Abstract: A circuit configuration includes a voltage surge protector that is connected in parallel to the link capacitor to protect the link capacitor from voltage overloads. A printed circuit board assembly, an electric axle drive, and a motor vehicle are also disclosed.

Abstract: A computer-implemented method and system for transforming a trained artificial intelligence (AI) model into a trustworthy artificial intelligence model, wherein the trained AI model is provided via a user interface of a webservice platform, a validation data set based on training data of the trained artificial intelligence model is provided, generic samples are generated by a computing component of the webservice platform based on the validation data set, the trained artificial intelligence model is transformed by optimizing a calibration based on the generic samples, where the transformation of the artificial intelligence model is performed by a computing component of the web service platform, and the input, i.e. the trained artificial intelligence model and a validation data set, is provided therefor to the computing component via a user interface of the web service platform.

Abstract: A seat attachment system for a mass transit vehicle including a rail, a fastener and a seat interface. The rail, which is adapted to be secured to a chassis of the mass transit vehicle, has a longitudinal T-slot adapted to receive the fastener. The rail has two non-parallel sets of corrugations on its external face. The seat interface, which is adapted to be mounted to a seat frame, is also provided with two non-parallel sets of corrugations whose angle matches that of the sets of corrugations on the rail. The sets of corrugations are adapted to cooperate and interlock in pairs so as to respectively locate the seat interface, and thereby a seat to which it is attached, vertically and horizontally upon the fastener being tightened.

Abstract: The disclosed relates to a computer-implemented method of training a Neural Network as well as a corresponding computer program, computer-readable medium and data processing system. In addition to a categorical cross-entropy loss LCCE weights of the NN are updated based on predictive entropy loss LS and an adversarial calibration loss Ladv.

Abstract: The invention refers to a natural language processing system configured for receiving an input sequence ci of input words (v1, v2, . . . vN) representing a first sequence of words in a natural language of a first text and generating an output sequence of output words (, , . . . ) representing a second sequence of words in a natural language of a second text and modeled by a multinominal topic model, wherein the multinominal topic model is extended by an incorporation of language structures using a deep contextualized Long-Short-Term Memory model.

Abstract: A device for determining a spindle status of a spindle of a machine tool includes a detector for detecting sensor data of the spindle for a defined time window. A processing unit analyses the sensor data through artificial intelligence by calculating a defined feature of the sensor data for the defined time window and determining a spindle status from the sensor data. An output member outputs the determined spindle status.

Abstract: The present invention relates to a method for determining in a subject's biological sample the relative proportions of papillary renal cell carcinoma (pRCC), clear cell renal cell carcinoma (ccRCC), and chromophobe renal cell carcinoma (chRCC), an array comprising capture molecules capable of specifically binding to RCC signature genes or coding sequences thereof or products encoded thereby, and the use of RCC signature genes for classifying a subject into a renal cell carcinoma (RCC) risk group and/or for determining in a subject's biological sample the relative proportions of pRCC, ccRCC, and chRCC.

Abstract: A computer-implemented method and a data processing apparatus provide and apply a trained probabilistic graphical model for verifying and/or improving the consistency of labels within the scope of medical image processing. Also provided are a computer-implemented method for verifying and/or improving the consistency of labels within the scope of medical imaging processing, a data processing apparatus embodied to verify and/or improve the consistency of labels within the scope of medical image processing, and a corresponding computer program product and a computer-readable medium.

Abstract: The following relates to a computer-implemented method and system for transforming a trained artificial intelligence model into a trustworthy artificial intelligence model, by providing the trained artificial intelligence model via a user interface of a webservice platform, providing a validation data set, which is based on training data of the trained artificial intelligence model, generating generic samples by a computing component of the webservice platform based on the validation data set, and transforming the trained artificial intelligence model by optimizing a calibration based on the generic samples. The transformation of the AI model is performed by a computing component of the web service platform. The input, i.e. the trained artificial intelligence model as well as a validation data set, is provided to the computing component via a user interface of the web service platform. Such a user interface is implemented by any applicable frontend.

Abstract: The aim of the invention is to configure an image analysis device (BA). This is achieved in that a plurality of training images (TPIC) assigned to an object type (OT) and an object sub-type (OST) are fed into a first neural network module (CNN) in Order to detect image features. Furthermore, training output data sets (FEA) of the first neural network module (CNN) are fed into a second neural network module (MLP) in Order to detect object types using image features. According to the invention, the first and second neural network module (CNN, MLP) are trained together such that training output data sets (OOT) of the second neural network module (MLP) at least approximately reproduce the object types (OT) assigned to the training images (TPIC).

