Abstract: Techniques for use in connection with performing optimization using an objective function. The techniques include using at least one computer hardware processor to perform: identifying, using an integrated acquisition utility function and a probabilistic model of the objective function, at least a first point at which to evaluate the objective function; evaluating the objective function at least at the identified first point; and updating the probabilistic model of the objective function using results of the evaluating to obtain an updated probabilistic model of the objective function.

Abstract: Techniques for use in connection with performing optimization using a plurality of objective functions associated with a respective plurality of tasks. The techniques include using at least one computer hardware processor to perform: identifying, based at least in part on a joint probabilistic model of the plurality of objective functions, a first point at which to evaluate an objective function in the plurality of objective functions; selecting, based at least in part on the joint probabilistic model, a first objective function in the plurality of objective functions to evaluate at the identified first point; evaluating the first objective function at the identified first point; and updating the joint probabilistic model based on results of the evaluation to obtain an updated joint probabilistic model.

Abstract: Embodiments of the invention relate to a neural network circuit comprising a memory block for maintaining neuronal data for multiple neurons, a scheduler for maintaining incoming firing events targeting the neurons, and a computational logic unit for updating the neuronal data for the neurons by processing the firing events. The network circuit further comprises at least one permutation logic unit enabling data exchange between the computational logic unit and at least one of the memory block and the scheduler. The network circuit further comprises a controller for controlling the computational logic unit, the memory block, the scheduler, and each permutation logic unit.

Abstract: A method and system for analysis of data, including creating a first node, determining a first hyper-cube for the first node, determining whether a sample resides within the first hyper-cube. If the sample does not reside within the first hyper-cube, the method includes determining whether the sample resides within a first hyper-sphere, wherein the first hyper-sphere has a radius equal to a diagonal of the first hyper-cube.

Abstract: A method can include receiving data associated with a geologic environment; based on at least a portion of the data, estimating relationships for multiple properties of the geologic environment; and based at least in part on the relationships, performing simultaneous joint inversion for at least one property of the geologic environment.

Abstract: This patent specification relates to apparatus, systems, methods, and related computer program products for providing home security objectives, such as calculating a security score for a home. More particularly, this patent specification relates to a plurality of devices, including intelligent, multi-sensing, network-connected devices, that communicate with each other and/or with a central server or a cloud-computing system to provide any of a variety of useful home security objectives, such as calculating a security score for a home.

Abstract: Methods and system for providing information selected from a large set of digital content to at least one user. One such method comprises receiving user context information associated with the at least one user and identifying or generating, using at least one processor executing stored program instructions, a first concept in a semantic network, the first concept representing at least a portion of the user context information.

Abstract: At least one aspect of the invention is directed to a power monitoring system including a generator coupled to a fuel tank, a plurality of monitors, and a processor configured to monitor one or more loads drawing power from the generator; monitor one or more parameters that affect the amount of power drawn by the one or more loads; monitor a fuel consumption rate of the generator; generate one or more load profiles for each of the one or more loads; receive a set of the one or more loads for which a predicted time is to be generated; receive values for the one or more parameters; generate a predicted load profile for the set of the one or more loads and the values of the one or more parameters; receive information indicating an amount of remaining fuel; and calculate a predicted available run time.

Abstract: A tool and technique are employed to distill and prioritize multiple organizational (sub-system) capabilities based on ranked customer requirements or desires. A translational matrix is employed to organize preferences of the sub-system with respect to the customer requirements or desires. Relative importance scores are input into the matrix to reflect prioritized input versus prioritized capabilities. An adjusted relative importance score of the sub-system is automatically calculated and a resultant re-prioritization of sub-system attributes is created for application to new designs, services, or processes.

Abstract: A tool and technique are employed to distill and prioritize multiple organizational (sub-system) capabilities based on ranked customer requirements or desires. A translational matrix is employed to organize preferences of the sub-system with respect to the customer requirements or desires. Relative importance scores are input into the matrix to reflect prioritized input versus prioritized capabilities. An adjusted relative importance score of the sub-system is automatically calculated and a resultant re-prioritization of sub-system attributes is created for application to new designs, services, or processes.

