A method, computer system and computer-readable medium for determining a surface pattern for a target object using an evolutionary algorithm such as a genetic algorithm, a parameterized texture-generating function, a 3D renderer for rendering images of a 3D model of the target object with a texture obtained from the parameterized texture generating function, and an object recognition model to process the images and predict whether or not the image contains an object of the target object's type or category. Sets of parameters are generated using the evolutionary algorithm and the accuracy of the object recognition model's prediction of the images with the 3D model textured according to each set of parameters is used to determine a fitness score, by which sets of parameters are scored for the purpose of obtaining future further generations of sets of parameters, such as by genetic algorithm operations such as mutation and crossover operations. The surface pattern is obtained based on the images of the 3D model rendered with a surface texture generated according to a high-scoring set of parameters.

A peer-review flagging system is operable to receive a medical scan and a medical report written by a medical professional in conjunction with review of the medical scan. Automated assessment data is generated by performing an inference function on the medical scan by utilizing a computer vision model trained on a plurality of medical scans. Human assessment data is generated by performing an extraction function on the medical report. Consensus data is generated by comparing the automated assessment data to the first human assessment data. A peer-review notification is transmitted to a client device for display. The peer-review notification indicates the medical scan is flagged for peer-review in response to determining the consensus data indicates the automated assessment data compares unfavorably to the human assessment data.

Image data is accessed. The image data includes frequency domain components. A subset of the frequency domain components is selected based on the relative importance of the frequency domain components. Only the subset of the frequency domain components is provided to an accelerator that executes a neural network to perform an artificial intelligence task using the subset of frequency domain components.

A method, system and computer-readable storage medium for performing a cognitive information processing operation. The cognitive information processing operation includes: receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system executing on a hardware processor of an information processing system, the augmented intelligence system and the information processing system providing a cognitive computing function; performing an explainability with recourse operation, the explainability with recourse operation providing an assurance explanation regarding the cognitive computing function; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.

A method, system and computer-readable storage medium for performing a cognitive information processing operation. The cognitive information processing operation includes: receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system executing on a hardware processor of an information processing system, the augmented intelligence system and the information processing system providing a cognitive computing function; performing an explainability with recourse operation, the explainability with recourse operation providing an assurance explanation regarding the cognitive computing function; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.

A device configured to emulate an actor in a correlithm object processing system includes a memory and a processor. The memory stores an actor table that includes a string correlithm object comprising a plurality of sub-string correlithm objects. The actor is implemented by the processor and receives an input correlithm object, determines n-dimensional distances between the input correlithm object and at least a portion of the plurality of sub-string correlithm objects, and identifies a sub-string correlithm object from the actor table with the shortest determined n-dimensional distance. The actor outputs the real-world output value associated with the identified sub-string correlithm object in the actor table.

A medical scan system is operable to receive a set of labeling data corresponding to a set of medical scans from each of a set of client devices corresponding to a set of users. The set of medical scans and each set of labeling data is transmitted to an expert client device associated with an expert user, and a set of golden labeling data and a plurality of sets of correction data are received from the expert client device. A set of performance score data is generated based on the plurality of sets of correction data, and each performance score data of the set of performance score data is assigned to a corresponding one of the set of users. An updated training set that includes the set of golden labeling data is generated, and a medical scan analysis function is retrained based on the updated training set.

There is provided with an image identification apparatus. An extraction unit extracts a feature value of an image from image data using a Neural Network (NN). A processing unit identifies the image based on the feature value extracted by the extraction unit. The NN comprises a plurality of calculation layers connected hierarchically. The NN includes a plurality of sub-neural networks for performing processing of calculation layers after a specific calculation layer. Mutually different data from an output of the specific calculation layer are respectively inputted to the plurality of sub-neural networks.

Embodiments of the present application provide a method and a device for optimizing a target operation speed curve in an ATO of a train. The method includes: calculating a plurality of performance indexes of the train driving in a current section of a line, and constructing an objective function for optimizing the target operation speed curve of the train according to the plurality of performance indexes; determining constraint conditions of the objective function according to speed limit information of the line and running time of the train in the current section; and solving the objective function according to the constraint conditions based on a differential evolution algorithm to obtain the target operation speed curve of the train. The objective function for optimizing the target operation speed curve of the train are constructed using the plurality of performance indexes, which makes the optimization of the train speed curve more accurate.

An information processing apparatus includes a memory; and a processor configured to determine a plurality of initial image processing programs and to add the initial image processing programs to an image processing program group, extract at least two image processing programs from the image processing program group, generate a candidate of a next-generation image processing program from the extracted image processing programs, based on genetic programming, calculate a fitness of the candidate of the next-generation image processing program, using a learning data item including an input image and a target processing result, and determine the next-generation image processing program or update the image processing program group based on the calculated fitness, wherein the processor is configured to determine, as some of the initial image processing programs, at least some of first image processing programs included in the image processing program group upon previous determination of a next-generation image processing program.