Abstract: A computer-implemented method of predicting dynamics of objects in a surrounding of a vehicle is disclosed. The method starts with a step of receiving a first data sets characterizing dynamics of the objects respectively. Then, each of the first data sets is propagated through an encoder outputting a latent representation for each of the first data sets. Then, a graph based on the latent representations is generated. Then, the graph is propagated through a Graph Neural Network outputting an updated graph. Based on the updated graph a decoder outputs a predicted dynamic for selected object for a subsequent time step.

Abstract: Analytical methods and systems applied to sequential event data are disclosed. An exemplary system and method analyzes datasets containing events in a plurality of journeys. The methods and systems described analyze and quantify the relative importance of events and sequences leading to outcomes where the data is complex and interconnected. In some embodiments, a graphical user interface illustrates the quantification of these datasets. In some embodiments, the graphical user interface maps the journey paths to show the relative importance of each journey path. In some embodiments, the maps of journey paths are interactive, allowing selection of paths of interest for detailed analysis. In some embodiments, the methods and systems calculate paths similar to a journey path of interest. An exemplary method and system also provides detailed recommendations for changing events within a sequence to either increase or decrease the likelihood of achieving a selected outcome.

Abstract: A method for configuring a neural network which is designed to map measured data to one or more output variables. The method includes: transformation(s) of the measured data is/are specified which when applied to the measured data, is/are meant to induce the output variables supplied by the neural network to exhibit an invariant or equivariant behavior; at least one equation is set up which links a condition that the desired invariance or equivariance be given with the architecture of the neural network; by solving the at least one equation a feature is obtained that characterizes the desired architecture and/or a distribution of weights of the neural network in at least one location of this architecture; a neural network is configured in such a way that its architecture and/or its distribution of weights in at least one location of this architecture has/have all of the features ascertained in this way.

Abstract: Some embodiments are directed to a computer-implemented method of interacting with a physical environment according to a policy. The policy determines multiple action probabilities of respective actions based on an observable state of the physical environment. The policy includes a neural network parameterized by a set of parameters. The neural network determines the action probabilities by determining a final layer input from an observable state and applying a final layer of the neural network to the final layer input. The final layer is applied by applying a linear combination of a set of equivariant base weight matrices to the final layer input. The base weight matrices are equivariant in the sense that, for a set of multiple predefined transformations of the final layer input, each transformation causes a corresponding predefined action permutation of the base weight matrix output for the final layer input.

Abstract: Analytical methods and systems applied to sequential event data are disclosed. An exemplary system and method analyzes datasets containing events in a plurality of journeys. The methods and systems described analyze and quantify the relative importance of events and sequences leading to outcomes where the data is complex and interconnected. In some embodiments, a graphical user interface illustrates the quantification of these datasets. In some embodiments, the graphical user interface maps the journey paths to show the relative importance of each journey path. In some embodiments, the maps of journey paths are interactive, allowing selection of paths of interest for detailed analysis. In some embodiments, the methods and systems calculate paths similar to a journey path of interest. An exemplary method and system also provides detailed recommendations for changing events within a sequence to either increase or decrease the likelihood of achieving a selected outcome.

Abstract: The present invention relates to a secured and unforgeable digital license plate that facilitates tracking of a vehicle's location and the monitoring of the vehicle's mechanical and electrical condition, as well as providing indications about the vehicle's traffic and parking lot violations. The display on the digital license plate is highly visible to both motor vehicle enforcement officers and to drivers and passengers of nearby vehicles, and is indicative that the vehicle bearing the digital license plate with the displayed indication is exhibiting anomalous motor activity.

Abstract: A computer-implemented method for applying control to a robot, and apparatus therefor. A parametric model of an environment, in particular a deep neural network, is trained in accordance with a method for training the parametric model of the environment. The model is trained depending on a controlled system. A strategy is learned in accordance with a method for model-based learning of the strategy. Control is applied to the robot depending on the parametric model and on the strategy.

Abstract: A method for training a machine learning model. The method includes: determining a plurality of training sequences of training-input data elements, wherein for each training sequence each training-input data element contains sensor data for a time point from a time period assigned to the training sequence in which a prespecified event takes place at least once at one or more respective event time points; determining, for each training-input data element, the temporal distance between the time point for which the training-input data element contains sensor data and one of the one or more respective event time points; and training the machine learning model depending on the determined temporal distances.

Abstract: A method for generating additional training data for training a machine learning algorithm is disclosed. The method includes (i) providing training data for training the machine learning algorithm, wherein the training data includes labeled sensor data from at least one sensor, (ii) transforming the training data for training the machine learning algorithm in a graph structure, wherein nodes in the graph structure represent objects represented in the corresponding sensor data, and wherein a starting node of the graph structure represents the position of the at least one sensor with respect to the objects represented in the corresponding sensor data, and (iii) generating additional training data for training the machine learning model by modifying the graph structure.

Abstract: A control system for a motor vehicle, for outputting a controlled variable, with the aid of which a directly controlled variable of a motor vehicle is adjustable via suitable control operations, in order to adapt the directly controlled variable to a reference variable of the control system. The control system includes a controller, which is configured to output a first output variable on the basis of the directly controlled variable of the motor vehicle, and on the basis of the reference variable of the control system. The control system further includes a predictive model, which may be trained to output a second output variable that reflects a deviation of a driving behavior of a driver of the motor vehicle from the first output variable of the controller. The controlled variable of the control system encompasses an addition of the first output variable and the second output variable.