Abstract: Systems, methods and aspects, and embodiments thereof relate to unsupervised or semi-supervised features learning using a quantum processor. To achieve unsupervised or semi-supervised features learning, the quantum processor is programmed to achieve Hierarchal Deep Learning (referred to as HDL) over one or more data sets. Systems and methods search for, parse, and detect maximally repeating patterns in one or more data sets or across data or data sets. Embodiments and aspects regard using sparse coding to detect maximally repeating patterns in or across data. Examples of sparse coding include L0 and L1 sparse coding. Some implementations may involve appending, incorporating or attaching labels to dictionary elements, or constituent elements of one or more dictionaries. There may be a logical association between label and the element labeled such that the process of unsupervised or semi-supervised feature learning spans both the elements and the incorporated, attached or appended label.

Abstract: An NFA hardware engine includes a pipeline and a controller. The pipeline includes a plurality of stages, where one of the stages includes a transition table. Both a first automaton and a second automaton are encoded in the same transition table. The controller receives NFA engine commands onto the NFA engine and controls the pipeline in response to the NFA engine commands.

Abstract: A method, computer-readable storage device and apparatus for calculating a health quality measure are disclosed. For example, a method receives characteristics of motion information, wherein the characteristics of motion information is based upon gait information, monitors the characteristics of motion information over a time period to determine a plurality of different modes of motion within the time period, and calculates the health quality measure based upon the plurality of different modes of motion.

Abstract: The information matching apparatus includes: a training data setting unit that sets supervised data in a machine learning device of supervised learning that learns judgment criteria used for a judgment of identicalness, similarity, and relevance between a plurality of records by matching the records configured by sets of values corresponding to items; a check point setting unit that sets a check point configured by one set of two records used for evaluating the set supervised data; and a learning result evaluation unit, for the set check point, acquires a change between a judgment result using judgment criteria derived as a result of learning based on set first supervised data and a judgment result using judgment criteria derived as a result of learning based on set second supervised data set and evaluates the supervised data based on the acquired change.

Abstract: Methods and apparatus are provided for using a breakpoint determination unit to examine an artificial nervous system. One example method generally includes operating at least a portion of the artificial nervous system; using the breakpoint determination unit to detect that a condition exists based at least in part on monitoring one or more components in the artificial nervous system; and at least one of suspending, examining, modifying, or flagging the operation of the at least the portion of the artificial nervous system, based at least in part on the detection.

Abstract: Embodiments of the present invention provide a memristor having a first electrode, a second electrode and a memristive layer arranged between the first electrode and the second electrode. Thereby, the memristor is adapted to obtain an asymmetrical current density distribution in the memristive layer.

Abstract: Systems and methods for the annotation of source data using confidence labels in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining confidence labels for crowdsourced annotations includes obtaining a set of source data, obtaining a set of training data representative of the set of source data, determining the ground truth for each piece of training data, obtaining a set of training data annotations including a confidence label, measuring annotator accuracy data for at least one piece of training data, and automatically generating a set of confidence labels for the set of unlabeled data based on the measured annotator accuracy data and the set of annotator labels used.

Abstract: A method of building a soft linkage between a plurality of graphs includes initializing a correspondence between type-1 and type-2 objects in the plurality of graphs, and reducing a cost function by alternately updating the type-1 correspondence and updating the type-2 correspondence.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using recurrent neural networks to analyze health events. One of the methods includes: processing each of a plurality of initial temporal sequences of health events to generate, for each of the initial temporal sequences, a respective network internal state of a recurrent neural network for each time step in the initial temporal sequence; storing, for each of the initial temporal sequences, one or more of the network internal states for the time steps in the temporal sequence in a repository; obtaining a first temporal sequence; processing the first temporal sequence using the recurrent neural network to generate a sequence internal state for the first temporal sequence; and selecting one or more initial temporal sequences that are likely to include health events that are predictive of future health events in the first temporal sequence.

Abstract: A method for inferring, without supervision, information about a data set and/or recognizers that are operated thereon. The recognizers are modules that are capable of analyzing, interpreting and labeling raw data of the data set with a label, which is a cognitive or substance-based identifier of the data, for instance, identifying peaks, troughs, patterns and trends of particular significance. The method infers the information about the data set and/or the recognizers based on the observable outputs of each recognizer and a mathematical means of reconciling the agreement/disagreement of the outputs. The method operates without need for knowledge of the correct label to be applied to the data set by each of the recognizers, such as a test set or prior knowledge of the accuracy of the recognizer.