Abstract: Computer-implemented systems and methods including language models for explaining and resolving code errors. A computer-implemented method may include: receiving or accessing a log comprising an error message, the error message indicating an error in code; determining the error message from the log; determining a context associated with the error; generating a prompt for a large language model (“LLM”), the prompt comprising at least: the error message, and the context associated with the error; transmitting the prompt to the LLM; and receiving an output from the LLM in response to the prompt, the output comprising at least: an explanation of the error message, and a suggested fix for the error.

Abstract: In various examples, multi-subject multi-camera tracking for high-density environments is provided. In some embodiments, an MTMC tracking system may associate previously initialized behavior states with currently generated behaviors clusters based on selecting between subject trajectory tracking and cluster matching algorithms. The system may receive image data comprising feeds from a plurality of optical images sensors. The system may generate behavior clusters and apply trajectory tracking to determine if the prior behavior states can be propagated based on a continuity of trajectory analysis. If behavior clusters cannot be associated based on trajectory tracking, a matching algorithm may be applied to the set of behavior clusters. For matched clusters, a matched a prior behavior state may be assigned to the cluster and propagated forward. If a cluster does not match with a prior behavior state, a new global ID and behavior state may be initialized based on representations forming the cluster.

Abstract: At least some embodiments of the present disclosure are related to methods and systems for evaluating, generating, and/or prototyping data pipelines. In certain embodiments, a system is configured to perform operations include: receiving an input dataset, the input dataset including a data schema; generating a first prompt based on the input dataset and a first prompt structure having one or more text strings and one or more blanks; providing the first prompt to a language model; receiving a use case generated by the language model for the input dataset, the use case including a description of how to use the input dataset; generating a data pipeline based on the use case; and applying the data pipeline to the input dataset to generate an output dataset.

Abstract: Methods, systems, and devices for data management are described. A first application in a destination computing environment of a data management system (DMS) may determine that a bulk-push criterion is satisfied for a second application in a source computing environment of the DMS. The first application may transmit, to an asynchronous metadata service, a request indicating the second application for which the bulk-push criterion is satisfied. The request may be configured to cause the asynchronous metadata service to query a database in the source computing environment, identify a latest version of one or more rows that include metadata associated with the second application, and generate data records indicating the latest version of the one or more rows that include the metadata associated with the second application. The first application may receive the data records via an asynchronous data stream between the first application and the second application.

Abstract: Methods, systems, and devices for data management are described. A first application in a destination computing environment of a data management system (DMS) may determine that a bulk-push criterion is satisfied for a second application in a source computing environment of the DMS. The first application may transmit, to an asynchronous metadata service, a request indicating the second application for which the bulk-push criterion is satisfied. The request may be configured to cause the asynchronous metadata service to query a database in the source computing environment, identify a latest version of one or more rows that include metadata associated with the second application, and generate data records indicating the latest version of the one or more rows that include the metadata associated with the second application. The first application may receive the data records via an asynchronous data stream between the first application and the second application.

Abstract: Methods, systems, and devices for data management are described. A first application in a destination computing environment of a data management system (DMS) may determine that a bulk-push criterion is satisfied for a second application in a source computing environment of the DMS. The first application may transmit, to an asynchronous metadata service, a request indicating the second application for which the bulk-push criterion is satisfied. The request may be configured to cause the asynchronous metadata service to query a database in the source computing environment, identify a latest version of one or more rows that include metadata associated with the second application, and generate data records indicating the latest version of the one or more rows that include the metadata associated with the second application. The first application may receive the data records via an asynchronous data stream between the first application and the second application.

Abstract: Methods, systems, and devices for data management are described. A data enrichment service supported by a data management system (DMS) may receive, from a first application in a destination computing environment of the DMS, a set of enrichment definitions for metadata synchronization between the first application and a second application in a source computing environment of the DMS. A change data capture (CDC) service supported by the DMS may generate a set of data records that correspond to metadata changes associated with the second application. The data enrichment service may transform the set of data records by using data enrichment components to modify the set of data records according to the set of enrichment definitions provided by the first application. The data enrichment components may be dynamically partitioned into groups that execute in parallel. The second application may push the enriched data records to the first application in real-time.

Abstract: Methods, systems, and devices for data management are described. A destination data storage environment of a data management system may transmit, to a source data storage environment configured to run one or more applications, a request to synchronize metadata for the one or more applications from the source data storage environment to the destination data storage environment. In some examples, the request may include configuration information indicating one or more filtering parameters for filtering a data stream to identify a subset of a set of data records and start and stop times for pushing data to the destination data storage environment. The destination data storage environment may receive, from the source data storage environment, the subset of the set of data records based on the configuration information, where the subset of the set of data records are determined from a filtering operation at the source data storage environment.

Abstract: Object scriptability includes receiving a high-level language script describing at least one data-analysis object, including a node representing the data-analysis object in a graph-based data structure including a plurality of nodes, where each node from the plurality of nodes represents a respective data-analysis object in a data analysis system, where each node from the plurality of nodes is connected to at least one other node from the plurality of nodes by an edge, and where the edge represents a relationship between the respective objects in the data analysis system.

