Abstract: In an embodiment, a method for optimizing computer machine learning includes receiving an optimization goal. The optimization goal is used to search a database of base option candidates (BOC) to identify matching BOCs that at least in part matches the goal. A selection of a selected base option among the matching BOCs is received. Machine learning prediction model(s) are selected based at least in part on the goal to determine prediction values associated with alternative features for the selected base option, where the model(s) were trained using training data to at least identify weight values associated with the alternative features for models. Based on the prediction values, at least a portion of the alternative features is sorted to generate an ordered list. The ordered list is provided for use in manufacturing an alternative version of the selected base option with the alternative feature(s) in the ordered list.

Abstract: A method includes defining model attributes of an organizational machine model that organizes feedback data from one or more sources of the feedback data into plural different topic groups based on similarities in concepts expressed in the feedback data. The model attributes represent criteria for establishment of the organizational machine model and include a topic model number that defines how many of the different topic groups are to be created by the organizational machine model and used to organize the feedback data into, a hyperparameter optimization alpha value that defines how likely a feedback datum in the feedback data is to be included in a single topic group of the different topic groupings or multiple topic groups of the different topic groupings, and a hyperparameter optimization beta value that defines how broadly each of the different topic groups are defined relative to the feedback data.

Abstract: A method, system, and computer program product for weight adjusted composite model for forecasting in anomalous environments are provided in the illustrative embodiments. A base forecasting model and a second forecasting model are combined to form a composite model, the base forecasting model configured to forecast an event in a time series, the second forecasting model configured to represent an anomalous portion of data in the time series. A mixing algorithm is combined with the composite model to adjust a set of weights associated with the composite model. Upon identifying a future period in which the event is to be forecasted, using the mixing algorithm, a subset of the set of weights is adjusted to from a weight adjusted composite model. The weight adjusted composite model is executed to forecast the event in the future period.

Abstract: A learning apparatus includes at least one memory and at least one circuit. The circuit (a) obtains a first neural network that has learned by using source learning data and obtains target learning data, the target learning data including a plurality of first data items each of which is given a first label and a plurality of second data items each of which is given a second label, (b) obtains a plurality of first output vectors by inputting the plurality of first data items to a second neural network and obtains a plurality of second output vectors by inputting the plurality of second data items to the second neural network, and (c) generates a first relation vector corresponding to the first label by using the plurality of first output vectors and generates a second relation vector corresponding to the second label by using the plurality of second output vectors.

Abstract: Described herein is a framework for outdoor localization. In accordance with one aspect of the framework, a set of hotspot labels are received from one or more user devices connected to an outdoor wireless local area network. Manifold learning may be performed based on the set of hotspot labels to construct one or more manifolds. Using the one or more constructed manifolds, the framework may then estimate a location of a particular user device associated with a query record received from during an online location query.

Abstract: A computer-implemented method for determining the volume of activation of neural tissue. In one embodiment, the method uses one or more parametric equations that define a volume of activation, wherein the parameters for the one or more parametric equations are given as a function of an input vector that includes stimulation parameters. After receiving input data that includes values for the stimulation parameters and defining the input vector using the input data, the input vector is applied to the function to obtain the parameters for the one or more parametric equations. The parametric equation is solved to obtain a calculated volume of activation.

Abstract: A resistive memory cell is connected in circuitry which has a first input terminal for applying neuron input signals including a read portion and a write portion. The circuitry includes a read circuit producing a read signal dependent on resistance of the memory cell, and an output terminal providing a neuron output signal, dependent on the read signal in a first state of the memory cell. The circuitry also includes a storage circuit storing a measurement signal dependent on the read signal, and a switch set operable to supply the read signal to the storage circuit during application of the read portion of each neuron input signal to the memory cell, and, after application of the read portion, to apply the measurement signal in the apparatus to enable resetting of the memory cell to a second state.

Abstract: A method and system for assigning a multimedia content element to a user. The method includes generating at least one signature to the multimedia content element; determining, based on the generated at least one signature, whether the multimedia content element exists in a database; and assigning a unique identifier of the user to the multimedia content element, when it is determined that the multimedia content element does not exist in the database.

Abstract: Managing answers in a question-answering environment is disclosed. Managing answers in the question-answering environment can include sorting, based on a set of answer categories for a subject matter, a first set of answers into a first answer category and a second set of answers into a second answer category. Managing answers in the question-answering environment can include determining, using the subject matter, a first category sequence including the first answer category and the second answer category, and establishing, based on the first category sequence, a first answer sequence established from a portion of the first set of answers from the first answer category and a portion of the second set of answers from the second answer category.

Abstract: The disclosed embodiments relate to a technique for training a prognostic pattern-recognition system to detect incipient anomalies that arise during execution of a computer system. During operation, the system gathers and stores telemetry data obtained from n sensors in the computer system during operation of the computer system. Next, the system uses the telemetry data gathered from the n sensors to train a baseline model for the prognostic pattern-recognition system. The prognostic pattern-recognition system then uses the baseline model in a surveillance mode to detect incipient anomalies that arise during execution of the computer system. The system also uses the stored telemetry data to train a set of additional models, wherein each additional model is trained to operate with one or more missing sensors. Finally, the system stores the additional models to be used in place of the baseline model when one or more sensors fail in the computer system.

