Described is a method for predicting multiple components' content in a case that rare earth ions with and without color feature coexist, and relates to component content prediction in rare earth extraction process. It is difficult to quickly/accurately detect component's content in rare earth extraction process. Because of relatively large difference between images' color features of CePr/Nd mixed solution with colorless Ce ions and Pr/Nd solution, detecting content method of single rare earth element based on color feature is no longer applicable. The method includes: first searching for H and S components with maximum correlation with component content in HSI color space; establishing ELM based multi-component content soft measurement model using H and S component first-order moment as input; and for uncertainty of initial weight and ELM (extreme learning machine) model's threshold, optimizing model parameters using genetic algorithm GA to optimize ELM model for component content prediction higher precision.

A method performed by a first network node operating in a communications network is disclosed. The method is for handling a performance of a radio access network (RAN) including one or more radio network nodes. The first network node determines a configuration of one or more parameters in the RAN based on one or more machine-implemented reinforcement learning (RL) procedures to optimize the performance of the RAN based on the one or more parameters. The RL procedures are further based on at least one of: i) one or more physical characteristics of a deployment of the RAN, ii) one or more radio characteristics of the RAN, and iii) a location of users or traffic load in the RAN. The first network node then initiates providing one or more indicators of the determined configuration to a second network node.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a reservoir computing neural network. In one aspect there is provided a reservoir computing neural network comprising: (i) a brain emulation sub-network, and (ii) a prediction sub-network. The brain emulation sub-network is configured to process the network input in accordance with values of a plurality of brain emulation sub-network parameters to generate an alternative representation of the network input. The prediction sub-network is configured to process the alternative representation of the network input in accordance with values of a plurality of prediction sub-network parameters to generate the network output. The values of the brain emulation sub-network parameters are determined before the reservoir computing neural network is trained and are not adjusting during training of the reservoir computing neural network.

Methods and systems that provide one or more recommended configurations to planners using large data sets in an efficient manner. These methods and systems provide optimization of objectives using a genetic algorithm that can provide parameter recommendations that optimize one or more objectives in an efficient and timely manner. The methods and systems disclosed herein are flexible enough to satisfy diverse use cases.

Generally, the present disclosure is directed to a neural architecture search process for finding small and fast video processing networks for understanding of video data. The neural architecture search process can automatically design networks that provide comparable video processing performance at a fraction of the computational and storage cost of larger existing models, thereby conserving computing resources such as memory and processor usage.

A computing machine receives a real synthetic aperture radar (SAR) image including one or more targets. The real SAR image is one of a plurality of real SAR images in a training set. The computing machine generates, for the real SAR image, a model-based target shadow background (TSB) image using a three-dimensional (3D) model of the target. The computing machine generates, for the real SAR image and using an auto-encoder engine, an auto-encoder-generated TSB image using an artificial neural network (ANN). The computing machine computes, using a discriminator engine, an image difference between the auto-encoder-generated TSB image and the model-based TSB image. The computing machine adjusts weights in the auto-encoder engine based on the computed image difference.

A rider state modification system for improving a state of a rider in a vehicle includes a first neural network that operates to classify a state of the vehicle through analysis of information about the vehicle captured by an Internet-of-things device during operation of the vehicle. The rider state modification system further includes a second neural network that operates to optimize at least one operating parameter of the vehicle based on the classified state of the vehicle, information about a state of a rider occupying the vehicle, and information that correlates vehicle operation with an effect on rider state.

A method evolves generic computational building blocks. The method initializes a parent population with randomly generated programs or programs evolved by a genetic programming instance that uses randomized targets. The method also obtains a list of randomly generated test inputs. The method generates a target dataset that includes input-output pairs of randomly generated binary strings. The method also applies a fitness function to assign a fitness score to each program, based on the target dataset. The method grows a seed list by applying genetic operators, and selecting offspring that satisfy a novelty condition. The novelty condition is representative of an ability of a program to produce unique output for the list of randomly generated test inputs. The method iterates until a terminating condition has been satisfied. The terminating condition is representative of an ability of programs in the seed list to solve one or more genetic programming instances.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a representation of an image captured by an onboard camera of a drone and providing the representation of the image to a drone image processing neural network having a brain emulation sub-network with an architecture that is specified by synaptic connectivity between neurons in a brain of a biological organism, including instantiating a respective artificial neuron corresponding to each biological neuron of multiple biological neurons, and instantiating a respective connection between each pair of artificial neurons that correspond to a pair of biological neurons that are connected by a synaptic connection, and processing the representation of the image using the drone image processing neural network having the brain emulation sub-network to generate a network output that defines a prediction characterizing the image captured by the onboard camera of the drone.

A system and method of generating one or more integration tests are disclosed herein. A computing system receives a URL from a client device. The URL corresponds to a website hosted by a third party server. The computing system generates a recurrent neural network model for testing of the website. The one or more variables associated with the recurrent neural network model are defined by a genetic algorithm. The computing system inputs code associated with the website into the recurrent neural network model. The recurrent neural network model learns a plurality of possible paths through the website by permutating through each possible set of options on the website. The recurrent neural network mode generates, as output, a plurality of integration tests for at least the test website. The computing system compiles the plurality of integration tests into a format compatible with a testing service specified by the client device.