A disclosed system includes a physically plausible virtual runtime environment to simulate a real-life environment for a simulated robot and a test planning and testing component to define a robotic task and generate virtual test cases for the robotic task. The test planning and testing component is further operable to generate virtual test cases for the robotic task, determine a control strategy for executing the virtual test cases, and create the physics-based simulated environment. The system further includes a robot controller operable to execute the virtual test cases in parallel in the physics-based simulated environment, measure a success of the execution, and store training and validation data to a historical database to train a machine learning algorithm. The robot controller may continuously execute the virtual test cases and use the machine learning algorithm to adjust parameters of the control strategy until optimal test cases are determined.

Disclosed is a neural network structure enabling efficient training of the network and a method thereto. The structure is a ladder-type structure wherein one or more lateral input(s) is/are taken to decoding functions. By minimizing one or more cost function(s) belonging to the structure the neural network structure may be trained in an efficient way.

This disclosure is directed to an apparatus for intelligent matching of disparate input signals received from disparate input signal systems in a complex computing network for establishing targeted communication to a computing device associated with the intelligently matched disparate input signals.

This disclosure is directed to an apparatus for intelligent matching of disparate input signals received from disparate input signal systems in a complex computing network for establishing targeted communication to a computing device associated with the intelligently matched disparate input signals.

In a general aspect, a quantum information processing circuit is analyzed. In some implementations, a linear response function of a quantum information processing circuit is generated. A linear circuit model is generated based on the linear response function. A composite circuit model is generated by combining the linear circuit model and a nonlinear circuit model. An operating parameter of the quantum information processing circuit is computed by solving the composite circuit model. In some implementations, an electromagnetic structure solver determines the linear response function based on a circuit specification, a quantum circuit analysis tool calculates the operating parameters, and the circuit specification is modified based on the operating parameters.

The present design is directed to a cognitive system including a receiver configured to receive a set of actors and associated actor information and receive assets and their associated asset information, a creation apparatus configured to create data dictionary entries for a taxonomy based on the set of actors and the assets and create a cognitive model using the data dictionary entries for a time period, and a computing apparatus configured to compute trust of the cognitive model as a fuzzy number and activate the cognitive model if trust of the cognitive model is above a cognitive model trust threshold. When the cognitive model is activated, the cognitive modeling system is configured to schedule a collection of tasks to run that perform regular extraction of actions from an original data source and perform at least one anomaly analysis associated with the cognitive model.

Methods, systems, and apparatus for solving optimization tasks. In one aspect, a system includes one or more classical processors and one or more quantum computing resources, wherein the one or more classical processors and one or more quantum computing resources are configured to perform operations comprising receiving input data comprising data specifying a computational task to be solved; processing the received input data using a first quantum computing resource to generate data representing a reduced computational task, wherein the reduced computational task has lower dimensionality that the computational task; and processing the data representing the reduced computational task to obtain a solution to the computational task.

Methods, systems, and apparatus for solving optimization tasks. In one aspect, a system includes one or more classical processors and one or more quantum computing resources, wherein the one or more classical processors and one or more quantum computing resources are configured to perform operations comprising receiving input data comprising data specifying a computational task to be solved; processing the received input data using a first quantum computing resource to generate data representing a reduced computational task, wherein the reduced computational task has lower dimensionality that the computational task; and processing the data representing the reduced computational task to obtain a solution to the computational task.

The attention recognition embodied by the method for evaluating the concentration ability of a sensory assessor is organically combined with evaluations for three categories of ranking capability, namely, excellent, good, and poor. Therefore, sensory assessors displaying high sensibility and poor attention form part of the group possessing excellent ranking capability, while sensory assessors exhibiting moderate sensibility and high attention can be found in the group possessing good ranking capability. Furthermore, sensory assessors displaying fair sensibility and high attention can be found in the group with poor ranking capability. This system can identify the concentration ability of assessors, therefore, providing support for the reliability of ranking results.

Efficient hardware design of the fuzzy-inference engine has become necessary for high-performance applications. The disclosed technology applies unary processing to the platform of fuzzy-logic. To mitigate the latency, the proposed design processes right-aligned bit-streams. A one-hot decoder is used for fast detection of the bit-stream with maximum value. Implementing a fuzzy-inference engine with 81 fuzzy-inference rules, the disclosed architecture provides 82%, 46%, and 67% saving in the hardware area, power and energy consumption, respectively, and 94% reduction in the number of used LUTs compared to conventional binary implementation.