Abstract: Particular embodiments may locally store, at a client device of a first user, information associated with a plurality of nodes and a plurality of edges of a social graph received from a social-networking system. The first user may be associated with a first node of the social graph. The client device may detect that the first user is entering an input term. The client device may provide predictive typeahead results as the first user enters the input term. The predictive typeahead results may be based on the input term. Each predictive typeahead result may correspond to one of the nodes of the social graph stored locally on the client device. Each predictive typeahead result may include at least one image associated with the corresponding node.

Abstract: The current application is directed to intelligent controllers that use sensor output and electronically stored information to determine whether or not one or more types of entities are present within an area, volume, or environment monitored by the intelligent controllers. The intelligent controllers select operational modes and/or modify control schedules with respect to the presence and absence of the one or more entities. The intelligent controllers selectively carry out scheduled control operations during periods of time when one or more types of entities are determined not to be in a controlled environment.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an actor neural network used to select actions to be performed by an agent interacting with an environment. One of the methods includes obtaining a minibatch of experience tuples; and updating current values of the parameters of the actor neural network, comprising: for each experience tuple in the minibatch: processing the training observation and the training action in the experience tuple using a critic neural network to determine a neural network output for the experience tuple, and determining a target neural network output for the experience tuple; updating current values of the parameters of the critic neural network using errors between the target neural network outputs and the neural network outputs; and updating the current values of the parameters of the actor neural network using the critic neural network.

Abstract: A system and method for troubleshooting problems in complex systems using multiple knowledgebases comprises a first knowledgebase that has a case-based reasoning engine and knowledge from which a first set of possible solutions can be determined, and a second knowledgebase that has a case-based reasoning engine and knowledge from which a second set of possible solutions can be determined. The first knowledgebase pertains to a first equipment, and the second knowledgebase pertains to a second equipment. The second equipment is a component of the first equipment. A Federation Manager associated with the first knowledgebase transfers a case-based reasoning session between the first and second knowledgebases.

Abstract: A method of determining a set of prescribed actions includes receiving a configuration script identifying a set of influencers, a set of performance indicators, a model type, a target time, and a prescription method. The method further includes deriving a model of the model type based on data associated with the set of influencers or with the set of performance indicators. The method also includes projecting a set of future influencer values associated with the set of influencers and projecting a set of future indicator values of the set of performance indicators at the target time using the model. The method can further include prescribing using the prescription method and based on projecting using the model a set of prescribed actions associated with the subset of actionable influencers. The method also includes displaying the set of prescribed actions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a model using parameter server shards. One of the methods includes receiving, at a parameter server shard configured to maintain values of a disjoint partition of the parameters of the model, a succession of respective requests for parameter values from each of a plurality of replicas of the model; in response to each request, downloading a current value of each requested parameter to the replica from which the request was received; receiving a succession of uploads, each upload including respective delta values for each of the parameters in the partition maintained by the shard; and updating values of the parameters in the partition maintained by the parameter server shard repeatedly based on the uploads of delta values to generate current parameter values.

Abstract: Detecting propensity profile for a person may comprise receiving artifacts associated with the person; detecting profile characteristics for the person based on the artifacts; receiving a plurality of predefined profiles comprising a plurality of characteristics and relationships between the characteristics over time, each of the plurality of predefined profiles specifying an indication of propensity; matching the profile characteristics for the person with one or more of the plurality of predefined profiles; and outputting one or more propensity indicators based on the matching, the propensity indicators comprising at least an expressed strength of a given propensity in the person at a given time.

Abstract: Systems and methods are provided for a computer-implemented method of providing an interactive avatar that reacts to a communication from a communicating party. Data from an avatar characteristic table is provided to an avatar action model, where the avatar characteristic table is a data structure stored on a computer-readable medium that includes values for a plurality of avatar personality characteristics. A communication with the avatar is received from the communicating party. A next state for the avatar is determined using the avatar action model, where the avatar action model determines the next state based on the data from the avatar characteristic table, a current state for the avatar, and the communication. The next state for the avatar is implemented, and the avatar characteristic table is updated based on the communication from the communicating party, where a subsequent state for the avatar is determined based on the updated avatar characteristic table.

Abstract: Certain relationships representing material insights are identified from among a set of discovered relationships. Cognitive discovery of relationships in a knowledge base, or corpus, are ranked according to one or more metrics indicative of material insights, including recentness and degree of alignment.

Abstract: Systems and methods are provided for automatically scoring a constructed response. The constructed response is processed to generate a plurality of numerical vectors that is representative of the constructed response. A model is applied to the plurality of numerical vectors. The model includes an input layer configured to receive the plurality of numerical vectors, the input layer being connected to a following layer of the model via a first plurality of connections. Each of the connections has a first weight. An intermediate layer of nodes is configured to receive inputs from an immediately-preceding layer of the model via a second plurality of connections, each of the connections having a second weight. An output layer is connected to the intermediate layer via a third plurality of connections, each of the connections having a third weight. The output layer is configured to generate a score for the constructed response.