Abstract: System and method for fine granularity control of data access and usage for across multi-tenant systems. A user makes a request to access a particular set of data from a particular remote data source for a specific purpose. The system authorizes the user to validate whether the user is qualified to make the request. The data source is checked to see if the particular data has been granted access for that particular purpose. A cloud neutral token is created and converted into a cloud specific token upon reaching the remote data source. The cloud specific token is used to create a temporary IAM role and IAM policy with a predetermined time to live. After the time to live expires, the IAM role and IAM policy are deleted.

Abstract: System and method for fine granularity control of data access and usage for across multi-tenant systems. A user makes a request to access a particular set of data from a particular remote data source for a specific purpose. The system authorizes the user to validate whether the user is qualified to make the request. The data source is checked to see if the particular data has been granted access for that particular purpose. A cloud neutral token is created and converted into a cloud specific token upon reaching the remote data source. The cloud specific token is used to create a temporary IAM role and IAM policy with a predetermined time to live. After the time to live expires, the IAM role and IAM policy are deleted.

Abstract: System and method for fine granularity control of data access and usage for across multi-tenant systems. A user makes a request to access a particular set of data from a particular remote data source for a specific purpose. The system authorizes the user to validate whether the user is qualified to make the request. The data source is checked to see if the particular data has been granted access for that particular purpose. A cloud neutral token is created and converted into a cloud specific token upon reaching the remote data source. The cloud specific token is used to create a temporary IAM role and IAM policy with a predetermined time to live. After the time to live expires, the IAM role and IAM policy are deleted.

Abstract: System and method for fine granularity control of data access and usage for across multi-tenant systems. A user makes a request to access a particular set of data from a particular remote data source for a specific purpose. The system authorizes the user to validate whether the user is qualified to make the request. The data source is checked to see if the particular data has been granted access for that particular purpose. A cloud neutral token is created and converted into a cloud specific token upon reaching the remote data source. The cloud specific token is used to create a temporary IAM role and IAM policy with a predetermined time to live. After the time to live expires, the IAM role and IAM policy are deleted.

Abstract: A method for natural language processing includes receiving, by one or more processors, an unstructured text input. An entity classifier is used to identify entities in the unstructured text input. The identifying the entities includes generating, using a plurality of sub-classifiers of a hierarchical neural network classifier of the entity classifier, a plurality of lower-level entity identifications associated with the unstructured text input. The identifying the entities further includes generating, using a combiner of the hierarchical neural network classifier, a plurality of higher-level entity identifications associated with the unstructured text input based on the plurality of lower-level entity identifications. Identified entities are provided based on the plurality of higher-level entity identifications.

Abstract: A virtual dataset may be created in a database system. The virtual dataset may include data items stored in a storage system that are each associated with a respective label. The virtual dataset may include a first changeset identifying the data items, and may be updated to include a second changeset identifying different data items later stored in the storage system and included in the virtual dataset. Access to a learning dataset that includes either the first changeset, the second changeset, or both, may be provided upon request.

Abstract: A request to access a virtual dataset identifying one or more changeset selection criteria may be received. One or more changesets may be selected based on the selection criteria. Each changeset may correspond with a point in time and may include data references to data items added to the virtual dataset at the point in time. A learning dataset that includes a plurality of data items may be identified.

Abstract: Systems and methods which provide Generative Adversarial Network (GAN) based intra prediction for video coding are described. GAN based intra prediction as implemented according to embodiments may be modeled as an inpainting task. For example, intra prediction may be formulated as a learning based inpainting task, wherein a latent variable is designed to control different generation modes. GAN based intra prediction provided according to embodiments of the invention may be implemented alone or in combination with one or more other video compression technique, such as a direction intra prediction technique. The intra prediction module of such a HEVC encoder/decoder may be redesigned to also apply GAN based inpainting in intra prediction, wherein Rate-Distortion Optimization (RDO) may be performed to select the best intra prediction mode between the intra prediction approaches.

Abstract: Systems and methods which provide Convolutional Neural Network (CNN) based synthesized view quality enhancement for video coding are described. Embodiments may comprise an encoder configured for CNN based synthesized view quality enhancement configured to provide improved coding efficiency while maintaining synthesized view quality. Additionally or alternatively, embodiments may comprise a virtual viewpoint generator configured for CNN based synthesized view quality enhancement configured provide post-processing of the synthesized view at the decoder side to reduce the artifacts. CNN based synthesized view quality enhancement may, for example, be provided for 3D video coding to improve its coding efficiency, which can be utilized in 3D scenarios, such as 3DTV.

Abstract: Systems and methods which provide Convolutional Neural Network (CNN) based synthesized view quality enhancement for video coding are described. Embodiments may comprise an encoder configured for CNN based synthesized view quality enhancement configured to provide improved coding efficiency while maintaining synthesized view quality. Additionally or alternatively, embodiments may comprise a virtual viewpoint generator configured for CNN based synthesized view quality enhancement configured provide post-processing of the synthesized view at the decoder side to reduce the artifacts. CNN based synthesized view quality enhancement may, for example, be provided for 3D video coding to improve its coding efficiency, which can be utilized in 3D scenarios, such as 3DTV.

Abstract: A method for natural language processing includes receiving, by one or more processors, an unstructured text input. An entity classifier is used to identify entities in the unstructured text input. The identifying the entities includes generating, using a plurality of sub-classifiers of a hierarchical neural network classifier of the entity classifier, a plurality of lower-level entity identifications associated with the unstructured text input. The identifying the entities further includes generating, using a combiner of the hierarchical neural network classifier, a plurality of higher-level entity identifications associated with the unstructured text input based on the plurality of lower-level entity identifications. Identified entities are provided based on the plurality of higher-level entity identifications.