Abstract: Systems and methods for frequency management, including: an online media service configured to: receive a request for a media item, the request including a recipient identifier; identify a set of candidate media items ranked by a set of matching criteria; a frequency management service configured to: perform a query against a lookup service, where the query includes (i) an entity identifier of at least one candidate media item of the set of candidate media items, and (ii) the recipient identifier; receive a response from the lookup service including a quantity of impressions associated with the entity identifier and the recipient identifier; identify a predefined frequency threshold; determine that the frequency threshold is exceeded and exclude the at least one candidate media item from a result set based on the determination; and provide the result set including an identifier of at least one other candidate media item.

Abstract: The embodiments of the present disclosure provide a power management system of a mobile X-ray machine and a control method thereof. The power management system comprises a power module group; a main control module being connected with a upper machine, and configured to receive an action signal sent by the upper machine, acquire status information of the power module group, and output a control signal; a functional component power pack being connected with the power module group and the main control module, and configured to convert electrical energy of the power module group according to the control signal and output converted energy to a functional component of a high-voltage generator.

Abstract: A energy storage system includes a power supply device, a main battery and a backup battery, and a power supply method of the energy storage system includes: when the power supply device works normally, a load is powered and the backup battery is floatingly charged by the power supply device, and the main battery is in standby mode; when the power supply device stops supplying power, the load will be powered by the backup battery, and meanwhile, the main battery will be triggered to switch from the standby mode to a backup mode to start supplying power to the load; when an output power of the main battery is equal to a power of the load, an output power of the backup battery is zero and the backup battery is in a bypass state.

Abstract: A energy storage system includes a power supply device, a main battery and a backup battery, and a power supply method of the energy storage system includes: when the power supply device works normally, a load is powered and the backup battery is floatingly charged by the power supply device, and the main battery is in standby mode; when the power supply device stops supplying power, the load will be powered by the backup battery, and meanwhile, the main battery will be triggered to switch from the standby mode to a backup mode to start supplying power to the load; when an output power of the main battery is equal to a power of the load, an output power of the backup battery is zero and the backup battery is in a bypass state.

Abstract: Transient computing clusters can be temporarily provisioned in cloud-based infrastructure to run data processing tasks. Such tasks may be run by services operating in the clusters that consume and produce data including operational metadata. Techniques are introduced for tracking data lineage across multiple clusters, including transient computing clusters, based on the operational metadata. In some embodiments, operational metadata is extracted from the transient computing clusters and aggregated at a metadata system for analysis. Based on the analysis of the metadata, operations can be summarized at a cluster level even if the transient computing cluster no longer exists. Further relationships between workflows, such as dependencies or redundancies, can be identified and utilized to optimize the provisioning of computing clusters and tasks performed by the computing clusters.

Abstract: Techniques are disclosed for inferring design-time information based on run-time artifacts generated by services operating in a distributed computing cluster. In an embodiment, a metadata system extracts metadata including run-time artifacts generated by services in a distributed computing cluster while processing a workflow including multiple jobs. The extracted metadata is processed to identify entities and entity relationships which can then be used to generate lineage information. Using the lineage information, the metadata system can infer design-time information associated with the workflow. The inferred design-time information can then be utilized to, for example, recreate the workflow, recreate previous versions of the workflow, optimize the workflow, etc.

Abstract: Systems and methods for programmatic generation of training data, including: a training data generation engine configured to: identify an image asset corresponding to an entity; identify a training video; select a consecutive subset of frames of the training video based on a procedure for ranking frames on their candidacy for overlaying content; for at least one frame of the subset of frames: perform an augmentation technique on the identified logo image to generate an augmented image asset; overlay at least one variation of the image asset, including the augmented image asset, onto each of the subset of frames to generate a set of overlayed frames; and generate an augmented version of the training video including the overlayed frames; and a model training engine configured to: train an artificial intelligence model for entity detection using the augmented version of the training video.

Abstract: Systems and methods for frequency management, including: an online media service configured to: receive a request for a media item, the request including a recipient identifier; identify a set of candidate media items ranked by a set of matching criteria; a frequency management service configured to: perform a query against a lookup service, where the query includes (i) an entity identifier of at least one candidate media item of the set of candidate media items, and (ii) the recipient identifier; receive a response from the lookup service including a quantity of impressions associated with the entity identifier and the recipient identifier; identify a predefined frequency threshold; determine that the frequency threshold is exceeded and exclude the at least one candidate media item from a result set based on the determination; and provide the result set including an identifier of at least one other candidate media item.

Abstract: Systems and methods for entity detection using artificial intelligence, including: a deep learning model service configured to: select and analyze a set of frames from a media item to determine a set of candidate brand-probability pairs; a voting engine configured to: determining that a first brand-probability pair of a set of candidate brand-probability pairs based on at least one obtained hyperparameter value does not meet a threshold for determining whether candidate brand-probability pairs are to be included in a result set; excluding the first brand-probability pair from the result set based on the determination; sorting the result set; and selecting at least one final brand-probability pair from the result set; and an offline transcoding service configured to: store the final brand-probability pair in a repository with a relation to an identifier of the media item.

Abstract: System and methods for intuitive search operation results using machine learning including: identifying a first candidate content item matching a content item search request; identifying a first content item model corresponding to the first candidate content item including word vector collaborative filtering representations of the first candidate content item; identifying a set of content item models where each: is associated with at least one corresponding available content item, and includes word vector collaborative filtering representations; applying deep neural learning to compare the first content item model with the set of content item models to generate a subset of the content item models most relevant to the first content item model; generating a result set of available content items corresponding to the subset of the content item models most relevant to the first content item model; and providing the result set of available content items.

