Abstract: A live map system may be used to propagate observations collected by autonomous vehicles operating in an environment to other autonomous vehicles and thereby supplement a digital map used in the control of the autonomous vehicles. In addition, a live map system in some instances may be used to propagate location-based teleassist triggers to autonomous vehicles operating within an environment. A location-based teleassist trigger may be generated, for example, in association with a teleassist session conducted between an autonomous vehicle and a remote teleassist system proximate a particular location, and may be used to automatically trigger a teleassist session for another autonomous vehicle proximate that location and/or to propagate a suggested action to that other autonomous vehicle.

Abstract: The present invention relates to a Lactobacillus spp. selective prebiotic composition comprising one, or a mixture of two or more, of: xylooligosaccharides, cellobiose and/or gentiooligosaccharides. The present invention also relates to a synbiotic composition comprising a probiotic component comprising one or more strains of Lactobacillus rhamnosus and/or one or more strains of Lactobacillus plantarum and a prebiotic component comprising a growth medium which is specific for the growth of the probiotic component, wherein the prebiotic growth medium comprises one or more, or a mixture of two of more, components selected from: xylooligosaccharides, cellobiose and/or gentiooligosaccharides. The present invention also relates to methods of producing and selecting such compositions.

Abstract: An approach is provided for selecting training observations for machine learning models. The approach involves determining a first distribution of a plurality of features observed in the training data set, and a second distribution of the plurality of features observed in the candidate pool of observations. The approach further involves selecting one or more observations in the candidate pool of observations for annotation based on the first distribution and the second distribution. The approach further involves adding the one or more observations to the training data set after annotation. The training data set is used for training the machine learning model.

Abstract: Synthetic training information/data of a second probe style is generated based on first probe information/data of a first probe style using a style transfer model. First probe information/data is defined. An instance of first probe information/data comprises labels and first probe style sensor information/data. A style transfer model generates training information/data based on at least a portion of the first probe information/data. An instance of training information/data corresponds to an instance of first probe information/data and comprises second probe style sensor information/data. The first and second probe styles are different. A second probe style model is trained using machine learning and the training information/data. The second probe style model is used to analyze second probe style second probe information/data to extract map information/data from the second probe information/data. Each instance of second probe data is captured by one or more second probe sensors of a second probe apparatus.

Abstract: An apparatus, method and computer program product are provided to train a feature detector to identify a respective feature from images captured by a camera. With respect to an apparatus, the apparatus causes at least one feature from one or more images that have been labelled to be projected onto a map. The apparatus is also caused to refine a representation of a path of a vehicle that carries a camera that captured the one or more images based upon registration of the at least one feature that has been projected with the map. Based upon the path of the vehicle following refinement, the apparatus projects one or more other features that have not been labelled from the map into the one or more images and then utilizes the images to train a feature detector.

Abstract: Synthetic training information/data of a second probe style is generated based on first probe information/data of a first probe style using a style transfer model. First probe information/data is defined. An instance of first probe information/data comprises labels and first probe style sensor information/data. A style transfer model generates training information/data based on at least a portion of the first probe information/data. An instance of training information/data corresponds to an instance of first probe information/data and comprises second probe style sensor information/data. The first and second probe styles are different. A second probe style model is trained using machine learning and the training information/data. The second probe style model is used to analyze second probe style second probe information/data to extract map information/data from the second probe information/data. Each instance of second probe data is captured by one or more second probe sensors of a second probe apparatus.

Abstract: The present invention relates to a Lactobacillus spp. selective prebiotic composition comprising one, or a mixture of two or more, of: xylooligosaccharides, cellobiose and/or gentiooligosaccharides. The present invention also relates to a synbiotic composition comprising a probiotic component comprising one or more strains of Lactobacillus rhamnosus and/or one or more strains of Lactobacillus plantarum and a prebiotic component comprising a growth medium which is specific for the growth of the probiotic component, wherein the prebiotic growth medium comprises one or more, or a mixture of two of more, components selected from: xylooligosaccharides, cellobiose and/or gentiooligosaccharides. The present invention also relates to methods of producing and selecting such compositions.

Abstract: An approach is provided for dynamic adaptation of an in-vehicle feature detector. The approach involves embedding a feature detection model, precomputed weights for the feature detection model, or a combination thereof in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof. The approach also involves deploying the feature detection model, the precomputed weights, or a combination thereof to adapt an in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof. The in-vehicle feature detector can then use the feature detection model, the precomputed weights, or a combination thereof to process sensor data collected while in the geographic area to detect one or more features.

Abstract: An apparatus, method and computer program product are provided to train a feature detector to identify a respective feature from images captured by a camera. With respect to an apparatus, the apparatus causes at least one feature from one or more images that have been labelled to be projected onto a map. The apparatus is also caused to refine a representation of a path of a vehicle that carries a camera that captured the one or more images based upon registration of the at least one feature that has been projected with the map. Based upon the path of the vehicle following refinement, the apparatus projects one or more other features that have not been labelled from the map into the one or more images and then utilizes the images to train a feature detector.

