Abstract: By accurately predicting industrial aging processes (IAP), such as the slow deactivation of a catalyst in a chemical plant, it is possible to schedule maintenance events further in advance, thereby ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described by mechanistic models or simple empirical prediction models. In order to accurately predict IAP, data-driven models are proposed, comparing some traditional stateless models (linear and kernel ridge regression, as well as feed-forward neural networks) to more complex stateful recurrent neural networks (echo state networks and long short-term memory networks). Additionally, variations of the stateful models are discussed. In particular, stateful models using mechanistical pre-knowledge about the degradation dynamics (hybrid models).

Abstract: The present invention relates to training predictive data-driven model for predicting an industrial time dependent process. A data driven generative model is introduced for modelling and generating complex sequential data comprising multiple modalities, by learning a joint time-dependent representation of the different modalities. The model may be configured to handle any combination of missing modalities, which enables conditional generation based on known modalities, providing a high degree of control over the properties of the generated sequences.

Abstract: By accurately predicting industrial aging processes (IAP), such as the slow deactivation of a catalyst in a chemical plant, it is possible to schedule maintenance events further in advance, thereby ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described by mechanistic models or simple empirical prediction models. In order to accurately predict IAP, data-driven models are proposed, comparing some traditional stateless models (linear and kernel ridge regression, as well as feed-forward neural networks) to more complex stateful recurrent neural networks (echo state networks and long short-term memory networks). Additionally, variations of the stateful models are discussed. In particular, stateful models using mechanistical pre-knowledge about the degradation dynamics (hybrid models).

Abstract: Pruning and/or quantizing a machine learning predictor or, in other words, a machine learning model such as a neural network is rendered more efficient if the pruning and/or quantizing is performed using relevance scores which are determined for portions of the machine learning predictor on the basis of an activation of the portions of the machine learning predictor manifesting itself in one or more inferences performed by the machine learning (ML) predictor.

Abstract: A concept for Federated Learning which is more efficient and/or robust is presented. Beyond this, concepts for specifying clients and/or measuring training data similarities in a manner more suitable for being applied in Federated Learning environments, are described.

Abstract: The present application is concerned with several aspects of improving the efficiency of distributed learning.

Abstract: Disclosed is a biosignal acquisition device for the acquisition, in particular the concurrent or simultaneous acquisition, of optical and electrical biosignals. The optical and electrical biosignals are both received by an analog front end device for biosignals, with an opto-electric converter for converting the optical biosignals into electrical signals. Also disclosed are a system of a plurality of biosignal acquisition devices and a biosignal acquisition method.

Abstract: The coding efficiency of predictive picture codecs using transform-based residual coding is enhanced by allowing the codec to switch between a set of transforms for the sake of the transform-based residual coding. It turned out that even when using explicit signaling from encoder to decoder in order to signal the actual transform to be used out of the set of transforms from encoder to decoder, an increase in coding efficiency may result. Alternatively, the switching between the set of transforms may be performed without explicit signaling at all, or using a combination of explicit signaling and some sort of prediction of the switching.

Abstract: The task of relevance score assignment to a set of items onto which an artificial neural network is applied is obtained by redistributing an initial relevance score derived from the network output, onto the set of items by reversely propagating the initial relevance score through the artificial neural network so as to obtain a relevance score for each item. In particular, this reverse propagation is applicable to a broader set of artificial neural networks and/or at lower computational efforts by performing same in a manner so that for each neuron, preliminarily redistributed relevance scores of a set of downstream neighbor neurons of the respective neuron are distributed on a set of upstream neighbor neurons of the respective neuron according to a distribution function.

Abstract: Disclosed is a biosignal acquisition device for the acquisition, in particular the concurrent or simultaneous acquisition, of optical and electrical biosignals. The optical and electrical biosignals are both received by an analog front end device for biosignals, with an opto-electric converter for converting the optical biosignals into electrical signals. Also disclosed are a system of a plurality of biosignal acquisition devices and a biosignal acquisition method.

