Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, are described for resolving duplicate images. In one aspect, a method includes obtaining a selection of a single image from among a plurality of duplicate images. Each duplicate image has an associated set of metadata. The method also includes aggregating each set of metadata into aggregated information, and storing the selected image together with the aggregated information on data storage accessible to a data processing apparatus.

Data associated with an object is obtained, where the object is associated with an individual and the data includes first image data that represents the object. Second image data is obtained from a device associated with the individual. As a result of an analysis of multiple images associated with the individual that include the first image data, the second image data is determined to also represent the object. An attestation that attests to the object being associated with the individual is provided.

Techniques described herein provide intelligent context-based testing. One or more implementations receive an image that includes content. In turn, some implementations process the image to extract test information from the content, such as questions, answers, learning material, and so forth. By analyzing the test information, various implementations determine one or more characteristics associated with the test information, and dynamically generate new test information based on the determined one or more characteristics. As one example, some implementations obtain new content by searching for content that includes the one or more characteristics and generate new test information based on the new content and the extracted test information.

Techniques described herein provide intelligent context-based testing. One or more implementations receive an image that includes content. In turn, some implementations process the image to extract test information from the content, such as questions, answers, learning material, and so forth. By analyzing the test information, various implementations determine one or more characteristics associated with the test information, and dynamically generate new test information based on the determined one or more characteristics. As one example, some implementations obtain new content by searching for content that includes the one or more characteristics and generate new test information based on the new content and the extracted test information.

In some aspects, the described systems and methods provide for a method for training a model to predict survival time for a patient. The method includes accessing annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.

Aspects include a system, computer program production and computer-implemented method for tagging an image. An image classification engine stored in a memory of a computer device generates a plurality of tags for the image and uses the plurality of tags to generate a relevance subgraph for the image. An embedding engine embeds nodes and edges of the relevance subgraph into fixed dimension vectors of a matrix. A neural network stored in the memory determines a feature vector from the image. A processor applies the feature vector to the matrix to generate a context vector for the image. The context vector is used to tag the image.

A method and system for providing a data analysis in the form of a customized geographic visualization on a graphical user interface (GUI) on a remote client computing device using only a web browser on the remote client device. The system receives a user's selected data analysis to be performed by the system for display on the remote client device. The system verifies the data access permissions of the user to render a data analysis solution customized to that particular user, and automatically prevents that user from gaining access to data analysis solutions to which that user is prohibited. The system is configured to respond to the user's data analysis request, perform the necessary computations on the server side on the fly, and send a dataset interpretable by the client device's web browser for display on the client device or on a device associated with the client device.

A head mounted display device is provided that includes a display device, a camera device, an input device, and a processor. The processor is configured to store a database of physical objects and virtual objects that have been associated with one or more semantic tags. The processor is further configured to receive a natural language input from a user via the input device and perform semantic processing on the natural language input to determine a user specified operation and identify one or more semantic tags indicated by the natural language input. The processor is further configured to select a target virtual object and a target physical object based on the identified one or more semantic tags, perform the determined user specified operation on the target virtual object based on the target physical object, and display the target virtual object at a physical location associated with the target physical object.

An electronic computing device includes a processing unit and system memory. The system memory includes instructions which, when executed by the processing unit, cause the electronic computing device to receive data associated with one or more customers of an institution. The data is received from one or more other electronic computing devices. The received data is analyzed to identify objects in the data. Relationships are derived between the objects in the data. One or more images depicting the objects are obtained from the Internet. Information regarding the objects is sent to a display device. The information includes the images depicting the objects and the relationships between the objects.

An electronic computing device includes a processing unit and system memory. The system memory includes instructions which, when executed by the processing unit, cause the electronic computing device to receive data associated with one or more customers of an institution. The data is received from one or more other electronic computing devices. The received data is analyzed to identify objects in the data. Relationships are derived between the objects in the data. One or more images depicting the objects are obtained from the Internet. Information regarding the objects is sent to a display device. The information includes the images depicting the objects and the relationships between the objects.