Abstract: Synthetic training information/data of a second probe style is generated based on first probe information/data of a first probe style using a style transfer model. First probe information/data is defined. An instance of first probe information/data comprises labels and first probe style sensor information/data. A style transfer model generates training information/data based on at least a portion of the first probe information/data. An instance of training information/data corresponds to an instance of first probe information/data and comprises second probe style sensor information/data. The first and second probe styles are different. A second probe style model is trained using machine learning and the training information/data. The second probe style model is used to analyze second probe style second probe information/data to extract map information/data from the second probe information/data. Each instance of second probe data is captured by one or more second probe sensors of a second probe apparatus.

Abstract: An approach is provided for dynamic adaptation of an in-vehicle feature detector. The approach involves embedding a feature detection model, precomputed weights for the feature detection model, or a combination thereof in a data layer of map data representing a geographic area from which a training data set was collected to generate the feature detection model, the precomputed weights, or a combination thereof. The approach also involves deploying the feature detection model, the precomputed weights, or a combination thereof to adapt an in-vehicle feature detector based on determining that the in-vehicle feature detector is in the geographic area, plans to travel in the geographic area, or a combination thereof. The in-vehicle feature detector can then use the feature detection model, the precomputed weights, or a combination thereof to process sensor data collected while in the geographic area to detect one or more features.

Abstract: An approach is provided for selecting training observations for machine learning models. The approach involves determining a first distribution of a plurality of features observed in the training data set, and a second distribution of the plurality of features observed in the candidate pool of observations. The approach further involves selecting one or more observations in the candidate pool of observations for annotation based on the first distribution and the second distribution. The approach further involves adding the one or more observations to the training data set after annotation. The training data set is used for training the machine learning model.

Abstract: Systems and methods are provided to incentivize collection of high definition (HD) map content. The system includes at least one sensor, a communications interface, and a device processor. The sensor is configured to acquire sensor data relating to a feature at a location on a roadway. The communications interface is configured to communicate with at least one other device. The device processor is configured to generate an observation data package from the sensor data, perform, using the location, a spatial query on a blockchain configured to store a plurality of data entries to identify whether any data entries of the plurality of data entries exist for the observation data package. When no data entries exist, the device processor is configured to generate a new data entry for the blockchain for the observation data package, and when a data entry exists, validate the observation data package and augment the existing data entry.

Abstract: A system for performing global scale object detection using satellite imagery, comprising an object detection server that receives and analyzes image data to identify objects within an image via a curated computational method, and a curation interface that enables a user to curate image information for use in object identification, and a method for a curated computational method for performing global scale object detection.

Abstract: A system for performing global scale object detection using satellite imagery, comprising an object detection server that receives and analyzes image data to identify objects within an image via a curated computational method, and a curation interface that enables a user to curate image information for use in object identification, and a method for a curated computational method for performing global scale object detection.

Abstract: A process for analysing a biological sample, comprising the steps of: (a) identifying a micro-organism present within the sample; and (b) determining the effect of one or more antimicrobial(s) on a micro-organism from the sample, wherein steps (a) and (b) are performed by analysing the micro-organism's nucleic acid. Steps (a) and (b) will generally occur in that order, but they may take place concurrently. The steps may advantageously be performed within a single apparatus. Conveniently, the nucleic acid analyte used for step (a) is the same as that used in step (b) e.g. the same PCR amplicon. A micro-organism's nucleic acids can thus be used both to identify the presence of the micro-organism within a sample and then to assess the effect of antimicrobials on its growth.

Abstract: The present disclosure includes a turbocharger. The turbocharger may include a titanium-aluminide turbine and a shaft. A single joint connects the turbine to the shaft. The joint may include an alloy comprising at least 80 atomic percent nickel and palladium.

Abstract: A process for analysing a biological sample, comprising the steps of: (a) identifying a micro-organism present within the sample; and (b) determining the effect of one or more antimicrobial(s) on a micro-organism from the sample, wherein steps (a) and (b) are performed by analysing the micro-organism's nucleic acid. Steps (a) and (b) will generally occur in that order, but they may take place concurrently. The steps may advantageously be performed within a single apparatus. Conveniently, the nucleic acid analyte used for step (a) is the same as that used in step (b) e.g. the same PCR amplicon. A micro-organism's nucleic acids can thus be used both to identify the presence of the micro-organism within a sample and then to assess the effect of antimicrobials on its growth.

Abstract: The present disclosure includes a turbocharger. The turbocharger may include a turbine that includes titanium-aluminide and a shaft that includes titanium. A single joint connects the turbine to the shaft.