In various examples, sensor data may be received that represents a field of view of a sensor of a vehicle located in a physical environment. The sensor data may be applied to a machine learning model that computes both a set of boundary points that correspond to a boundary dividing drivable free-space from non-drivable space in the physical environment and class labels for boundary points of the set of boundary points that correspond to the boundary. Locations within the physical environment may be determined from the set of boundary points represented by the sensor data, and the vehicle may be controlled through the physical environment within the drivable free-space using the locations and the class labels.

The present disclosure is directed to systems and methods for classifying biomedical images. A feature classifier may generate a plurality of tiles from a biomedical image. Each tile may correspond to a portion of the biomedical image. The feature classifier may select a subset of tiles from the plurality of tiles by applying an inference model. The subset of tiles may have highest scores. Each score may indicate a likelihood that the corresponding tile includes a feature indicative of the presence of the condition. The feature classifier may determine a classification result for the biomedical image by applying an aggregation model. The classification result may indicate whether the biomedical includes the presence or lack of the condition.

A data conversion processing unit converts a second group including a plurality of reflection point data units in which a reflection point corresponding to each reflection point data unit belongs to a three-dimensional object among a first data unit group into a third group including a plurality of projection point data units by projecting the second group onto a horizontal plane in a world coordinate system. A clustering processing unit clusters the plurality of projection point data units of the third group into a plurality of clusters based on positions of these units on the horizontal plane. A space of interest setting unit sets a space of interest for each cluster by using the plurality of reflection point data units corresponding to the plurality of projection point data units included in each cluster.

Provided is a position estimation device capable of highly accurate position estimation. A position estimation device 1 of the present invention is the position estimation device 1 which estimates a current position of a moving object 100 equipped with an imaging device 12, estimates the current position of the moving object 100, create a plurality of virtual positions based on the current position, creates virtual images at the plurality of virtual positions, respectively, compares the plurality of virtual images with an actual image to calculate a comparison error, calculates a weight based on at least one of information acquired by the imaging device 12 and information of a current position error of the moving object, performs weighting on the comparison error using the weight, and corrects the current position based on the comparison error to be weighted.

Identification means 71 identifies an object indicated by data by applying the data to a model learned by machine learning. Determination means 72 determines whether or not the data is transmission target data to be transmitted to a predetermined computer based on a result obtained by applying the data to the model. Data transmission means 73 transmits the data determined to be the transmission target data to the predetermined computer at a predetermined timing.

There is described a neural network system implemented by one or more computers for determining graph similarity. The neural network system comprises one or more neural networks configured to process an input graph to generate a node state representation vector for each node of the input graph and an edge representation vector for each edge of the input graph; and process the node state representation vectors and the edge representation vectors to generate a vector representation of the input graph. The neural network system further comprises one or more processors configured to: receive a first graph; receive a second graph; generate a vector representation of the first graph; generate a vector representation of the second graph; determine a similarity score for the first graph and the second graph based upon the vector representations of the first graph and the second graph.

A method for classifying images of oligolayer exfoliation attempts. In some embodiments, the method includes forming a micrograph of a surface, and classifying the micrograph into one of a plurality of categories. The categories may include a first category, consisting of micrographs including at least one oligolayer flake, and a second category, consisting of micrographs including no oligolayer flakes, the classifying comprising classifying the micrograph with a neural network.

A method for segmentation of a point cloud includes receiving a first frame of point cloud from a sensor; segmenting the first frame of point cloud to obtain a first set of point clusters representing a segmentation result for the first frame of point cloud; receiving a second frame of point cloud from the sensor; mapping the first set of point clusters to the second frame of point cloud; determining points within the second frame of point cloud which do not belong to the mapped first set of point clusters; segmenting the points within the second frame of point cloud which do not belong to the mapped first set of point clusters to obtain a second set of point clusters; and generating a segmentation result for the second frame of point cloud by combining the first set of point clusters and the second set of point clusters.

Some embodiments are directed to a data sampling device for obtaining a sample of records from a remote dataset satisfying a private criterion using multi-party computation. One or more sample providing devices store respective subdatasets of the remote dataset. The data sampling device determine a candidate size for a sample providing device; requests the sample providing device to determine a candidate sample of the candidate size from the subdataset of the sample providing device; perform a multi-party computation with the sample providing device to obtain a set of indices of records from the candidate sample satisfying the private criterion; sample a subset of the set of indices; and obtains from the sample providing device records of the candidate sample corresponding to the subset of the set of indices.

Embodiments herein provide for a machine learning algorithm that generates models that are more interpretable and transparent than existing machine learning approaches. These embodiments identify, at a record level, the effect of individual input variables on the machine learning model. To provide those improvements, a reason code generator assigns monotonic relationships to a series of input variables, which are then incorporated into the machine learning algorithm as metadata. In some embodiments, the reason code generator creates records based on the monotonic relationships, which are used by the machine learning algorithm to generate predicted values. The reason code generator compares an original predicted value from the machine learning model to the predicted values from the machine learning model.