Abstract: System and methods for cold-starting content on a platform using machine learning including: identifying content metadata and contextual data both corresponding to a target content item; generating a target content item model by applying deep neural learning that: applies a word vector embedding operation to the content metadata to generate a collaborative filtering representation of the content metadata, applies a word vector embedding operation to the contextual data to generate a collaborative filtering representation of the contextual data, and bridges the collaborative filtering representations of the content metadata and the contextual data to generate the target content item model; applying deep neural learning to compare the target content item model with a set of existing content item models; determining cold-start characteristics of the target content item based on the comparison; and providing the cold-start characteristics for distribution management of the target content item.

Abstract: The embodiments of the present disclosure provide a power management system of a mobile X-ray machine and a control method thereof. The power management system comprises a power module group; a main control module being connected with a upper machine, and configured to receive an action signal sent by the upper machine, acquire status information of the power module group, and output a control signal; a functional component power pack being connected with the power module group and the main control module, and configured to convert electrical energy of the power module group according to the control signal and output converted energy to a functional component of a high-voltage generator.

Abstract: Transient computing clusters can be temporarily provisioned in cloud-based infrastructure to run data processing tasks. Such tasks may be run by services operating in the clusters that consume and produce data including operational metadata. Techniques are introduced for tracking data lineage across multiple clusters, including transient computing clusters, based on the operational metadata. In some embodiments, operational metadata is extracted from the transient computing clusters and aggregated at a metadata system for analysis. Based on the analysis of the metadata, operations can be summarized at a cluster level even if the transient computing cluster no longer exists. Further relationships between workflows, such as dependencies or redundancies, can be identified and utilized to optimize the provisioning of computing clusters and tasks performed by the computing clusters.

Abstract: An adaptive control method for a mobile X-ray machine includes: acquiring operating information of a battery, determining a state of the battery; if the battery is in a failure state, selecting a first control value; if the battery is not in the failure state, determining whether a residual charge of the battery is less than a low charge threshold; if the residual charge of battery is less than low charge threshold, determining the battery is in a low charge state, and selecting a second control value; and if residual charge of battery is not less than low charge threshold, determining the battery is in a normal state, and selecting a third control value; acquiring circuit parameter information of a capacitor charger, and controlling capacitor charger based on circuit parameter information and the first control value or the second control value or the third control value.

Abstract: Transient computing clusters can be temporarily provisioned in cloud-based infrastructure to run data processing tasks. Such tasks may be run by services operating in the clusters that consume and produce data including operational metadata. Techniques are introduced for tracking data lineage across multiple clusters, including transient computing clusters, based on the operational metadata. In some embodiments, operational metadata is extracted from the transient computing clusters and aggregated at a metadata system for analysis. Based on the analysis of the metadata, operations can be summarized at a cluster level even if the transient computing cluster no longer exists. Further relationships between workflows, such as dependencies or redundancies, can be identified and utilized to optimize the provisioning of computing clusters and tasks performed by the computing clusters.

Abstract: Techniques are disclosed for inferring design-time information based on run-time artifacts generated by services operating in a distributed computing cluster. In an embodiment, a metadata system extracts metadata including run-time artifacts generated by services in a distributed computing cluster while processing a workflow including multiple jobs. The extracted metadata is processed to identify entities and entity relationships which can then be used to generate lineage information. Using the lineage information, the metadata system can infer design-time information associated with the workflow. The inferred design-time information can then be utilized to, for example, recreate the workflow, recreate previous versions of the workflow, optimize the workflow, etc.

Abstract: Techniques are disclosed for inferring design-time information based on run-time artifacts generated by services operating in a distributed computing cluster. In an embodiment, a metadata system extracts metadata including run-time artifacts generated by services in a distributed computing cluster while processing a workflow including multiple jobs. The extracted metadata is processed to identify entities and entity relationships which can then be used to generate lineage information. Using the lineage information, the metadata system can infer design-time information associated with the workflow. The inferred design-time information can then be utilized to, for example, recreate the workflow, recreate previous versions of the workflow, optimize the workflow, etc.

Abstract: Transient computing clusters can be temporarily provisioned in cloud-based infrastructure to run data processing tasks. Such tasks may be run by services operating in the clusters that consume and produce data including operational metadata. Techniques are introduced for tracking data lineage across multiple clusters, including transient computing clusters, based on the operational metadata. In some embodiments, operational metadata is extracted from the transient computing clusters and aggregated at a metadata system for analysis. Based on the analysis of the metadata, operations can be summarized at a cluster level even if the transient computing cluster no longer exists. Further relationships between workflows, such as dependencies or redundancies, can be identified and utilized to optimize the provisioning of computing clusters and tasks performed by the computing clusters.

Abstract: Transient computing clusters can be temporarily provisioned in cloud-based infrastructure to run data processing tasks. Such tasks may be run by services operating in the clusters that consume and produce data including operational metadata. Techniques are introduced for tracking data lineage across multiple clusters, including transient computing clusters, based on the operational metadata. In some embodiments, operational metadata is extracted from the transient computing clusters and aggregated at a metadata system for analysis. Based on the analysis of the metadata, operations can be summarized at a cluster level even if the transient computing cluster no longer exists. Further relationships between workflows, such as dependencies or redundancies, can be identified and utilized to optimize the provisioning of computing clusters and tasks performed by the computing clusters.

Abstract: Techniques are disclosed for inferring design-time information based on run-time artifacts generated by services operating in a distributed computing cluster. In an embodiment, a metadata system extracts metadata including run-time artifacts generated by services in a distributed computing cluster while processing a workflow including multiple jobs. The extracted metadata is processed to identify entities and entity relationships which can then be used to generate lineage information. Using the lineage information, the metadata system can infer design-time information associated with the workflow. The inferred design-time information can then be utilized to, for example, recreate the workflow, recreate previous versions of the workflow, optimize the workflow, etc.