Abstract: Machine learning is a process that learns a model from a given dataset, where the model can then be used to make a prediction about new data. In order to reduce the costs associated with collecting and labeling real world datasets for use in training the model, computer processes can synthetically generate datasets which simulate real world data. The present disclosure improves the effectiveness of such synthetic datasets for training machine learning models used in real world applications, in particular by generating a synthetic dataset that is specifically targeted to a specified downstream task (e.g. a particular computer vision task, a particular natural language processing task, etc.).

Abstract: A method for detecting a state of a door or window is provided, comprising: receiving sensor data from a sensor arranged on the door or window, the sensor data comprising magnetometer data and at least one of angular velocity data and acceleration data; judging whether magnetic field distortion is present on the basis of the sensor data; and when it is judged that the magnetic field distortion is not present, determining the state of the door or window on the basis of the magnetometer data. The accuracy of state detection is thereby increased.

Abstract: Systems and methods disclosed relate to generating training data. In one embodiment, the disclosure relates to systems and methods for generating training data to train a neural network to detect and classify objects. A simulator obtains 3D models of objects, and simulates 3D environments comprising the objects using physics-based simulations. The simulations may include applying real-world physical conditions, such as gravity, friction, and the like on the objects. The system may generate images of the simulations, and use the images to train a neural network to detect and classify the objects from images.