A first electronic device controls a second electronic device to measure a position of the first electronic device. The first electronic device includes a motion sensor, a network interface circuit, a processor, and a memory. The motion sensor senses motion of the first electronic device. The network interface circuit communicates with the second electronic device. The memory stores program code that is executed by the processor to perform operations that include, responsive to determining that the first electronic device has a level of motion that satisfies a defined rule, transmitting a request for the second electronic device to measure a position of the first electronic device. The position of the first electronic device is sensed and then stored in the memory. An acknowledgement is received from the second electronic device indicating that it has stored sensor data that can be used to measure the position of the first electronic device.

Approaches presented herein use ultrahyperbolic representations (e.g., non-Riemannian manifolds) in inferencing tasks—such as classification—performed by machine learning models (e.g., neural networks). For example, a machine learning model may receive, as input, a graph including data on which to perform an inferencing task. This input can be in the form of, for example, a set of nodes and an adjacency matrix, where the nodes can each correspond to a vector in the graph. The neural network can take this input and perform mapping in order to generate a representation of this graph using an ultrahyperbolic (e.g., non-parametric, pseudo- or semi-Riemannian) manifold. This manifold can be of constant non-zero curvature, generalizing to at least hyperbolic and elliptical geometries. Once such a manifold-based representation is obtained, the neural network can perform one or more inferencing tasks using this representation, such as for classification or animation.

A system for estimating a three dimensional pose of one or more persons in a scene is disclosed herein. The system includes one or more cameras and a data processor configured to execute computer executable instructions. The computer executable instructions include: (i) receiving one or more images of the scene from the one or more cameras; (ii) extracting features from the one or more images of the scene for providing inputs to a first branch pose estimation neural network and second branch pose estimation neural network; (iii) generating a first training signal from the second branch pose estimation neural network using a three dimensional reconstruction module for input into the first branch pose estimation neural network; (iv) generating one or more volumetric heatmaps; and (v) applying a maximization function to the one or more volumetric heatmaps to obtain a 3D pose of one or more persons in the scene.

A diagnostic system to analyze imaging data includes a memory configured to store hybrid imaging data of a tissue sample. The system also includes a processor operatively coupled to the memory and configured to generate a four quadrant plot based on the hybrid imaging data. Each point in the four quadrant plot corresponds to an image voxel of the tissue sample. The processor is also configured to determine one or more angle values and one or more distance values for image voxels in the four quadrant plot. The processor is further configured to identify one or more characteristics of the tissue sample based at least in part on the one or more angle values and the one or more distance values. The processor is further configured to perform a matrix analysis of the data, which can be used to identify the one or more characteristics of the tissue sample.

We propose a neural network-implemented method for base calling analytes. The method includes accessing a sequence of per-cycle image patches for a series of sequencing cycles, where pixels in the image patches contain intensity data for associated analytes, and applying three-dimensional (3D) convolutions on the image patches on a sliding convolution window basis such that, in a convolution window, a 3D convolution filter convolves over a plurality of the image patches and produces at least one output feature. The method further includes beginning with output features produced by the 3D convolutions as starting input, applying further convolutions and producing final output features and processing the final output features through an output layer and producing base calls for one or more of the associated analytes to be base called at each of the sequencing cycles.

Various embodiments of a facial recognition system are provided. In one embodiment, a processor determines a value for a lighting parameter associated with a captured facial image, determines whether any previously obtained images in a biometric database includes a similar value for the lighting parameter and, if not, stores the newly captured image in the database along with the lighting parameter value. In another embodiment, the processor calculates a score indicative of the likelihood that the face in the captured facial image is identical to the face of a previously obtained image in the database, determines whether the score exceeds a threshold value and, if so, generates a signal indicating a match. The processor adjusts the threshold based on one or more parameter values.

Various embodiments of the disclosed subject matter provide systems, methods, architectures, mechanisms, apparatus, computer implemented method and/or frameworks configured for tracking Earth orbiting objects and adapting SSN tracking operations to improve tracking accuracy while reducing computational complexity and resource consumption associated with such tracking.

A model can be trained to detect interactions of other drivers through a window of their vehicle. A human driver behind a window (e.g., front windshield) of a vehicle can be detected in a real-world driving data. The human driver can be tracked over time through the window. The real-world driving data can be augmented by replacing at least a portion of the human driver with at least a portion of a virtual driver performing a target driver interaction to generate an augmented real-world driving dataset. The target driver interaction can be a gesture or a gaze. Using the augmented real-world driving data set, a machine learning model can be trained to detect the target driver interactions. Thus, simulation can be leveraged to provide a large set of useful training data without having to acquire real-world data of drivers performing target driver interactions as viewed from outside the vehicle.

An information processing apparatus that performs control related to movement of a moving object configured to measure its own position includes a memory storing instructions, and at least one processor that, upon execution of the instructions, is configured to operate as a first acquisition unit configured to acquire environmental information about an environment where the moving object moves, an estimation unit configured to estimate first positional information indicating that a region subjected to measurement accuracy degradation is below a threshold value based on the environmental information, and a determination unit configured to determine content of control information based on the first positional information.

An example system includes a processor to receive a data set and similarity scores. The processor is to execute an eigen response analysis on eigenvectors calculated for a similarity matrix generated based on the similarity scores for the data set. The processor is to output an estimated number of clusters in the data set based on the eigen response analysis.