Abstract: An exemplary method comprises calculating products, each product being generated by multiplying a first result given by applying one of a plurality of input data used for training a machine learning model to a first function that represents a forward propagation of the plurality of input data through the machine learning model, and a second result given by applying the one of the plurality of input data to a second function that represents a backward propagation of a partial differential of a loss therethrough, the loss being generated by summing errors between desired values that the machine learning model should output in response to the plurality of input data and prediction values that the machine learning model outputs in response thereto; and detecting among the plurality of input data, input data that contributes the most to the loss, based on magnitudes of the products.

Abstract: System and method for an active learning system including a sensor obtains data from a scene including a set of images having objects. A memory to store active learning data including an object detector trained for detecting objects in images. A processor in communication with the memory, is configured to detect a semantic class and a location of at least one object in an image selected from the set of images using the object detector to produce a detection metric as a combination of an uncertainty of the object detector about the semantic class of the object in the image (classification) and an uncertainty of the object detector about the location of the object in the image (localization). Using an output interface or a display type device, in communication with the processor, to display the image for human labeling when the detection metric is above a threshold.

Abstract: An active learning system classifies multiple objects in an input image from the set of images with a classification metric indicative of uncertainty of each of the classified object to belong to one or different classes and determines a diversity metric of the input image indicative of diversity of the objects classified in the input image. The active learning system evaluates the diversity metric of the input image and to cause rendering of the input image on a display device based on a result of the evaluation and trains the classifier using the labelled objects of the input image.

Abstract: An active learning system classifies multiple objects in an input image from the set of images with a classification metric indicative of uncertainty of each of the classified object to belong to one or different classes and determines a diversity metric of the input image indicative of diversity of the objects classified in the input image. The active learning system evaluates the diversity metric of the input image and to cause rendering of the input image on a display device based on a result of the evaluation and trains the classifier using the labelled objects of the input image.

Abstract: Systems and methods for an imaging system having a memory with a historical localization dictionary database having geo-located driving image sequences, such that each reference image is applied to a threshold to produce a binary representation. A sensor to acquire a sequence of input images of a dynamic scene. An encoder to determine, for each input image in the sequence, a histogram of each input image indicating a number of vertical edges at each bin of the input image and to threshold the histogram to produce a binary representation of the input image. A visual odometer to compare the binary representations of each input image and each reference image, by matching an input image against a reference image. Wherein the visual odometer determines a location of the input image based on a match between the input image and the reference image.

Abstract: An image processing system includes a memory to store a classifier and a set of labeled images for training the classifier, wherein each labeled image is labeled as either a positive image that includes an object of a specific type or a negative image that does not include the object of the specific type, wherein the set of labeled images has a first ratio of the positive images to the negative images. The system includes an input interface to receive a set of input images, a processor to determine a second ratio of the positive images, to classify the input images into positive and negative images to produce a set of classified images, and to select a subset of the classified images having the second ratio of the positive images to the negative images, and an output interface to render the subset of the input images for labeling.

Abstract: A distributed machining simulation partitions a model of the workpiece to produce a set of sub-workpieces and modifies the machining instructions for each sub-workpiece to produce a set of groups of modified machining instructions different from the machining instructions. Each group of the modified machining instructions is associated with a sub-workpiece and includes effective machining instructions that move the machining tool through the sub-workpiece and at least one fake machining instruction that moves the machining tool through the model of the workpiece outside of the sub-workpiece.

Abstract: A system for assigning a semantic label to a three-dimensional (3D) point of a point cloud includes an interface to transmit and receive data via a network, a processor connected to the interface, a memory storing program modules including a communicator and a labeler executable by the processor. In this case, the communicator causes the processor to perform operations including establishing, using the interface, a communication with an external map server storing a map defined in a map coordinate system, determining a location on the map corresponding to the 3D point, and querying the map using the location to obtain an information relevant to the semantic label at the location in the map, wherein the labeler causes the processor to determine the semantic label of the 3D point using the information.

