A method of handling outliers is provided. The method includes determining a set of residuals, wherein each residual represents a difference between a measurement included in a set of measurements and a predetermined estimate; clustering the residuals into a plurality of clusters; calculating a consistency value for each of the plurality of clusters based on a number of measurements included in the set of measurements and a standard deviation of the measurements; identifying a cluster having a maximum consistency value among the plurality of clusters as inliers by applying the consistency function to the plurality of clusters; and handling the outliers based on an approximation of one or more parameters as a function of a statistical relationship of the inliers included in the cluster having the maximum consistency value among the plurality of clusters and an initial estimation of the one or more parameters.

Disclosed embodiments include technologies for improving safety mechanisms in computer assisted and/or automated driving (CA/AD) vehicles for protecting vulnerable road users (VRUs). Embodiments include VRU clustering mechanisms, VRU profile awareness mechanisms for ITS-Ss, and VRU Maneuver Coordination Mechanisms for Collision Risk Analysis and Collision Avoidance. Other embodiments are described and/or claimed.

Systems and methods provide a demand forecasting and network optimization for telecommunications services in a network. The systems and methods use classical and quantum computing devices. The computing devices evaluate data types using statistical symmetry recognition and operate between classical and quantum environments. Computing devices receive deposited data, batch data, and streamed data that relates to telecommunications services and segregate the data into spatial and temporal factors. The computing devices receive an analytic request for a forecast of the telecommunications services and conduct a multi-class plural-factored elastic cluster (MPEC) analysis for the telecommunications services using the segregated data. The MPEC analysis includes generating vectors comprised of slopes from plural coefficients to determine demand elasticity from plural features. The computing devices generate, based on the multi-class plural-factored elastic cluster model, a real-time demand-based forecast for the telecommunications services, and output the demand-based forecast.

A computer-implemented method, which enables the data to be clustered without being required to perform any distance calculations among the points of the dataset, includes assigning points of a dataset to cells of a cellular automaton; assigning each cell, having a data point assigned, a distinct state value and a constant temperature value; and assigning all cells, to which a data point is not assigned, a unique state value different from the state values utilized for cells having a data point and to a temperature lower than the constant temperature value; selecting a cell in the cellular automaton randomly; calculating the average temperature of the selected cell and its neighbor cells; setting the temperature of the cells having no data point, as the average temperature; if a neighbor cell temperature is above the predetermined threshold value, moving this neighbor cell to the state of the selected cell.

Embodiments relate to a system, program product, and method for automatically determining which activation data points in a neural model have been poisoned to erroneously indicate association with a particular label or labels. A neural network is trained network using potentially poisoned training data. Each of the training data points is classified using the network to retain the activations of the last hidden layer, and segment those activations by the label of corresponding training data. Clustering is applied to the retained activations of each segment, and a cluster assessment is conducted for each cluster associated with each label to distinguish clusters with potentially poisoned activations from clusters populated with legitimate activations. The assessment includes analyzing, for each cluster, a distance of a median of the activations therein to medians of the activations in the labels.

A method and apparatus for subsurface data processing includes determining a set of clusters based at least in part on measurement vectors associated with different depths or times in subsurface data, defining clusters in a subsurface data by classes associated with a state mode, reducing a quantity of the subsurface data based at least in part on the classes, and storing the reduced quantity of the subsurface data and classes with the state model in a training database for a machine learning process.

An example method performed by a processing system includes obtaining a light detecting and ranging point cloud of a building, where the point cloud includes a plurality of points, and where each point is associated with a set of (x,y,z) coordinates. A first point of the plurality of points is assigned to a subset of the plurality of points that is associated with the building, where the subset includes points whose (x,y) coordinates fall within a footprint of the building. The first point is grouped into a first cluster according to at least one of: a (z) coordinate of the first point and a gradient to which the first point belongs. A first prism formed by the first cluster is constructed. A model of the building is stored as a plurality of connected prisms, where the plurality of connected prisms includes the first prism.

An apparatus for a motor vehicle driver assistance system is provided. The apparatus is configured to optimise object clusters, where each object cluster includes a sequence of position measurements for at least one object in the vicinity of the vehicle. Initially, in a pre-clustering phase, the assignment of the measured object positions to the object clusters may be based on the relative proximity of the measured object positions. The apparatus identifies a rogue object cluster on the basis of a first diagnostic, and a rogue object track from the measurements within the rogue object cluster. The position measurements from the rogue object track are removed from the clusters, and remaining position measurements in the rogue object cluster are reassigned to the other object clusters. The rogue object cluster is removed. Thus the object clusters are optimised.

An apparatus for a motor vehicle driver assistance system is provided. The apparatus is configured to optimise object clusters, where each object cluster includes a sequence of position measurements for at least one object in the vicinity of the vehicle. Initially, in a pre-clustering phase, the assignment of the measured object positions to the object clusters may be based on the relative proximity of the measured object positions. The apparatus identifies a rogue object cluster on the basis of a first diagnostic, and a rogue object track from the measurements within the rogue object cluster. The position measurements from the rogue object track are removed from the clusters, and remaining position measurements in the rogue object cluster are reassigned to the other object clusters. The rogue object cluster is removed. Thus the object clusters are optimised.

Systems and methods provide a demand forecasting and network optimization for telecommunications services in a network. The systems and methods use classical and quantum computing devices. The computing devices evaluate data types using statistical symmetry recognition and operate between classical and quantum environments. Computing devices receive deposited data, batch data, and streamed data that relates to telecommunications services and segregate the data into spatial and temporal factors. The computing devices receive an analytic request for a forecast of the telecommunications services and conduct a multi-class plural-factored elastic cluster (MPEC) analysis for the telecommunications services using the segregated data. The MPEC analysis includes generating vectors comprised of slopes from plural coefficients to determine demand elasticity from plural features. The computing devices generate, based on the multi-class plural-factored elastic cluster model, a real-time demand-based forecast for the telecommunications services, and output the demand-based forecast.