Abstract: Method for the automatic analysis of an image of a biological sample with respect to a pathological relevance, wherein a)local features of the image are aggregated to a global feature of the image using a bag of visual word approach, b) step a) is repeated at least two times using different methods resulting in at least two bag of word feature datasets, c) computation of at least two similarity measures using the bag of word features obtained from a training image dataset and bag of word features from the image, d) the image training dataset comprising a set of visual words, classifier parameters, including kernel weights and bag of word features from the training images, e) the computation of the at least two similarity measures is subject to an adaptive computation of kernel normalization parameters and/or kernel width parameters, f) for each image one score is computed depending on the classifier parameters and kernel weights and the at least two similarity measures, the at least one score being a measure

Abstract: Method for the automatic analysis of an image (1, 11, 12, 13) of a biological sample with respect to a pathological relevance, wherein fj local features of the image (1, 11, 12, 13) are aggregated to a global feature of the image (1, 11, 12, 13) using a bag of visual word approach, g) step a) is repeated at least two times using different methods resulting in at least two bag of word feature datasets, h) computation of at least two similarity measures using the bag of word features obtained from a training image dataset and bag of word features from the image (1, 11, 12, 13) i) the image training dataset comprising a set of visual words, classifier parameters, including kernel weights and bag of word features from the training images, j) the computation of the at least two similarity measures is subject: to an adaptive computation of kernel normalization parameters and/or kernel width parameters, f) for each image (1, 11, 12, 13) one score is computed depending on the classifier parameters and kernel weights

Abstract: The invention is concerned with a method and an apparatus for automatic comparison of at least two data sequences characterized in—an evaluation of a local relationship between any pair of subsequences in two or more sequences; —an evaluation of a global relationship by means of aggregation of the evaluations of said local relationships.

Abstract: The invention relates to a sensor system and several method for the capacitive measurement of electromagnetic signals having a biological origin. Such a sensor system comprises a capacitive electrode device (10), an electrode shielding element (20) which surrounds the electrode device (10) at least in part in order to shield the same (10) from interfering external electromagnetic fields, and a signal processing device (30) for processing electromagnetic signals that can be detected by means of the electrode device (10). According to the invention, additional shielding means (21) three-dimensionally surround the electrode device (10) and the electrode shielding element (20) at least in part in order to block out interfering external electromagnetic fields. The changes in the electrode capacity of the capacitive sensor system are determined with the aid of several methods which particularly use the inventive sensor system in order to take said changes into account when the test signals are evaluated.

Abstract: The invention relates to a sensor system and several method for the capacitive measurement of electromagnetic signals having a biological origin. Such a sensor system comprises a capacitive electrode device (10), an electrode shielding element (20) which surrounds the electrode device (10) at least in part in order to shield the same (10) from interfering external electromagnetic fields, and a signal processing device (30) for processing electromagnetic signals that can be detected by means of the electrode device (10). According to the invention, additional shielding means (21) three-dimensionally surround the electrode device (10) and the electrode shielding element (20) at least in part in order to block out interfering external electromagnetic fields. The changes in the electrode capacity of the capacitive sensor system are determined with the aid of several methods which particularly use the inventive sensor system in order to take said changes into account when the test signals are evaluated.

Abstract: The invention relates to a method and system for analyzing messages transmitted in a communication network. An embodiment of the method comprises the steps of: determining an information-related citation index indicating how often information comprised in a message has been forwarded in consecutive messages to other users of the communication network, and providing an analysis result based on the information-related citation index.

Abstract: The invention relates to a sensor system and several method for the capacitive measurement of electromagnetic signals having a biological origin. Such a sensor system comprises a capacitive electrode device (10), an electrode shielding element (20) which surrounds the electrode device (10) at least in part in order to shield the same (10) from interfering external electromagnetic fields, and a signal processing device (30) for processing electromagnetic signals that can be detected by means of the electrode device (10). According to the invention, additional shielding means (21) three-dimensionally surround the electrode device (10) and the electrode shielding element (20) at least in part in order to block out interfering external electromagnetic fields. The changes in the electrode capacity of the capacitive sensor system are determined with the aid of several methods which particularly use the inventive sensor system in order to take said changes into account when the test signals are evaluated.

Abstract: In a method for detecting the modes of a dynamic system with a large number of modes that each have a set ? (t) of characteristic system parameters, a time series of at least one system variable x(t) is subjected to modeling, for example switch segmentation, so that in each time segment of a predetermined minimum length a predetermined prediction model, for example a neural network, for a system mode is detected for each system variable x(t), whereby modeling of the time series is followed by drift segmentation in which, in each time segment in which there is transition of the system from a first system mode to a second system mode, a series of mixed prediction models is detected produced by linear, paired superimposition of the prediction models of the two system modes.