Disclosed are a quantum system-based pattern recognition computation apparatus and method for artificial intelligence or machine learning. The computation apparatus recognizes patterns between input data by using a quantum system. The computation apparatus includes a modeling unit and an interpretation unit. The modeling unit sets up an objective function based on the similarity between a first pattern derived from the relationships between points of interests of a first data and a second pattern derived from the relationships between points of interests of a second data. The interpretation unit finds an optimum first pattern and an optimum second pattern, in which the similarity between the first pattern and the second pattern is optimized, by interpreting a final quantum state obtained through an adiabatic evolution process of the quantum system in which the objective function is optimized.

System and methods are provided for predicting a roadway feature. A geometric pattern in a roadway network is selected. The geometric pattern comprises one or more first links and one or more first nodes. A similar pattern to the geometric pattern is identified that comprises one or more second links and one or more second nodes in the roadway network. One or more features for the geometric pattern are identified. An absence of a feature in the similar pattern is determined based on the one or more features for the geometric pattern. A notification relating to the absence of the feature is generated.

An activity recognition system is disclosed. A plurality of temporal features is generated from a digital representation of an observed activity using a feature detection algorithm. An observed activity graph comprising one or more clusters of temporal features generated from the digital representation is established, wherein each one of the one or more clusters of temporal features defines a node of the observed activity graph. At least one contextually relevant scoring technique is selected from similarity scoring techniques for known activity graphs, the at least one contextually relevant scoring technique being associated with activity ingestion metadata that satisfies device context criteria defined based on device contextual attributes of the digital representation, and a similarity activity score is calculated for the observed activity graph as a function of the at least one contextually relevant scoring technique, the similarity activity score being relative to at least one known activity graph.

In various example embodiments, a system and method for determining an item that has confirmed characteristics are described herein. An image that depicts an object is received from a client device. Structured data that corresponds to characteristics of one or more items are retrieved. A set of characteristics is determined, the set of characteristics being predicted to match with the object. An interface that includes a request for confirmation of the set of characteristics is generated. The interface is displayed on the client device. Confirmation that at least one characteristic from the set of characteristics matches with the object depicted in the image is received from the client device.

A method and system is provided for generating a perception scene graph (PSG) having a focus region for a motor vehicle. Information is collected about a volume of space including surrounding areas adjacent a motor vehicle by a plurality of external sensors. The information is processed by a perception controller to generate the PSG having a virtual three-dimensional (3-D) model of the volume of space and area adjacent the motor vehicle. The perception controller is configured to allocate variable processing power to process selected portions of the collected sensor information. At least one focus region is defined. A focus region is a sub-set of the volume of space and/or area adjacent the motor vehicle. Processing power is increased by the perception controller to process the portions of the collected information relating to the focus region such that a high fidelity 3-D model of the focus region is generated.

An implantable medical device can include a device housing including a circuitry module and a header including a header core defining a bore configured to receive a distal end of a lead, an antenna, and a header shell disposed around the header core and the antenna. The antenna can be a closed loop antenna and arranged such that the antenna is positioned within two planes to maximize the area within the closed loop to increase the radiation characteristics of the antenna.

The image data analytics for computation configuration method and computer system with computation accessibility is provided. The computation accessibility and configuration on image data processing are implemented by a plurality of computers having a plurality of processors and a plurality of data storages, the image data analytics for computation accessibility and configuration includes the following steps: inputting an original image by an input device and initializing the original image; defining a plurality of tiles equally dividing the original image into a same size of a regular shape; transferring the plurality of image regions to form a graph having vertices and edges; cutting the graph into a plurality of sub-graphs; arranging the plurality of sub-graphs to the plurality of processors or cores to conduct parallel processing simultaneously for analyzing the image data; and storing a plurality of processing results respectively in the plurality of data storages.

Described herein are methods and systems for closed-form 3D model generation of non-rigid complex objects from scans with large holes. A computing device receives (i) a partial scan of a non-rigid complex object captured by a sensor coupled to the computing device; (ii) a partial 3D model corresponding to the object, and (iii) a whole 3D model corresponding to the object, wherein the partial 3D scan and the partial 3D model each includes one or more large holes. The device performs a rough match on the partial 3D model and changes the whole 3D model using the rough match to generate a deformed 3D model. The device refines the deformed 3D model using a deformation graph, reshapes the refined deformed 3D model to have greater detail, and adjusts the whole 3D model according to the reshaped 3D model to generate a closed-form 3D model that closes holes in the scan.

An object recognition system is provided. The object recognition system for recognizing an object may include an input unit to receive, as an input, a depth image representing an object to be analyzed, and a processing unit to recognize a visible object part and a hidden object part of the object, from the depth image, by using a classification tree. The object recognition system may include a classification tree learning apparatus to generate the classification tree.

Methods, systems, articles of manufacture, and apparatus to recalibrate confidences for image classification are disclosed. An example apparatus to classify an image includes an image crop detector to detect a first image crop from the image, the first image crop corresponding to a first object, a grouping controller to select a second image crop corresponding to a second object at a location of the first object, a prediction generator to, in response to executing a trained model, determine a label corresponding to the first object and a confidence level associated with the label, and a confidence recalibrator to recalibrate the confidence level based on a probability of the first object having a first attribute based on the second object having a second attribute, the confidence level recalibrated to increase an accuracy of the image classification.