are persistent across the repetitive executions of the machine learning technique; and producing the output at least in part by selecting at least one of the segments from at least one of the coherent clusters.

Abstract: A technology is provided for automated tuning of a machine learning model in a computing service environment. Predictive weights that include false positive outcomes, false negative outcomes, true positive outcomes, and true negative outcomes may be defined and/or received. A weight adjusted classification threshold, for use in a classification model of the machine learning model in a service provider environment, according to the predictive weights to enable the machine learning model to increase the total value of the machine learning model and decrease performance outcome errors. The improved classification threshold may be adjusted according to a change in the predictive weights. A data point may be classified according to the weight adjusted classification threshold in the classification model.

Abstract: Some aspects are directed to a method of operating an apparatus, the apparatus comprising a first quantum system having a plurality of coherent quantum states, the first quantum system being coupled to a second quantum system, the method comprising providing an input energy signal to the second quantum system that stimulates energy transfer between the first quantum system and the second quantum system and that causes net dissipation of energy to be output from the second quantum system, wherein the input energy signal includes at least two components having different frequencies and each having an amplitude and a phase, and adiabatically varying the amplitude and the phase of the at least two components of the input energy signal to cause a change in one or more of the plurality of coherent quantum states of the first quantum system.

Abstract: A method includes generating, by a processor of a computing device, an output set of models corresponding to a first epoch of a genetic algorithm and based on an input set of models of the first epoch. The input set and the output set includes data representative of a neural network. The method includes determining a particular model of the output set based on a fitness function. A first topological parameter of a first model of the input set is modified to generate the particular model of the output set. The method includes modifying a probability that the first topological parameter is to be changed by a genetic operation during a second epoch of the genetic algorithm that is subsequent to the first epoch. The method includes generating a second output set of models corresponding to the second epoch and based on the output set and the modified probability.

Abstract: A method for relative temperature preference learning is described. In one embodiment, the method includes identifying one or more current settings of a thermostat located at a premises, identifying one or more current indoor and outdoor conditions, calculating a current indoor differential between the current indoor temperature and the current target temperature, calculating a current outdoor differential between the current outdoor temperature and the current target temperature, and learning temperature preferences based on an analysis of the one or more current indoor conditions and the one or more current outdoor conditions. The one or more current settings of the thermostat include at least one of a current target temperature, current runtime settings, and current airflow settings.

Abstract: An apparatus and method are provided for a managed knowledge network platform (KNP). Model dissimilarity values for model pairs are obtained, each model pair including a first model of a plurality of models in a KNP and a different model in the plurality of models. Path lengths between a first model node of a plurality of model nodes in the KNP and each one of other model nodes are computed, where the first model node represents the first model and the first model node is connected to a first user node of a plurality of user nodes representing users of the KNP. At least one of the different models is selected based on the model dissimilarity values and the path lengths. A recommendation that includes the at least one model is generated for a first user represented by the first user node.

Abstract: A plurality of different hosted services each includes enabling logic that enables a set of actions. Usage data for a plurality of different tenants are accessed and actions are grouped into features based upon underlying enabling logic. A correlation score between features is identified based on tenant usage data for those features. A tenant under analysis is selected and usage data for the tenant under analysis is used to identify related features that the tenant under analysis is not using, based upon the correlation scores for the features. An output system is controlled to surface the related features for the tenant under analysis.

Abstract: An analytic system provides direct functional principal component analysis. (A) A next group variable value is selected from values of a group variable. (B) Explanatory variable values of observations having the selected next group variable value are sorted in ascending order. (C) The response variable value associated with each sorted explanatory variable value is stored in a next row of a data matrix. (D) (A) through (C) are repeated. (E) An eigenfunction index is incremented. (F) An FPCA is performed using the data matrix to define an eigenfunction for the eigenfunction index. (G) (E) and (F) are repeated. (H) FPCA results from the performed FPCA are presented within a window of a display. The FPCA results include an eigenvalue and an eigenfunction associated with the eigenvalue for each functional principal component identified from the performed FPCA in (F).

Abstract: A system and method for a neural network that is trained to recognize patterns in the exitance convergence behaviour of a radiosity equation being solved for a set of finite element environments, and subsequently employed to monitor and predict the exitance convergence behaviour of novel finite element environments. The neural network is trained with feature vectors representing partial snapshots of exitance vectors at various iterations in a radiosity calculation. The feature vectors are related to numbers of iterations that can be skipped by making approximate calculations instead of performing the iterations. In use, when a radiosity equation is being solved, the neural network identifies feature vectors generated during the calculations that signify that a certain number of iterations can be skipped by making an approximate calculation.

Abstract: A computer-implemented system and method for predicting activity outcome based on user attention is provided. Sensor data is collected for a user, and an activity performed by a user is identified based on the sensor data. Features of the user are collected while performing the activity and a subject of focus of the user is determined based on the collected features. An outcome of the activity performed by the user is predicted based on the features and the determined subject of focus.

Abstract: Data integration process/tool refinement and correction of rejected data. A method acquires rejected data from a data integration tool, the rejected data rejected by the data integration tool during a data integration process. The method applies machine learning to a cognitive system, the machine learning being based at least in part on at least some of the acquired rejected data, and the machine learning including training the cognitive system to identify corrections to data elements to facilitate data element acceptance by the data integration tool. The method analyzes a data element of the acquired rejected data and identifies a correction to apply to the data element. The method applies the correction to the data element to obtain a corrected data element. The method also provides the corrected data element to the data integration tool for acceptance by the data integration tool and provision to a target.

Abstract: A system of classifying devices and/or app instances a new or returning divides attributes generated from observations received from an uncharacterized device/software app into base-fingerprint attributes and predictor attributes, where the two kinds of attributes have different longevities. Predictor attribute tuples from attribute tuples having the same base fingerprint as the base fingerprint corresponding to the uncharacterized device/app, and the predictor attribute tuple corresponding to the uncharacterized device/app are analyzed using a machine learned predictor function to obtain a final fingerprint. Machine learning techniques such as logistic regression, support vector machine, and artificial neural network can provide a predictor function that can decrease the conflict rate of the final fingerprint and, hence, the utility thereof, without significantly affecting the accuracy of classification.

Abstract: A method and system are provided. The method includes receiving by a computer having a processor and a memory, sequence data that includes labeled data and unlabeled data. The method further includes generating, by the computer having the processor and the memory, a recurrent neural network model of the sequence data, the recurrent neural network model having a recurrent layer and an aggregate layer. The recurrent neural network model feeds sequences generated from the recurrent layer into the aggregate layer for aggregation, stores temporal dependencies in the sequence data, and generates labels for at least some of the unlabeled data.

Abstract: The technology disclosed determines which field values in a set of unique field values for a particular field in a fielded dataset are anomalous using six similarity measures. A factor vector is generated per similarity measure and combined to form an input matrix. A convolutional neural network processes the input matrix to generate evaluation vectors. A fully-connected network evaluates the evaluation vectors to generate an anomaly scalar for a particular unique field value. Thresholding is applied to anomaly scalar to determine whether the particular unique field value is anomalous.