A device, a method and a computer program for training and/or verifying the robustness of an artificial neural network. The artificial neural network is designed to determine an output variable. The method includes: predefining an input variable for the network which has a plurality of dimensions,. For each dimension of the input variable or for each dimension of an output of a linear layer of the artificial neural network without an activation function to which the input variable is mapped by the artificial neural network, the method includes a determination of an upper input variable limit for which a disturbance variable model by which the input variable is able to be mapped to a disturbed input variable has the highest possible value in the dimension, and a determination of a lower input variable limit for which the disturbance variable model has the lowest value possible in the dimension.

Methods, systems, and computer-readable storage media for receiving input data including a set of entities of a first type and a set of entities of a second type, providing a set of features based on entities of the first type, the set of features including features expected to be included in entities of the second type, filtering entities of the second type based on the set of features to provide a sub-set of entities of the second type, and generating an output by processing the set of entities of the first type and the sub-set of entities of the second type through a ML model, the output comprising a set of matching pairs, each matching pair in the set of matching pairs comprising an entity of the set of entities of the first type and at least one entity of the sub-set of entities of the second type.

The invention relates to a system and computer-implemented method for enabling correction of a segmentation of an anatomical structure in 3D image data. The segmentation may be provided by a mesh which is applied to the 3D image data to segment the anatomical structure. The correction may for example involve a user directly or indirectly selecting a mesh part, such as a mesh point, that needs to be corrected. The behaviour of the correction, e.g., in terms of direction, radius/neighbourhood or strength, may then be dependent on the mesh normal direction, and in some embodiments, on a difference between the mesh normal direction and the orientation of the viewing plane.

Disclosed is a quotation method executed by a computer, comprising: obtaining structure parameters and electrical parameters of a product (S101); constructing an external view of the product by using the structure parameters of the product, and performing similarity comparison on the external view of the product and the external view of a historical product to obtain an appearance similarity sorting (102); performing similarity comparison on the electrical parameters of the product and the electrical parameters of the historical product to obtain an electrical parameter similarity sorting (103); on the basis of the cost weights of a structural member and an electrical component and the appearance similarity sorting and the electrical parameter similarity sorting, obtaining a comprehensive sorting which is based on the structure parameters and the electrical parameters (S104); and determining, based on the comprehensive sorting, a bill of materials of the product, and calculating, based on the bill of the materials of the product, the product quotation (105).

Smart sensor methods and systems are described that improve on prior systems. An example device includes a sensor, a memory, a network connection, and two processing units, wherein a first processing unit compares current data provided by the first sensor to the reference data previously provided by the first sensor. Based on the result of the comparison, a second processing unit may be enabled to process the current data, or may be disabled to prevent the second processing unit from processing the current data.

An information processing apparatus obtains first image data representing an image displayed on a first monitor provided in an autonomous vehicle after the completion of updating of first software that controls the display on the first monitor. Then, the information processing apparatus determines whether or not the items displayed on a second monitor used for remotely monitoring the operation of the autonomous vehicle after the completion of updating of second software that controls the display on the second monitor and the items displayed on the first monitor extracted from the first image data are identical to each other.

There are provided techniques for analyzing an atom probe tomography data set obtained from a tip-shaped sample. The techniques include defining analysis sub-volumes in the atom probe tomography data set; performing a fast Fourier transform (FFT) on each of the analysis sub-volumes to obtain a signal in a Fourier domain; identifying at least one FFT peak in the signal in the Fourier domain, each FFT peak being indicative of an expected crystal feature in the corresponding analysis sub-volume; continuously and automatically calculating an image compression factor and a radius of the tip-shaped sample, based on identified crystal features, the identified crystal features being obtained from a collection of expected crystal features; and reconstructing a three-dimensional model of the tip-shaped sample. Said reconstructing includes comparing the identified crystal features with calibration data; and dynamically adjusting the image compression factor and the radius of the tip-shaped sample.

An electronic device and a method for tracking an object thereof are provided. The electronic device identifies whether there is a first object being tracked, when obtaining an image and rotation information of a camera of the electronic device, corrects state information of the first object using the rotation information, when there is the first object, detects a second object matched to the first object from the image based on the corrected state information, and tracks a position of the second object using an object tracking algorithm.

Methods and systems for detecting and predicting anomalies include processing frames of a video stream to determine values of a feature corresponding to each frame. A feature time series is generated that corresponds to values of the identified feature over time. A matrix profile is generated that identifies similarities of sub-sequences of the time series to other sub-sequences of the feature time series. An anomaly is detected by determining that a value of the matrix profile exceeds a threshold value. An automatic action is performed responsive to the detected anomaly.

Face model matrix training method, apparatus, and storage medium are provided. The method includes: obtaining a face image library, the face image library including k groups of face images, and each group of face images including at least one face image of at least one person, k>2, and k being an integer; separately parsing each group of the k groups of face images, and calculating a first matrix and a second matrix according to parsing results, the first matrix being an intra-group covariance matrix of facial features of each group of face images, and the second matrix being an inter-group covariance matrix of facial features of the k groups of face images; and training face model matrices according to the first matrix and the second matrix.