An aircraft control system (100) including an input interface (102), an output interface (114) and a processing engine (108) having a classifier (110) that applies input data (104) generated by the input interface (102) to generate output control data (112). The classifier (110) has a plurality of parameters which represent a control policy for operating the aircraft (800). The output interface (114) generates control outputs to control the aircraft (800) based on the output control data (112). A machine learning system (900) for training the classifier (110) including an environment (902), a pathway evaluation engine (904), storage (906), and a training engine (908). The machine learning system (900) generates training data (912) by selecting a pathway representing an operating procedure using the pathway evaluation engine (904). The training engine (908) trains the classifier (110) using the training data (912).

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for data augmentation. The method includes: generating a group of candidate images based on a target image by using a thermodynamic genetic algorithm (TDGA) model, the TDGA model being configured to apply one or more operations of a set of predetermined image processing operations during each evolution process; and determining multiple augmented images from the group of candidate images based on free energy of the group of candidate images, the multiple augmented images being determined as belonging to the same classification with the target image. In this way, data augmentation can be efficiently implemented by a thermodynamic genetic algorithm.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for data augmentation. The method includes: generating a group of candidate images based on a target image by using a thermodynamic genetic algorithm (TDGA) model, the TDGA model being configured to apply one or more operations of a set of predetermined image processing operations during each evolution process; and determining multiple augmented images from the group of candidate images based on free energy of the group of candidate images, the multiple augmented images being determined as belonging to the same classification with the target image. In this way, data augmentation can be efficiently implemented by a thermodynamic genetic algorithm.

This disclosure relates to an analysis method for analyzing a chemical reaction from one or more starting materials, comprising a preparation step S1 of preparing a reaction network diagram indicating the one or more starting materials, at least part of one or more intermediate products and one or more final products generated from the chemical reaction, and reaction pathways of the chemical reaction, and a prediction step S2 of predicting the reaction rate in each reaction pathway using an artificial intelligence algorithm.

A data analysis system includes processor to: arrange data in a multi-dimensional structure based on a target activity defined by a smart card; perform analysis on the data to predict an outcome of the target activity of activities; determine a probability of success of the outcome that has been predicted; determine, based on the outcome and probability of success, choices associated with the activities; determine patterns and changes in the data pertaining to the activities detected by an access device with access to the smart card; perform transformative and scheduling, exposed through an application programming interface for the data; schedule to arrange the choices and the probability of success of the outcome for the access device; cue the choices and the probably of success; and transmit the plurality of choices and the probability of success of the outcome to the access device for rendering on the smart card.

A method includes receiving a data set including medical information of respective patients, respective types of a clinical procedure performed on the patients, and respective survival rates of the patients. A survival tree graph is generated by maximizing a cost function of differences in the survival rates between the types of the clinical treatment procedure. A type of the clinical procedure is selected for a given patient, based on the survival tree graph.

A device and a method for monitoring rinsing or flushing processes of parts in automated analyser systems like diagnostic instruments that participate in the handling of liquids.

Techniques are provided for generating a set of target hybrid seeds with optimal yield and risk performance, including a server receiving a candidate set of hybrid seeds along with probability of successful yield values, associated historical agricultural data and property information, and selecting a subset of the hybrid seeds that have probability of success values greater than a filtering threshold. The server generates representative yield values for hybrid seeds based on the historical agricultural data and risk values for each hybrid seed. The server generates a dataset of target hybrid seeds for planting based on the risk values, the yield values, and the properties for the target fields. The dataset of target hybrid seeds includes target hybrid seeds that meet a specific threshold for a range of risk values. The server causes display of the dataset of target hybrid seeds including yield values and risk values for the target fields.

A computer implemented method for facilitating training of body-eye coordination using a computing device having access to a camera is disclosed. The method includes receiving a training video of a player from the camera; superimposing a visual cue onto the training video; extracting a body posture flow of the player from the training video by performing a computer vision algorithm on one or more frames of the training video; determining whether the player has responded to the visual cue by analyzing the body posture flow of the player; and generating a feedback to the player in response to determining that the player has responded to the visual cue. Multi-player embodiments of the present invention are also disclosed.

Systems, methods, and computer-readable mediums for distributing machine learning model training to network edge devices, while centrally monitoring training of the models and controlling deployment of the models. A machine learning model architecture can be generated at a machine learning structure controller. The machine learning model architecture can be deployed to network edge devices in a network environment to instantiate and train a machine learning model at the network edge devices. Performance reports indicating performance of the machine learning model at the network edge devices can be received by the machine learning structure controller from the network edge devices. The machine learning structure controller can determine whether to deploy another machine learning model architecture to the network edge devices based on the performance reports and subsequently deploy the another architecture to the network edge devices if it is determined to deploy the architecture based on the performance reports.