Abstract: Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement multi-plane image (MPI) compression are disclosed. Example apparatus disclosed herein include an interface to access an input multiplane image stack corresponding to a source camera viewpoint, the input multiplane image stack including a plurality of texture images and a corresponding plurality of alpha images, ones of the alpha images including pixel values representative of transparency of corresponding pixels in respective ones of the texture images. Disclosed example apparatus also include a compressed image encoder to at least one of (i) convert the plurality of texture images to a single composite texture image to generate a compressed multiplane image stack, or (ii) convert the plurality of alpha images to a single composite alpha image to generate the compressed multiplane image stack. In some disclosed examples, the interface is to output the compressed multiplane image stack.

Abstract: Content relevance based table query answering is described. In one or more examples, a query and a table are received. The table includes a plurality of cells. A plurality of scores for calculated that correspond to the plurality of cells based on the query. One or more machine-learning models are then leveraged to generate a search result from the query, table, and scores, which is presented in a user interface for display.

Abstract: Techniques for caching data in a database system include one or more non-transitory computer-readable media storing program instructions that, when executed, perform the method of receiving a request to access a database, determining a caching priority specified in the request, processing the request to generate query results, and after generating the query results, caching or not caching data from which the query results are generated based on the determined caching priority.

Abstract: Content relevance based table query answering is described. In one or more examples, a query and a table are received. The table includes a plurality of cells. A plurality of scores for calculated that correspond to the plurality of cells based on the query. One or more machine-learning models are then leveraged to generate a search result from the query, table, and scores, which is presented in a user interface for display.

Abstract: Content relevance based table query answering is described. In one or more examples, a query and a table are received. The table includes a plurality of cells. A plurality of scores for calculated that correspond to the plurality of cells based on the query. One or more machine-learning models are then leveraged to generate a search result from the query, table, and scores, which is presented in a user interface for display.

Abstract: Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement multi-plane image (MPI) compression are disclosed. Example apparatus disclosed herein include an interface to access an input multiplane image stack corresponding to a source camera viewpoint, the input multiplane image stack including a plurality of texture images and a corresponding plurality of alpha images, ones of the alpha images including pixel values representative of transparency of corresponding pixels in respective ones of the texture images. Disclosed example apparatus also include a compressed image encoder to at least one of (i) convert the plurality of texture images to a single composite texture image to generate a compressed multiplane image stack, or (ii) convert the plurality of alpha images to a single composite alpha image to generate the compressed multiplane image stack. In some disclosed examples, the interface is to output the compressed multiplane image stack.

Abstract: Techniques for using writeable partitions with read-only replicated records include one or more non-transitory computer-readable media storing program instructions that, when executed by one or more processors of an authorization server, cause the one or more processors to perform a method including receiving a replica of a portion of a database, storing the replica of the portion of the database in a read-only partition, adding a field to the portion of the database in a writeable partition, and writing data to the added field.

Abstract: Methods and systems are provided for facilitating generation and utilization of a commonsense contextualizing machine learning (ML) model, in accordance with embodiments described herein. In embodiments, a commonsense contextual ML model is trained by fine-tuning a pre-trained language model using a set of training path-sentence pairs. Each training path-sentence pair includes a commonsense path, identified via a commonsense knowledge graph, and a natural language sentence identified as contextually related to the commonsense path. The trained commonsense contextualizing ML model can then be used to generate a commonsense inference path for a text input. Such a commonsense inference path can include a sequence of entities and relations that provide commonsense context to the text input. Thereafter, the commonsense inference path can be provided to a natural language processing system for use in performing a natural language processing task.

Abstract: System and methods for generating, validating, and augmenting question-answer pairs using generative AI are provided. An online interaction server accesses a set of digital content available at a set of designated network locations. The online interaction server further trains a pre-trained large language model (LLM) using the set of digital content to obtain a customized LLM. The online interaction server generates a set of question-answer pairs based on the set of digital content using the customized LLM and validates the set of question-answer pairs by determining if an answer in a question-answer pair is derived from the set of digital content. The online interaction server also selects a digital asset to augment an answer in a validated question-answer pair based on a semantic similarity between the validated question-answer pair and the digital asset.

Abstract: Described herein are techniques to enhance the user experience for 3D rendered applications via neural frame generation using upsampled optical flow data. In one embodiment, a neural network is trained using both sparse optical flow data and dense optical flow data to enable neural frame generation to be performed by a deployed neural network using only sparse optical flow data. The sparse optical flow data can be upsampled to dense optical flow data by the trained neural network. The neural network can then use the upsampled dense optical flow data to perform frame generation.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed of adaptive configurations of video encoder preset modes. An example apparatus comprising interface circuitry to obtain a video to be encoded, instructions, and at least one processor circuit to be programmed by the instructions to configure a video encoder to encode a first frame of the video based on a first preset mode of a plurality of preset modes associated respectively with a plurality of different relative encoder performance targets, select a second preset mode of the plurality of preset modes based on one or more characteristics associated with a second frame of the video, the second preset mode different from the first preset mode, and configure the video encoder to encode the second frame based on the second preset mode.

