Abstract: Embodiments of the present disclosure relate to a method, system, and computer program product for domain adaptation. According to the method, a source model of a source domain is obtained, where the source model is trained to generate a label indicating a predicted category of data from the source domain. A training sample from a target domain is obtained, where the training sample comprises training data from the target domain and a true label indicating a true category of the training data from the target domain. A first label is generated for the training data by using the source model. The first label indicates a predicted category of the training data. A target model of the target domain is trained based on the training data, the true label and the first label.

Abstract: In an aspect for generating a 2-dimensional (2D) building mapping, a computer-implemented method may include one or more processors configured for receiving scanning data in a first file format corresponding to 3-dimensional (3D) aerial Light Detection and Ranging (LiDaR) images. Further, the method may include transforming the first file format to a second file format and transmitting the scanning data to a building semantic segmentation component. Further, the computer-implemented method may include generating semantic segmentation data corresponding to building structures defined by point clusters; generating instance segmentation data corresponding to top surfaces of the building structures; generating building boundary data for each one of the point clusters based on one or more of the semantic segmentation data and the instance segmentation data; and processing the building boundary data to generate a 2D building mapping comprising refined boundary lines for each of the point clusters.

Abstract: Analysis of edge closures of metal surface particles based on a graph structure.

Abstract: The present disclosure relates to a method, system and computer program product for electronic health record (EHR) data synthetization. According to the method, an original EHR dataset X is captured. A latent space Z is generated from the original EHR dataset X, wherein dimensionality of Z is lower than that of X. A stochastic process prior module is applied to the latent space Z. Synthetic EHR dataset X? is reconstructed from the latent space Z after being applied with the stochastic process prior.

Abstract: An approach is provided in which the approach receives an image that includes multiple image points and constructs a plane in the image based on a first subset of the plurality of image points. The approach identifies a second subset of the image points that belong to the plane and are not part of the first subset of image points, and removes the first subset of image points and the second subset of image points form the image points. The approach annotates the remaining subset of image points in the image.

Abstract: Aspects of the invention include a computer-implemented method including extracting a domain-specific object feature from a first image data, wherein the feature describes an object in the first image data. A domain-specific semantic meaning of text data is determined. The object feature is mapped to a portion of the text data, wherein the portion of the text data describes the object. A joint representation of the object and the portion of the text data is created. A second image data and a query directed towards an object in the second image data is received. An answer to the query is generated based on the joint representation.

Abstract: Analysis of edge closures of metal surface particles based on a graph structure.

Abstract: An approach is provided in which the approach receives an image that includes multiple image points and constructs a plane in the image based on a first subset of the plurality of image points. The approach identifies a second subset of the image points that belong to the plane and are not part of the first subset of image points, and removes the first subset of image points and the second subset of image points form the image points. The approach annotates the remaining subset of image points in the image.

Abstract: In an aspect for generating a 2-dimensional (2D) building mapping, a computer-implemented method may include one or more processors configured for receiving scanning data in a first file format corresponding to 3-dimensional (3D) aerial Light Detection and Ranging (LiDaR) images. Further, the method may include transforming the first file format to a second file format and transmitting the scanning data to a building semantic segmentation component. Further, the computer-implemented method may include generating semantic segmentation data corresponding to building structures defined by point clusters; generating instance segmentation data corresponding to top surfaces of the building structures; generating building boundary data for each one of the point clusters based on one or more of the semantic segmentation data and the instance segmentation data; and processing the building boundary data to generate a 2D building mapping comprising refined boundary lines for each of the point clusters.

Abstract: A model learning and sample value generating framework includes a system and method to comprehensively integrate encoding, decoding and value predicting, and optimizing functions to reconstruct as accurate as possible an original input sample data space. The system leverages a variational autoencoder model to generate as realistic samples of that data space as possible. The system learns a value prediction function to achieve a target outcome based on the latent feature data instead of the original input data. Further, the system solves the optimization problem in the latent space without constraints to avoid the difficulty in optimizing in the original sample data space. The generated optimal samples are as similar as possible to the real-world input samples. The system provides a flexible data generation mechanism which is suitable for various kinds of target outcome specifications.

Abstract: Aspects of the invention include a computer-implemented method including extracting a domain-specific object feature from a first image data, wherein the feature describes an object in the first image data. A domain-specific semantic meaning of text data is determined. The object feature is mapped to a portion of the text data, wherein the portion of the text data describes the object. A joint representation of the object and the portion of the text data is created. A second image data and a query directed towards an object in the second image data is received. An answer to the query is generated based on the joint representation.

Abstract: Embodiments of the present disclosure relate to a method, system, and computer program product for domain adaptation. According to the method, a source model of a source domain is obtained, where the source model is trained to generate a label indicating a predicted category of data from the source domain. A training sample from a target domain is obtained, where the training sample comprises training data from the target domain and a true label indicating a true category of the training data from the target domain. A first label is generated for the training data by using the source model. The first label indicates a predicted category of the training data. A target model of the target domain is trained based on the training data, the true label and the first label.

Abstract: The present disclosure relates to a method, system and computer program product for electronic health record (EHR) data synthetization. According to the method, an original EHR dataset X is captured. A latent space Z is generated from the original EHR dataset X, wherein dimensionality of Z is lower than that of X. A stochastic process prior module is applied to the latent space Z. Synthetic EHR dataset X? is reconstructed from the latent space Z after being applied with the stochastic process prior.

Abstract: Prioritizing resource allocation to computer applications which includes: grouping the computer applications into groups according to an initial criteria; modifying the groups according to one or more criteria used to identify active computer applications; analyzing the groups to prioritize the groups in order of the active time of the computer applications in the groups; analyzing the computer applications in the groups to prioritize the computer applications in the groups in order of the active time of the computer applications in the groups; setting the highest priority for the computer applications that either (1) have high frequency of use, or (1) are active now; and prioritizing the computer applications according to the priority setting.

Abstract: Prioritizing resource allocation to computer applications which includes: grouping the computer applications into groups according to an initial criteria; modifying the groups according to one or more criteria used to identify active computer applications; analyzing the groups to prioritize the groups in order of the active time of the computer applications in the groups; analyzing the computer applications in the groups to prioritize the computer applications in the groups in order of the active time of the computer applications in the groups; setting the highest priority for the computer applications that either (1) have high frequency of use, or (1) are active now; and prioritizing the computer applications according to the priority setting.