For information about point cloud data, a point cloud data sameness estimation apparatus and a point cloud data sameness estimation system in which accuracy of evaluating sameness is improved are provided. In the present disclosure, a point cloud data sameness estimation apparatus for estimating sameness of objects that are sources of two 3-dimensional point cloud datasets includes a point cloud data acquisition unit configured to acquire first point cloud data and second point cloud data including 3-dimensional point cloud data; a first neural network configured to output a first point cloud data feature, with information about the first point cloud data as an input into the first neural network; a second neural network configured to output a second point cloud data feature, with information about the second point cloud data as an input into the second neural network; and a sameness evaluation unit configured to output an evaluation about sameness of the first point cloud data and the second point cloud data, based on the first point cloud data feature and the second point cloud data feature, wherein a weight is mutually shared by the first neural network and the second neural network.

Systems, methods and apparatus of recordation of vehicle data associated with errant vehicle behavior. For example, a vehicle includes: sensors configured to generate sensor data; control elements configured to generate control signals to be applied to the vehicle in response to user interactions with the control elements; electronic control units configured to provide status data in operations of the electronic control units; and a data storage device. The data storage device is configured to receive input data including the sensor data, the control signals and the status data, store the input data in a cyclic way in an input partition over time, generate a classification of errant behavior based on the input data and using an artificial neural network, and preserve a portion of the input data associated with the classification of errant behavior.

Systems, methods and apparatus of recordation of vehicle data associated with errant vehicle behavior. For example, a vehicle includes: sensors configured to generate sensor data; control elements configured to generate control signals to be applied to the vehicle in response to user interactions with the control elements; electronic control units configured to provide status data in operations of the electronic control units; and a data storage device. The data storage device is configured to receive input data including the sensor data, the control signals and the status data, store the input data in a cyclic way in an input partition over time, generate a classification of errant behavior based on the input data and using an artificial neural network, and preserve a portion of the input data associated with the classification of errant behavior.

The road obstacle detection device includes a semantic label estimation unit that estimates a semantic label for each pixel of an image using a classifier learned in advance and generates a semantic label image, an original image estimation unit for reconstruction of the original image from the semantic label image, a difference calculating unit for calculating a difference between the original image and the reconstructed image from the original image estimation unit as a calculation result, and a road obstacle detection unit for detecting a road obstacle based on the calculation result.

The road obstacle detection device includes a semantic label estimation unit that estimates a semantic label for each pixel of an image using a classifier learned in advance and generates a semantic label image, an original image estimation unit for reconstruction of the original image from the semantic label image, a difference calculating unit for calculating a difference between the original image and the reconstructed image from the original image estimation unit as a calculation result, and a road obstacle detection unit for detecting a road obstacle based on the calculation result.

A method comprising: obtaining an image; identifying a rotation angle for the image by processing the image with a first neural network; rotating the image by the identified rotation angle to generate a rotated image; classifying the image with a second neural network; and outputting an indication of an outcome of the classification, wherein the first neural network is trained, at least in part, based on a categorical distance between training data and an output that is produced by the first neural network.

An example apparatus for semantic image segmentation includes a receiver to receive an image to be segmented. The apparatus also includes a gated dense pyramid network including a plurality of gated dense pyramid (GDP) blocks to be trained to generate semantic labels for respective pixels in the received image. The apparatus further includes a generator to generate a segmented image based on the generated semantic labels.

A method to adaptively predict non-default actions against unstructured utterances by an automated assistant operating in a computing-system is provided. The method includes extracting voice-features based on receiving an input utterance from at-least one speaker by an automatic speech recognition (ASR) device, identifying the input utterance as an unstructured utterance based on the extracted voice-features and a mapping between the input utterance with one or more default actions as drawn by the ASR, obtaining at least one probable action to be performed in response to the unstructured utterance through a dynamic bayesian network (DBN). The method further includes providing the at least one probable action obtained by the DBN to the speaker in an order of the posterior probability with respect to each action.

A computer-implemented method for generating an abstract meaning representation (“AMR”) of a sentence, comprising receiving, by a computing device, an input sentence and parsing the input sentence into one or more syntactic and/or semantic graphs. An input graph including a node set and an edge set is formed from the one or more syntactic and/or semantic graphs. Node representations are generated by natural language processing. The input graph is provided to a first neural network to provide an output graph having learned node representations aligned with the node representations in the input graph. The method further includes predicting via a second neural network, node label and predicting, via a third neural network, edge labels in the output graph. The AMR is generated based on the predicted node labels and predicted edge labels. A system and a non-transitory computer readable storage medium are also disclosed.

A slicer in a material drop ejecting three-dimensional (3D) object printer generates machine ready instructions that operate components of a printer, such as actuators and an ejector having at least one nozzle, to form features of an object more precisely than previously known. The instructions generated by the slicer control the actuators to move the ejector and a platform on which the object is formed relative to one another at a constant velocity to form edges of the feature.