The present invention relates to imaging a hollow organ. In order to provide an improved and facilitated imaging of a hollow organ of interest, a device (10) for providing three-dimensional data of a hollow organ is provided that comprises a measurement input (12), a data processor (14) and an output interface (16). The measurement input is configured to receive a plurality of local electric field measurements (18) of at least one electrode on a catheter inserted in a lumen of a hollow organ of interest. The measurement input is also configured to receive geometrical data (20) representative of the location of the at least one electrode inside the lumen during the measurements. The data processor is configured to receive pre-set electric field characteristics (22) associated with predetermined anatomical landmarks of the hollow organ expectable in the lumen in dependency of a type of the hollow organ. The data processor is also configured to compare at least one of the plurality of local electric field measurements with the pre-set electric field characteristics to determine matching electric field measurements. The data processor is further configured to allocate local electric field measurements to matching electric field characteristics based on the geometrical data to identify anatomical landmarks of the hollow organ by identifying those local field measurements in the plurality of measurements that correspond to landmarks of the hollow organ. The data processor is still further configured to generate a three-dimensional image data cloud (24) by transforming the allocated electric field measurements into portions of the three-dimensional image data cloud based on the identified anatomical landmarks. The output interface is configured to provide the three-dimensional image data cloud.

A method for training a neural network intended to analyze a dental situation of an updated patient. A historical learning database is created that relates to a dental body and to a spatial attribute associated with the dental body. The historical learning database includes more than 1,000 historical records, with each historical record relating to a respective historical patient. Each record including a set of historical images all depicting the dental body in the historical patient, called “historical dental body” and an item of spatial information including, for the historical patient, a set of values for the spatial attribute, called “historical spatial information.” The neural network is trained, by providing it with the sets of historical images as input and with the historical spatial information as output, with the spatial attribute defining an ordered sequence of variables in a three-dimensional reference frame.

A system for speaker verification is disclosed. An input receiving module receives an input audio-visual segment. An input processing module identifies one or more unlabelled speakers and one or more moments in time associated with each of the one or more unlabelled speakers in the audio-visual segment. An information extraction module extracts audio data representative of speech signal and visual data representative of facial images respectively. An input transformation module employs a first pre-trained neural network model to transform audio data of each unlabelled speaker into speaker speech space, employs a second pre-trained neural network model to transform visual data of each unlabelled speaker into speaker face space, and trains a third neural network model to match the audio data and the visual data of each unlabelled speaker with names of the labelled speakers obtained from prestored datasets. A speaker identification module identifies each unlabelled speaker with corresponding names, estimates confidence level corresponding to identification of the each unlabelled speaker from the audio-visual segment.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for characterizing a gaze position of a user in a query image. One of the methods includes obtaining a query image of a user captured by a camera of a mobile device; obtaining device characteristics data specifying (ii) characteristics of the mobile device, (ii) characteristics of the camera of the mobile device, or (iii) both; and processing a neural network input comprising (i) one or more images derived from the query image and (ii) the device characteristics data using a gaze prediction neural network, wherein the gaze prediction neural network is configured to, at run time and after the gaze prediction neural network has been trained, process the neural network input to generate a neural network output that characterizes a gaze position of the user in the query image.

Embodiments of the present disclosure provide a method, device, and computer program product for error evaluation. A method for error evaluation comprises in accordance with a determination that an error occurs in a data protection system, obtaining context information related to an operation of the data protection system; determining, based on the context information and using a trained deep learning model, a type of the error in the data protection system from a plurality of predetermined types, the deep learning model being trained based on training context information and a label on a ground-truth type of an error associated with the training context information; and providing the determined type of the error in the data protection system. In this way, it is possible to achieve automatic classification of errors in the data protection system, thereby improving the efficiency in error classification and saving the operation costs. Therefore, more rapid and more accurate measures can be taken to handle the errors.

A method is claimed for automatically transforming sonic well logs from a depth domain to a seismic two-way travel-time domain. The method includes obtaining a training well with a measured sonic well log in the depth domain and a borehole seismic dataset in the depth domain and obtaining an application well with only a measured sonic well log in the depth domain. The method further includes training a network to predict a transformed sonic well log for the training well based, at least in part, on the measured sonic well log and the borehole seismic dataset in the training well, and predicting with the network, the transformed sonic well log in the application well.

A system includes one or more sensors coupled to equipment; one or more 5G antennas; one or more 5G transceivers coupled to the one or more 5G antennas; and a processor to control one or more wireless or antenna network resources to deliver wireless communication to a remote device.

The disclosure following relates generally to complex simulations, and fault diagnosis. In some embodiments, a component that is causing a delayed simulation time of a system is determined. A component of reduced complexity is designed, and the component of reduced complexity is used to replace the original component in the system. Fault diagnosis may then be conducted using the updated system with the reduced complexity component, thus decreasing the time taken to diagnose the fault.

In one embodiment, a device in a network receives a machine learning encoder and decoder trained by a supervisory service. The service trains the encoder and decoder using vibration measurement data sent to the service by a plurality of devices. The device trains, based on the received encoder, a classifier to determine whether vibration measurement data is indicative of a behavioral anomaly. The device receives vibration measurement data captured by a particular set of one or more vibration sensors of a monitored system. The device evaluates, using the trained decoder, the received vibration measurement data to determine whether the data is indicative of a structural anomaly in the monitored system. The device evaluates, using the trained classifier, the received vibration measurement data to determine whether the data is indicative of a behavioral anomaly in the monitored system.

In order for a supplier of industrial cables to reduce personnel costs and to guarantee customers a consistently high quality standard promptly and reliably, even for global data traffic, a method for identifying industrial cables, comprising the following steps, is proposed: a. automatic visual identification of multiple different components of an industrial cable, from at least one image file; b. analysis of the geometric relationships and/or functional connections between the components; and c. extraction of individual characteristics of the components from the image file using information obtained in step b. A combination of the visual analysis with existing knowledge, also with the possible option of adding trained knowledge, allows an extremely reliably-functioning method for recognizing industrial cables to be provided.