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: A method for training a neural network system to predict the behavior of a set of interacting agents.

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: Representative implementations of devices and techniques provide local and/or remote control for one or more connected loads or remote appliances. A load control device includes controls for switching and varying the power to associated loads and also includes communication components for remote operations. A user interface device includes a configurable and user customizable interface to communicate control instructions to the load control device. The load control device and the user interface device may be installed as a single unit, mechanically and electrically coupled together, or they may be installed remote from each other. Accordingly, the load control device and the user interface device include wired and wireless communication capabilities.

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 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 method for ascertaining an explanation map of an image. All those pixels of the image are highlighted which are significant for a classification of the image ascertained with the aid of a deep neural network. The explanation map is being selected in such a way that it selects a smallest possible subset of the pixels of the image as relevant. The explanation map leads to the same classification result as the image when the explanation map is supplied to the deep neural network for classification. The explanation map is selected in such a way that an activation caused by the explanation map does not essentially exceed an activation caused by the image in feature maps of the deep neural network.

Abstract: A mat seal for an electrical connector includes a plurality of through-holes extending in a through-hole direction through the mat seal, a grid of a first material having a plurality of grid meshes, the grid is arranged with the through-holes extending through the grid meshes, a first material layer of a second material disposed on a first side of the grid, and a second material layer of a third material disposed on a second side of the grid opposite the first side. The first material of the grid has a greater compression modulus than the second material of the first material layer and the third material of the second material layer.

Abstract: A connection assembly includes a first housing part, a second housing part pluggable with the first housing part, and a joint connector held between the first housing part and the second housing part. The first housing part has a plurality of contact element receptacles and a plurality of latching elements. The latching elements latch a plurality of contact elements in the contact element receptacles. The second housing part has a locking section locking one of the latching elements in a latching position. The joint connector connects at least two of the contact elements.

Abstract: A method for ascertaining an explanation map of an image, in which all those pixels of the image are changed which are significant for a classification of the image ascertained with the aid of a deep neural network. The explanation map is selected in such a way that a smallest possible subset of the pixels of the image are changed, and the explanation map preferably does not lead to the same classification result as the image when it is supplied to the deep neural network for classification. The explanation map is selected in such a way that an activation caused by the explanation map does not essentially exceed an activation caused by the image in feature maps of the deep neural network.

Abstract: A method for ascertaining an explanation map of an image, in which all those pixels of the image are changed which are significant for a classification of the image ascertained with the aid of a deep neural network. The explanation map is selected in such a way that a smallest possible subset of the pixels of the image are changed, and the explanation map preferably does not lead to the same classification result as the image when it is supplied to the deep neural network for classification. The explanation map is selected in such a way that an activation caused by the explanation map does not essentially exceed an activation caused by the image in feature maps of the deep neural network.

Abstract: A connection assembly includes a first housing part, a second housing part pluggable with the first housing part, and a joint connector held between the first housing part and the second housing part. The first housing part has a plurality of contact element receptacles and a plurality of latching elements. The latching elements latch a plurality of contact elements in the contact element receptacles. The second housing part has a locking section locking one of the latching elements in a latching position. The joint connector connects at least two of the contact elements.

Abstract: A method for ascertaining an explanation map of an image. All those pixels of the image are highlighted which are significant for a classification of the image ascertained with the aid of a deep neural network. The explanation map is being selected in such a way that it selects a smallest possible subset of the pixels of the image as relevant. The explanation map leads to the same classification result as the image when the explanation map is supplied to the deep neural network for classification. The explanation map is selected in such a way that an activation caused by the explanation map does not essentially exceed an activation caused by the image in feature maps of the deep neural network.

Abstract: Representative implementations of devices and techniques provide local and/or remote control for one or more connected loads or remote appliances. A load control device includes controls for switching and varying the power to associated loads and also includes communication components for remote operations. A user interface device includes a configurable and user customizable interface to communicate control instructions to the load control device. The load control device and the user interface device may be installed as a single unit, mechanically and electrically coupled together, or they may be installed remote from each other. Accordingly, the load control device and the user interface device include wired and wireless communication capabilities.

Abstract: Representative implementations of devices and techniques provide local and/or remote control for one or more connected loads or remote appliances. A load control device includes controls for switching and varying the power to associated loads and also includes communication components for remote operations. A user interface device includes a configurable and user customizable interface to communicate control instructions to the load control device. The load control device and the user interface device may be installed as a single unit, mechanically and electrically coupled together, or they may be installed remote from each other. Accordingly, the load control device and the user interface device include wired and wireless communication capabilities.

Abstract: A mat seal for an electrical connector includes a plurality of through-holes extending in a through-hole direction through the mat seal, a grid of a first material having a plurality of grid meshes, the grid is arranged with the through-holes extending through the grid meshes, a first material layer of a second material disposed on a first side of the grid, and a second material layer of a third material disposed on a second side of the grid opposite the first side. The first material of the grid has a greater compression modulus than the second material of the first material layer and the third material of the second material layer.

Abstract: A method for detecting an object with the aid of a system, the system having a plurality of modules connected in series one after the other, at least one connection between a predefinable module and its immediately following module being implemented in each case with the aid of a filter and storage module connected in series, the first module of the modules connected in series being connected to an acquisition unit, and the last module of the modules connected in series being connected to a detection module. The method includes temporal filtering.

Abstract: A method for detecting an object with the aid of a system, the system having a plurality of modules connected in series one after the other, at least one connection between a predefinable module and its immediately following module being implemented in each case with the aid of a filter and storage module connected in series, the first module of the modules connected in series being connected to an acquisition unit, and the last module of the modules connected in series being connected to a detection module. The method includes temporal filtering.

Abstract: Representative implementations of devices and techniques provide local and/or remote control for one or more connected loads or remote appliances. A load control device includes controls for switching and varying the power to associated loads and also includes communication components for remote operations. A user interface device includes a configurable and user customizable interface to communicate control instructions to the load control device. The load control device and the user interface device may be installed as a single unit, mechanically and electrically coupled together, or they may be installed remote from each other. Accordingly, the load control device and the user interface device include wired and wireless communication capabilities.

Abstract: Representative implementations of devices and techniques provide local and/or remote control for an appliance. A load control device includes local controls and also includes communication components for remote operations. In one implementation, the load control device is configurable for use with a variety of appliances based on interchangeable sleeves that adapt the load control device to power leads having different configurations.