Abstract: A thermal management system is described. The thermal management system includes a heat sink structure and an array of microelectromechanical system (MEMS) jets. The heat sink structure is in thermal communication with a plurality of heat sources. The heat sink structure includes fins and a collection channel. The array of MEMS jets is arranged to cause a fluid to impinge on a surface of each of the fins and to be directed through the collection channel.

Abstract: Described herein are systems and methods for identifying security vulnerabilities. The systems and methods herein can utilize security vulnerability information to identify potential security threats and can utilize this information to generate an attack using a machine learning model, such as a large language model. Generated attacks can be carried out to assess impact of a security vulnerability. An output can be provided that represents the assessed impact. In some implementations, the systems and methods herein generate patches or other mitigations for security vulnerabilities, which can be tested and deployed to address security vulnerabilities.

Abstract: Described herein are systems and methods for verifying the integrity of data, such as data used for training machine learning models. Some implementations are directed to verifying the provenance of datasets, the contents of datasets, or both. In some implementations, multiple filters are selected for verifying the contents of datasets. Filters can be selected based on rules, random selection, or using a machine learning model in some implementations. In some implementations, data cleaning is provided.

Abstract: An active cooling system is described. The active cooling system includes at least one cooling element that has a vent therein and is in communication with a fluid. The cooling element(s) are actuated to vibrate to drive the fluid toward a heat-generating structure and to alternately open and close at least one virtual valve corresponding to the vent. The virtual valve is open for a low flow resistance and closed for a high flow resistance. The vent remains physically open for the virtual valve being closed.

Abstract: The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

Abstract: Described herein are systems and methods for verifying the integrity of data, such as data used for training machine learning models. Some implementations are directed to verifying the provenance of datasets, the contents of datasets, or both. In some implementations, multiple filters are selected for verifying the contents of datasets. Filters can be selected based on rules, random selection, or using a machine learning model in some implementations. In some implementations, data cleaning is provided.

Abstract: Systems and methods for measuring, grading, evaluating, and comparing AI models via a graphical user interface are disclosed. The technology obtains a set of application domains of the AI model in which an AI model will be used. The application domains are mapped to one or more guidelines to determine a set of guidelines that define operational boundaries of the AI model. The guidelines are used to generate assessment domains, each associated with specific benchmarks that include indicators of a degree of satisfaction with the guidelines. For each assessment domain, assessments are constructed to evaluate the AI model's degree of satisfaction with the corresponding guidelines. The AI model is then evaluated against the assessments. Based on these comparisons, grades are assigned to the AI model for each assessment domain. The application-domain-specific grades are generated and displayed at a GUI, reflecting the AI model's degree of satisfaction with the guidelines.

Abstract: The disclosed subject matter relates generally to inductor structures integrated in semiconductor devices. More particularly, the present disclosure relates to a semiconductor device having a semiconductor chip, a redistribution layer on the semiconductor chip, and an inductor structure having an upper section in the redistribution layer and a lower section in the semiconductor chip. The upper section and the lower section are concentric about a center region of the inductor structure. The lower section is connected to the upper section.

Abstract: Described herein are systems and methods for identifying security vulnerabilities. The systems and methods herein can utilize security vulnerability information to identify potential security threats and can utilize this information to generate an attack using a machine learning model, such as a large language model. Generated attacks can be carried out to assess impact of a security vulnerability. An output can be provided that represents the assessed impact. In some implementations, the systems and methods herein generate patches or other mitigations for security vulnerabilities, which can be tested and deployed to address security vulnerabilities.

Abstract: Structures including an inductor and methods of forming such structures. The structure comprises a semiconductor substrate including a first plurality of sealed cavities and a back-end-of-line stack on the semiconductor substrate. Each sealed cavity includes an air gap, and the back-end-of-line stack includes an inductor having a winding that overlaps with the scaled cavities.

Abstract: A cooling system is described. The cooling system includes a cooling element having a central region and a perimeter. The cooling element is anchored at the central region. At least a portion of the perimeter is unpinned. The cooling element is in communication with a fluid. The cooling element is actuated to induce vibrational motion to drive the fluid toward a heat-generating structure.

Abstract: An automated process for multi-feed video systems comprises receiving a plurality of video feeds from a plurality of video-capture devices. The video feeds each have a quality parameter set to an initial value. A priority feed is selected from the video feeds. A quality parameter of the priority feed is set to a priority value in response to selecting the priority feed. The video-capture devices are instructed to format the video feeds based on the quality parameter of each video feed. The quality parameter for each video feed comprises a bitrate or a resolution for the video feed. The priority feed is streamed as a video output. The video feeds are displayed for selection in an interface, and the interface comprises a map including a location of the video-capture devices.