Abstract: A charging system for an electric vehicle includes: a rechargeable energy storage device adapted to store electrical energy and to supply the stored electrical energy to at least one electric motor of the vehicle to propel the vehicle; a charger adapted to supply electrical energy to the energy storage device according to a selectable power rating of at least two power ratings to charge and/or recharge the energy storage device based on a selected one of power ratings; and a controller adapted to select one of the power ratings of the charger to set the charger to charge and/or recharge the energy storage device based on the selected one of the power ratings.

Abstract: Automatic data modeling in includes identifying an analytical object in response to first data expressing usage intent, generating an analytical model generation data query for the analytical object, obtaining a trained analytical model generated in accordance with the analytical model generation query and trained using results data obtained in accordance with the analytical object, generating a resolved request representing second data expressing usage intent and indicating a request for results data obtained using the trained analytical model, generating an analytical model results data query for obtaining the results data in accordance with the trained analytical model and the analytical object, and outputting data for presenting a visualization of the results data obtained by executing the analytical model results data query, wherein a first portion of the results data corresponds with the analytical object and a second portion of the results data corresponds with the trained analytical model.

Abstract: In some examples, methods and systems to automatically create runtime environments are provided. For example, a method includes: receiving a request to create a runtime environment; automatically generating a cluster of nodes based on the request, wherein the cluster of nodes are configured to run one or more containerized applications for the runtime environment; automatically applying a manifest onto the cluster of nodes, wherein the manifest includes one or more configurations associated with the runtime environment; and automatically deploying one or more software products into the cluster of nodes.

Abstract: Automatic data modeling in a low-latency data access and analysis system includes identifying an analytical-object in response to first data expressing usage intent, generating an analytical model generation data-query for the analytical-object, obtaining a trained analytical model generated in accordance with the analytical model generation query and trained using results data obtained in accordance with the analytical-object, generating a resolved-request representing second data expressing usage intent and indicating a request for results data obtained using the trained analytical model, generating an analytical model results data-query for obtaining the results data in accordance with the trained analytical model and the analytical-object, and outputting data for presenting a visualization of the results data obtained by executing the analytical model results data-query, wherein a first portion of the results data corresponds with the analytical-object and a second portion of the results data corresponds

Abstract: Automatic data modeling in a low-latency data access and analysis system includes identifying an analytical-object in response to first data expressing usage intent, generating an analytical model generation data-query for the analytical-object, obtaining a trained analytical model generated in accordance with the analytical model generation query and trained using results data obtained in accordance with the analytical-object, generating a resolved-request representing second data expressing usage intent and indicating a request for results data obtained using the trained analytical model, generating an analytical model results data-query for obtaining the results data in accordance with the trained analytical model and the analytical-object, and outputting data for presenting a visualization of the results data obtained by executing the analytical model results data-query, wherein a first portion of the results data corresponds with the analytical-object and a second portion of the results data corresponds

Abstract: A charging system for an electric vehicle includes: a rechargeable energy storage device adapted to store electrical energy and to supply the stored electrical energy to at least one electric motor of the vehicle to propel the vehicle; a charger adapted to supply electrical energy to the energy storage device according to a selectable power rating of at least two power ratings to charge and/or recharge the energy storage device based on a selected one of power ratings; and a controller adapted to select one of the power ratings of the charger to set the charger to charge and/or recharge the energy storage device based on the selected one of the power ratings.

Abstract: Object scriptability includes receiving a high-level language script describing at least one data-analysis object, including a node representing the data-analysis object in a graph-based data structure including a plurality of nodes, where each node from the plurality of nodes represents a respective data-analysis object in a data analysis system, where each node from the plurality of nodes is connected to at least one other node from the plurality of nodes by an edge, and where the edge represents a relationship between the respective objects in the data analysis system.

Abstract: Object scriptability methods and system are described herein. The method includes generating a graph-based data structure including a plurality of nodes, where each node from the plurality of nodes represents a respective data-analysis object in a data analysis system, where each node from the plurality of nodes is connected to at least one other node from the plurality of nodes by an edge, where the edge represents a relationship between the respective objects in the data analysis system, and where generating the graph-based data structure includes receiving a high-level language script describing at least one data-analysis object, and generating at least one node from the plurality of nodes in accordance with the high-level language script.

Abstract: Object scriptability methods and system are described herein. The method includes generating a graph-based data structure including a plurality of nodes, where each node from the plurality of nodes represents a respective data-analysis object in a data analysis system, where each node from the plurality of nodes is connected to at least one other node from the plurality of nodes by an edge, where the edge represents a relationship between the respective objects in the data analysis system, and where generating the graph-based data structure includes receiving a high-level language script describing at least one data-analysis object, and generating at least one node from the plurality of nodes in accordance with the high-level language script.

Abstract: An improved optical method for determining metal or a metal compound thereof in a sample with organic impurities. The optical method comprises of solvating the metal in a liquid medium and then mixing this liquid medium with an optical indicator and then measuring the presence of the metal. The improvement lies in removing any organic impurities by the addition of an additive, where the said additive is removed before the optical indicator is added.

Abstract: An improved optical method for determining metal or a metal compound thereof in a sample with organic impurities. The optical method comprises of solvating the metal in a liquid medium and then mixing this liquid medium with an optical indicator and then measuring the presence of the metal. The improvement lies in removing any organic impurities by the addition of an additive, where the said additive is removed before the optical indicator is added.