Abstract: A method for training a deep-learning-based machine learning algorithm. The method includes: providing training data for training the deep-learning-based machine learning algorithm, wherein the training data comprise sensor data; training, by a machine learning method, the deep-learning-based machine learning algorithm based on the training data; and subsequently optimizing at least one parameter of the trained deep-learning-based machine learning algorithm based on a non-differentiable cost function.

Abstract: A computer-implemented method for using machine learning to determine control parameters for a control system, in particular of a motor vehicle, in particular for controlling a driving operation of the motor vehicle. The method includes: providing a set of travel trajectories; deriving reward functions from the travel trajectories, using an inverse reinforcement learning method; deriving driver type-specific clusters based on the reward functions; determining control parameters for a particular driver type-specific cluster.

Abstract: Analytical methods and systems applied to sequential event data are disclosed. An exemplary system and method analyzes datasets containing events in a plurality of journeys. The methods and systems described analyze and quantify the relative importance of events and sequences leading to outcomes where the data is complex and interconnected. In some embodiments, a graphical user interface illustrates the quantification of these datasets. In some embodiments, the graphical user interface maps the journey paths to show the relative importance of each journey path. In some embodiments, the maps of journey paths are interactive, allowing selection of paths of interest for detailed analysis. In some embodiments, the methods and systems calculate paths similar to a journey path of interest. An exemplary method and system also provides detailed recommendations for changing events within a sequence to either increase or decrease the likelihood of achieving a selected outcome.

Abstract: A method for configuring a neural network which is designed to map measured data to one or more output variables. The method includes: transformation(s) of the measured data is/are specified which when applied to the measured data, is/are meant to induce the output variables supplied by the neural network to exhibit an invariant or equivariant behavior; at least one equation is set up which links a condition that the desired invariance or equivariance be given with the architecture of the neural network; by solving the at least one equation a feature is obtained that characterizes the desired architecture and/or a distribution of weights of the neural network in at least one location of this architecture; a neural network is configured in such a way that its architecture and/or its distribution of weights in at least one location of this architecture has/have all of the features ascertained in this way.

Abstract: Analytical methods and systems applied to sequential event data are disclosed. An exemplary system and method analyzes datasets containing events in a plurality of journeys. The methods and systems described analyze and quantify the relative importance of events and sequences leading to outcomes where the data is complex and interconnected. In some embodiments, a graphical user interface illustrates the quantification of these datasets. In some embodiments, the graphical user interface maps the journey paths to show the relative importance of each journey path. In some embodiments, the maps of journey paths are interactive, allowing selection of paths of interest for detailed analysis. In some embodiments, the methods and systems calculate paths similar to a journey path of interest. An exemplary method and system also provides detailed recommendations for changing events within a sequence to either increase or decrease the likelihood of achieving a selected outcome.

Abstract: Some embodiments are directed to a computer-implemented method of interacting with a physical environment according to a policy. The policy determines multiple action probabilities of respective actions based on an observable state of the physical environment. The policy includes a neural network parameterized by a set of parameters. The neural network determines the action probabilities by determining a final layer input from an observable state and applying a final layer of the neural network to the final layer input. The final layer is applied by applying a linear combination of a set of equivariant base weight matrices to the final layer input. The base weight matrices are equivariant in the sense that, for a set of multiple predefined transformations of the final layer input, each transformation causes a corresponding predefined action permutation of the base weight matrix output for the final layer input.

Abstract: A computer-implemented method for inferring a device feature of a device produced on a wafer. The method includes: providing a wafer feature model associating a wafer position indicating a position of a produced device on the wafer to a device feature, wherein the wafer feature model is configured to be trained by one or more wafer feature maps and particularly configured as a Gaussian process model, providing a sample device feature of at least one device at a sample wafer position, and inferring the device feature of at least one other device of the wafer depending on the provided wafer feature model.

Abstract: The present invention relates to a secured and unforgeable digital license plate that facilitates tracking of a vehicle's location and the monitoring of the vehicle's mechanical and electrical condition, as well as providing indications about the vehicle's traffic and parking lot violations. The display on the digital license plate is highly visible to both motor vehicle enforcement officers and to drivers and passengers of nearby vehicles, and is indicative that the vehicle bearing the digital license plate with the displayed indication is exhibiting anomalous motor activity.

Abstract: A computer-implemented method for applying control to a robot, and apparatus therefor. A parametric model of an environment, in particular a deep neural network, is trained in accordance with a method for training the parametric model of the environment. The model is trained depending on a controlled system. A strategy is learned in accordance with a method for model-based learning of the strategy. Control is applied to the robot depending on the parametric model and on the strategy.

Abstract: Analytical methods and systems applied to a plurality of input files to understand and explain crucial factors leading to customers jumping, or hopping, from one channel to another channel. The methods and systems described may include receiving a first file associated with a first channel dataset and receiving a second file associated with a second channel dataset. The methods and systems described may include merging the two datasets based on key fields found within the metadata of the two files. In some embodiments, additional statistical metrics and measures may be applied to the merged dataset to both rank the merged events and to display the characteristics of each event within the entire merged dataset.