Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: In some embodiments, intent labeling may be facilitated for machine learning model training. In some embodiments, a set of natural language inputs may be obtained. Based on an action set, content related to intent labeling may be presented on a user interface. Based on an entity hierarchy and user selection of an agent-side action of the action set, a first entity subset of entities associated with the agent-side action may be presented on the user interface. Based on the entity hierarchy and user selection of a first entity from the first entity subset, a second entity subset of entities associated with the first entity may be presented on the user interface. Based on user selection of a second entity from the second entity subset, the natural language input may be associated with an ordered intent label that includes the agent-side action, the first entity, and the second entity.

Abstract: An acrylamide gel formulation for use in electrophoresis, and a system and method for electrophoretic gel casting. In some embodiments, the system comprises a UV-Vis curing light source, and an acrylamide formulation that allows a resolving gel and a stacking gel to be photopolymerized in a single step. In some embodiments, the resolving solution formulation comprises acrylamide and bis-acrylamide, Bis-Tris buffering agent, and lithium phenyl-2, 4, 6-trimethylbenzoyl-phosphinate (LAP) as a water-soluble photoinitiator, which allows the solution to be photopolymerized using UV radiation within a certain wavelength. In some embodiments, a dilution buffer formulation comprises acrylamide and bis-acrylamide, Bis-Tris buffering agent, and lithium phenyl-2, 4, 6-trimethyl-benzoyl-phosphinate (LAP). The dilution buffer may be added to the resolving solution in a selected amount in order to modify the concentration of the acrylamide to a desired concentration.

Abstract: Various embodiments of novel glenoid implant are disclosed. The glenoid implant includes: an articular body that includes an articulation surface and a bone-facing surface on opposite side of the articular body; a metal tray attached to the bone-facing surface; and one or more holes provided in the articular body extending from the articulation surface through the articular body and the metal tray to accommodate a screw therethrough. An implant system that includes the glenoid implant and a corresponding humeral head implant is also provided.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: In some implementations, a device may obtain a natural language input including an emoji. The device may identify one or more appearance modifiers associated with the emoji. The device may generate a token associated with the emoji that removes the one or more appearance modifiers, wherein the token is associated with multiple emojis including the emoji, and wherein the token is a modified code associated with the emoji or is associated with a cluster that is associated with the multiple emojis. The device may provide, to a natural language processing (NLP) model, the token associated with the emoji. The device may obtain, from the NLP model, an output that indicates an interpretation of the natural language input based on providing the token to the NLP model.

Abstract: Systems, apparatuses, and methods are described for inverting neural embeddings. One or more forward neural embeddings associated with meanings, features, and/or characteristics of data samples may be generated for one or more data samples. One or more inverse neural embeddings associated with the one or more forward neural embeddings may be determined. One or more inverse feature sets for the one or more inverse neural embeddings may be generated.

Abstract: A method includes obtaining a first dataset associated with a first set of classes and a second dataset associated with a second set of classes, where the first and second datasets share features. The method also includes creating an aggregated dataset having a quality score for the second set of classes that satisfy a first threshold. Creating the aggregated dataset includes determining a first and second set of clusters based on the first and second datasets. The method includes determining a distance based on a record of the first set of clusters and a second record of the second set of clusters in a feature space. The method further includes determining a set of analysis scores based on the distance and generating an indication for a set of records associated with the set of analysis scores based on the set of analysis scores satisfying a second threshold.

Abstract: Systems, apparatuses, and methods are described for inverting neural embeddings. One or more forward neural embeddings associated with meanings, features, and/or characteristics of data samples may be generated for one or more data samples. One or more inverse neural embeddings associated with the one or more forward neural embeddings may be determined. One or more inverse feature sets for the one or more inverse neural embeddings may be generated.