Abstract: Systems and methods for measuring, grading, evaluating, and comparing AI models via a graphical user interface are disclosed. The technology obtains a set of application domains of the AI model in which an AI model will be used. The application domains are mapped to one or more guidelines to determine a set of guidelines that define operational boundaries of the AI model. The guidelines are used to generate assessment domains, each associated with specific benchmarks that include indicators of a degree of satisfaction with the guidelines. For each assessment domain, assessments are constructed to evaluate the AI model's degree of satisfaction with the corresponding guidelines. The AI model is then evaluated against the assessments. Based on these comparisons, grades are assigned to the AI model for each assessment domain. The application-domain-specific grades are generated and displayed at a GUI, reflecting the AI model's degree of satisfaction with the guidelines.

Abstract: Systems and methods for providing user-induced variable identification of end-to-end computing system security impact information via a user interface are disclosed. The system receives at a graphical user interface (GUI), a user calibration of a graphical security vulnerability element. The system then determines a set of computing system components that interact with data associated with the network operation based on a transmission of the network operation associated with a computing system. The system then determines a set of security vulnerabilities associated with each computing system component of the set of computing system components using a third-party resource. The system then applies a decision engine on the set of security vulnerabilities to determine a set of impacted computing-aspects associated with the set of computing system components.

Abstract: A system for providing notifications to client devices that includes one or more processors and one or more storage devices. The storage devices may store instructions that, when executed, configure the processors to perform operations. The operations include receiving a transaction notification from a third party; identifying a user account associated with the transaction notification; generating a push notification with a payload describing instructions to display a message and an interactive icon, and a resource identifier associated with the interactive icon, where the resource identifier includes a message ID encoding an interactive session and an action ID encoding a requested action. The operations also include transmitting the push notification to a client device associated with the user account; receiving a first indication indicating a user interaction with the interactive icon, the first indication having the resource identifier and updating the user account based on the action request.

Abstract: A device is configured to detect a communication error between a first network device and a second network device. The device is further configured to identify a first node in a computer network map corresponding with the first network device and to identify node properties for the first node. Error correction instructions in the node properties for the first node include an address for rerouting data traffic to a third network device. The device is further configured to apply the error correction instructions where applying the error correction instructions suspends data traffic to the second network device and reroutes data traffic to the third network device.

Abstract: Systems, methods, and computer program products are provided for monitoring network processing using node analysis. The method includes receiving node operation information relating to a node command from one or more nodes. The one or more nodes are grouped into a cluster. The method also includes determining one or more node characteristics based on the node operation information. The method further includes comparing the node characteristic(s) of the node command to expected node characteristic(s). The method still further includes determining a node outage likelihood. The node outage likelihood indicates the likelihood the given node will experience a node outage. The method also includes determining a cluster node operation plan. The cluster node operation plan is configured to determine the nodes of the cluster that must be in operation in an event of the node outage of the given node.

Abstract: A machine learning computing system identifies a vulnerability associated with a server. Based on information associated with the server and a knowledge base, the computing system schedules an interval for patching the server in a centralized tracking module. Based on the knowledge base and the vulnerability, the computing system creates, validates, and deploys the patch job. During patch job execution, the computing system monitors the status of the patch job at the server and transmits status updates to a user interface module. After expiration of the interval, the computing system generates an assessment report for the executed patch job. The computing system updates the knowledge base based on the assessment report to improve future decisioning processes. Based on the success or failure of the patch job, the computing system, upon a failure indication, automatically reschedules an interval for patching the server.

Abstract: Systems and methods for determining and displaying comparative platform-specific security vulnerabilities with respect to cloud-based computing platforms are disclosed. To compare platform-specific security vulnerabilities of cloud-based computing platforms, the system detects a user interaction at a webpage for a network operation. The system then determines a first set of computing aspects associated with a set of cloud-based computing platforms using response data received from a processing of the network operation. The system then identifies a second set of computing aspects associated with a comparative cloud-based computing system platform and determines an overall-computing aspect impact level for associated computing aspects of the second set of computing aspects.

Abstract: A system for providing notifications to client devices that includes one or more processors and one or more storage devices. The storage devices may store instructions that, when executed, configure the processors to perform operations. The operations include receiving a transaction notification from a third party; identifying a user account associated with the transaction notification; generating a push notification with a payload describing instructions to display a message and an interactive icon, and a resource identifier associated with the interactive icon, where the resource identifier includes a message ID encoding an interactive session and an action ID encoding a requested action. The operations also include transmitting the push notification to a client device associated with the user account; receiving a first indication indicating a user interaction with the interactive icon, the first indication having the resource identifier and updating the user account based on the action request.