Abstract: A seat attachment system for a mass transit vehicle including a rail, a fastener and a seat interface. The rail, which is adapted to be secured to a chassis of the mass transit vehicle, has a longitudinal T-slot adapted to receive the fastener. The rail has two non-parallel sets of corrugations on its external face. The seat interface, which is adapted to be mounted to a seat frame, is also provided with two non-parallel sets of corrugations whose angle matches that of the sets of corrugations on the rail. The sets of corrugations are adapted to cooperate and interlock in pairs so as to respectively locate the seat interface, and thereby a seat to which it is attached, vertically and horizontally upon the fastener being tightened.

Abstract: Provided is a computer-implemented method for post-processing output data of a classifier, including the steps: a. providing a validation data set with a plurality of labelled sample pairs, wherein each labelled sample pair comprises a model input and a corresponding model output; b. providing a plurality of perturbation levels; c. generating at least one perturbated sample pair for each labelled sample pair of the plurality of labelled sample pairs using a perturbation method based on the respective labelled sample pair and at least one perturbation level of the plurality of perturbation levels; d. determining a post-processing model based on the plurality of perturbated sample pairs; e. applying the determined post-processing model on testing data to post-process the output data of the classifier; and f. providing the post-processed output data of the classifier. Also provided is a corresponding technical unit and computer program product.

Abstract: The present invention concerns a text analysis system, the text analysis system being adapted for utilizing a topic model to provide a document representation. The topic model is based on learning performed on a text corpus utilizing hidden layer representations associated to words of the text corpus, wherein each hidden layer representation pertains to a specific word of the text corpus and is based on a word environment including words occurring before and after the specific word in a text of the text corpus.

Abstract: The invention refers to a natural language processing system configured for receiving an input sequence ci of input words representing a first sequence of words in a natural language of a first text and generating an output sequence of output words representing a second sequence of words in a natural language of a second text and modeled by a multinominal topic model, wherein the multinominal topic model is extended by an incorporation of full contextual information around each word vi, wherein both preceding words v<i and following words v>i around each word vi are captured by using a bi-directional language modelling and a feed-forward fashion, wherein position dependent forward hidden layers {right arrow over (h)}i and backward hidden layers i for each word vi are computed.

Abstract: A control device of an automation system, which is configured to control a plant, such as a production plant, including using an AI system, is provided. In an application of the control device, the device monitors the production with regard to the quality of the objects produced, for example, with regard to the presence of fault cases. The AI system is trained in advance based on a plurality of known states of the objects, so that the AI system may be trained for the occurrence of new, previously unknown states, where only a small number of example cases are required.

Abstract: The disclosed relates to a computer-implemented method of training a Neural Network as well as a corresponding computer program, computer-readable medium and data processing system. In addition to a categorical cross-entropy loss LCCE weights of the NN are updated based on predictive entropy loss LS and an adversarial calibration loss Ladv.

Abstract: Provided is an in-field apparatus and method for automatic localization of a fault having occurred at power transmission lines of a power supply system, the in-field apparatus includes a preprocessing unit configured to process measured voltage and/or current raw time series data of the power transmission lines to provide a normalized raw data and/or feature representation of the measured raw time series data, and an artificial intelligence module configured to predict an optimal evaluation time used for evaluation of the measured voltage and/or current raw time series data to localize the fault based on the normalized raw data and/or feature representation.

Abstract: The present invention relates to a computer-implemented method and a data processing apparatus for providing and applying a trained probabilistic graphical model for verifying and/or improving the consistency of labels within the scope of medical image processing, the use of the model for verifying and/or improving the consistency of labels within the scope of medical image processing, a computer-implemented method for verifying and/or improving the consistency of labels within the scope of medical imaging processing, a data processing apparatus embodied to verify and/or improve the consistency of labels within the scope of medical image processing, and a corresponding computer program product and a computer-readable medium.

Abstract: The invention refers to a natural language processing system configured for receiving an input sequence ci of input words (v1, v2, . . . vN) representing a first sequence of words in a natural language of a first text and generating an output sequence of output words (, , . . . ) representing a second sequence of words in a natural language of a second text and modeled by a multinominal topic model, wherein the multinominal topic model is extended by an incorporation of language structures using a deep contextualized Long-Short-Term Memory model.

Abstract: The invention refers to a natural language processing system configured for receiving an input sequence ci of input words representing a first sequence of words in a natural language of a first text and generating an output sequence of output words representing a second sequence of words in a natural language of a second text and modeled by a multinominal topic model, wherein the multinominal topic model is extended by an incorporation of full contextual information around each word vi, wherein both preceding words v<i and following words v>i around each word vi are captured by using a bi-directional language modelling and a feed-forward fashion, wherein position dependent forward hidden layers {right arrow over (h)}i and backward hidden layers for each word vi are computed.