Abstract: The technology described herein receives a natural-language sequence of words comprising multiple entities. The technology then identifies a plurality of entities in the natural-language sequence. The technology generates a masked natural-language sequence by masking a first entity in the natural-language sequence. The technology retrieves, from a knowledge base, information related to a second entity in the plurality of entities. The technology then trains a natural-language model to respond to a query. The training uses a first representation of the masked natural-language sequence, a second representation of the information, and the first entity.

Abstract: Some techniques described herein relate to generating a hierarchical topic model (HTM), which can be used to generate custom content. In one example, a method includes determining first-level topics in a topic hierarchy related to a corpus of documents. A first-level topic of the first-level topics includes multiple words. The multiple words are grouped into clusters based on word embeddings of the multiple words. The multiple words are then subdivided into second-level topics as subtopics of the first-level topic, such that the number of second-level topics equals the number of clusters. A document of the corpus of documents is assigned to the first-level topic and to a second-level topic of the second-level topics, and an indication is received of access by a user to the document. Custom content is generated for the user based on one or more other documents assigned to the first-level topic and the second-level topic.

Abstract: The technology described herein receives a natural-language sequence of words comprising multiple entities. The technology then identifies a plurality of entities in the natural-language sequence. The technology generates a masked natural-language sequence by masking a first entity in the natural-language sequence. The technology retrieves, from a knowledge base, information related to a second entity in the plurality of entities. The technology then trains a natural-language model to respond to a query. The training uses a first representation of the masked natural-language sequence, a second representation of the information, and the first entity.

Abstract: Some techniques described herein relate to generating a hierarchical topic model (HTM), which can be used to generate custom content. In one example, a method includes determining first-level topics in a topic hierarchy related to a corpus of documents. A first-level topic of the first-level topics includes multiple words. The multiple words are grouped into clusters based on word embeddings of the multiple words. The multiple words are then subdivided into second-level topics as subtopics of the first-level topic, such that the number of second-level topics equals the number of clusters. A document of the corpus of documents is assigned to the first-level topic and to a second-level topic of the second-level topics, and an indication is received of access by a user to the document. Custom content is generated for the user based on one or more other documents assigned to the first-level topic and the second-level topic.

Abstract: Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement multi-plane image (MPI) compression are disclosed. Example apparatus disclosed herein include an interface to access an input multiplane image stack corresponding to a source camera viewpoint, the input multiplane image stack including a plurality of texture images and a corresponding plurality of alpha images, ones of the alpha images including pixel values representative of transparency of corresponding pixels in respective ones of the texture images. Disclosed example apparatus also include a compressed image encoder to at least one of (i) convert the plurality of texture images to a single composite texture image to generate a compressed multiplane image stack, or (ii) convert the plurality of alpha images to a single composite alpha image to generate the compressed multiplane image stack. In some disclosed examples, the interface is to output the compressed multiplane image stack.

Abstract: An embodiment establishes a designated attribute value as a semantic criterion for grouping records in a bucket, identifies a first set of records having attribute values that satisfy the semantic criterion, and adds the first set of records to the bucket. The embodiment detects that the first set of records represent a first series of events that occurred in succession at respective times. The embodiment derives a temporal attribute value representative of a time pattern formed by the times of the first series of events and designates the temporal attribute value as a temporal criterion for grouping records in the bucket. The embodiment identifies a second set of records that represent a second series of events and satisfy the temporal criterion and adds the second set of records to the bucket based at least in part on the second set of records satisfying the temporal criterion.

Abstract: An embodiment of an image processor for immersive video includes technology to re-order patches from a plurality of views based on one or more of relative position and orientation related information for a desired synthesized view, select a set of views to be used in each view synthesis pass, perform two or more view synthesis passes for the synthesized view to provide two or more intermediate view synthesis results, and mask and merge the two or more intermediate view synthesis results to provide a final view synthesis result. Other embodiments are disclosed and claimed.

Abstract: Methods and systems are provided for facilitating generation and utilization of a commonsense contextualizing machine learning (ML) model, in accordance with embodiments described herein. In embodiments, a commonsense contextual ML model is trained by fine-tuning a pre-trained language model using a set of training path-sentence pairs. Each training path-sentence pair includes a commonsense path, identified via a commonsense knowledge graph, and a natural language sentence identified as contextually related to the commonsense path. The trained commonsense contextualizing ML model can then be used to generate a commonsense inference path for a text input. Such a commonsense inference path can include a sequence of entities and relations that provide commonsense context to the text input. Thereafter, the commonsense inference path can be provided to a natural language processing system for use in performing a natural language processing task.

Abstract: An embodiment establishes a designated attribute value as a semantic criterion for grouping records in a bucket, identifies a first set of records having attribute values that satisfy the semantic criterion, and adds the first set of records to the bucket. The embodiment detects that the first set of records represent a first series of events that occurred in succession at respective times. The embodiment derives a temporal attribute value representative of a time pattern formed by the times of the first series of events and designates the temporal attribute value as a temporal criterion for grouping records in the bucket. The embodiment identifies a second set of records that represent a second series of events and satisfy the temporal criterion and adds the second set of records to the bucket based at least in part on the second set of records satisfying the temporal criterion.