Abstract: System and method for an active learning system including a sensor obtains data from a scene including a set of images having objects. A memory to store active learning data including an object detector trained for detecting objects in images. A processor in communication with the memory, is configured to detect a semantic class and a location of at least one object in an image selected from the set of images using the object detector to produce a detection metric as a combination of an uncertainty of the object detector about the semantic class of the object in the image (classification) and an uncertainty of the object detector about the location of the object in the image (localization). Using an output interface or a display type device, in communication with the processor, to display the image for human labeling when the detection metric is above a threshold.

Abstract: Systems and methods for an imaging system having a memory with a historical localization dictionary database having geo-located driving image sequences, such that each reference image is applied to a threshold to produce a binary representation. A sensor to acquire a sequence of input images of a dynamic scene. An encoder to determine, for each input image in the sequence, a histogram of each input image indicating a number of vertical edges at each bin of the input image and to threshold the histogram to produce a binary representation of the input image. A visual odometer to compare the binary representations of each input image and each reference image, by matching an input image against a reference image. Wherein the visual odometer determines a location of the input image based on a match between the input image and the reference image.

Abstract: A distributed machining simulation partitions a model of the workpiece to produce a set of sub-workpieces and modifies the machining instructions for each sub-workpiece to produce a set of groups of modified machining instructions different from the machining instructions. Each group of the modified machining instructions is associated with a sub-workpiece and includes effective machining instructions that move the machining tool through the sub-workpiece and at least one fake machining instruction that moves the machining tool through the model of the workpiece outside of the sub-workpiece.

Abstract: An image processing system includes a memory to store a classifier and a set of labeled images for training the classifier, wherein each labeled image is labeled as either a positive image that includes an object of a specific type or a negative image that does not include the object of the specific type, wherein the set of labeled images has a first ratio of the positive images to the negative images. The system includes an input interface to receive a set of input images, a processor to determine a second ratio of the positive images, to classify the input images into positive and negative images to produce a set of classified images, and to select a subset of the classified images having the second ratio of the positive images to the negative images, and an output interface to render the subset of the input images for labeling.

Abstract: Systems and methods for radiation treatment for determining a dose of radiation. Determining, for a tumor voxel, a value for maximum and minimum constraints, for treating the tumor voxel based on a distance function of a distance field of the tumor and distance fields of organs at risk (OARs). Each constraint on each tumor voxel is a function of a distance from the tumor voxel to a boundary of the tumor, and distances to boundaries of OARs. Determining, for an OAR voxel, a value of a maximum constraint based on the distance field of the tumor. Each constraint on each OAR voxel is a function of a distance from the OAR voxel to the boundary of the tumor. Determining a tumor constraint and a corresponding OAR constraint according to a threshold constraint set, to obtain the constraint set. Determine the dose of radiation, according to the constraint set.

Abstract: A method estimates a pattern of change of a patient, specifically a change in the respiration pattern. An ultrasound video is segmented into groups of pictures (GOPs). Pixels from the first GOP are used to initialize a change model. Based on the change model, a change pattern for a next GOP is estimated, and the change model is changed to fit the change pattern. The estimating and the updating are repeated until a termination condition is reached.

Abstract: A method estimates a pattern of change of a patient, specifically a change in the respiration pattern. An ultrasound video is segmented into groups of pictures (GOPs). Pixels from the first GOP are used to initialize a change model. Based on the change model, a change pattern for a next GOP is estimated, and the change model is changed to fit the change pattern. The estimating and the updating are repeated until a termination condition is reached.

Abstract: A method estimates a pattern of change of a patient, specifically a change in the respiration pattern. An ultrasound video is segmented into groups of pictures (GOPs). Pixels from the first GOP are used to initialize a change model. Based on the change model, a change pattern for a next GOP is estimated, and the change model is changed to fit the change pattern. The estimating and the updating are repeated until a termination condition is reached.