Abstract: A method for public key infrastructure for serviceable electronic components is provided. The method includes identifying a plurality of electronic components in electronic communication with each other and establishing that each of the plurality of electronic components is a trusted device according to a certificate history including a plurality of signed public keys stored upon the respective electronic component. The method further includes utilizing Diffie-Hellman key exchange to distribute symmetric message authentication codes to each of the plurality of electronic components and communicating between the plurality of electronic components with the symmetric message authentication codes to achieve tangible functions that may be provided by the plurality of electronic components.

Abstract: A method for attestation of electronic components includes operating a system including the components. The components are configured for electronic communication with each other. A first electronic component provides plug-in hardware and/or configurable software. The method includes utilizing a second electronic component to perform attestation upon the first component using asymmetric key cryptography to verify authenticity. When using the asymmetric key cryptography successfully verifies the authenticity, the method includes selectively enabling one of power or data to the first component and communicating between the first component and the plurality of components to achieve tangible functions. When the cryptography fails to successfully verify the authenticity, the method includes selectively withholding one of power or data connection the first component.

Abstract: A system for interfacing with a meta-database representing data from a plurality of source databases. A key variable repository module operably couples the databases and includes an AI program with a scanner algorithm and a profiler algorithm. The scanner algorithm receives the source data from a source interface, compresses the data, and synchronizes the data with the meta-data using a meta-database interface. The profiler algorithm receives the meta-data from the meta-database interface, generates granular data types for the meta-data, determines variables indicative of the meta-data, generates variable probability distributions, produces variable associations, and modifies the meta-database to include the probability distributions and associations using the meta-data interface.

Abstract: A system for training a model from a subset of data representing decentrally stored source databases. A key variable repository module operably couples the databases and includes an AI program with a scanner algorithm and a profiler algorithm. The scanner algorithm receives the training data from a source interface, compresses the training data, and synchronizes the training data with the meta-data using a meta-database interface. The profiler algorithm receives the meta-data from the meta-database interface, generates granular data types for the meta-data, determines training variables indicative of the meta-data, generates variable probability distributions, produces training variable associations, and modifies the meta-database to include the probability distributions and associations using the meta-data interface. The key interface allows for searching the meta-database for training variables, variable probability distributions, and/or variable associations.

Abstract: A system for maintaining a meta-database including meta-data representing decentralized data from source databases, which cause inefficient selection of modeling data and/or variables. Each of source and meta-data interfaces communicate with the respective database(s). A key variable repository module operably couples the databases and includes an AI program with a scanner algorithm and a profiler algorithm. The scanner algorithm receives the source data from the source interface, compresses the data, and synchronizes the data with the meta-data using the meta-database interface. The profiler algorithm receives the meta-data from the meta-database interface, generates granular data types for the meta-data, determines variables indicative of the meta-data, generates variable probability distributions, produces variable associations, and modifies the meta-database to include the probability distributions and associations using the meta-data interface.

Abstract: The present disclosure relates to a method of preventing or treating a cancer via administering an EHMT2 inhibitor or a combination comprising an EHMT2 inhibitor compound and one or more additional therapeutic agent disclosed herein or a pharmaceutical composition thereof to subjects in need thereof. The present disclosure also relates to the use of such compounds or combinations for research or other non-therapeutic purposes.

Abstract: A system for interfacing with a meta-database representing data from a plurality of source databases. A key variable repository module operably couples the databases and includes an AI program with a scanner algorithm and a profiler algorithm. The scanner algorithm receives the source data from a source interface, compresses the data, and synchronizes the data with the meta-data using a meta-database interface. The profiler algorithm receives the meta-data from the meta-database interface, generates granular data types for the meta-data, determines variables indicative of the meta-data, generates variable probability distributions, produces variable associations, and modifies the meta-database to include the probability distributions and associations using the meta-data interface.

Abstract: Described herein are guide sleeves and kits for guide sleeves having a plurality of modules. Each module may have an axial extending main body having first and second axially spaced apart opposed ends and an outer sidewall. The modules may also have an axially extending internal opening extending between the first and second axially spaced apart opposed ends for receiving a flexible linear member. Internal rollers may be associated with the internal opening so that as the flexible linear member travels axially through the internal opening, the internal rollers rotate relative to the flexible linear member. When the modules are connected, a first module may be axially moveable with respect to a second module and the guide sleeve may be bendable.

Abstract: The present disclosure provides compounds, pharmaceutically acceptable compositions thereof, and methods of using the same.

Abstract: Disclosed are systems and methods that automatically classify, filter, and reduce large volumes of feedback data as a function of time using artificial intelligence technology. The aggregated feedback data is reduced by representing the feedback data as sets of descriptors corresponding to one or more time periods that are displayed on a graphical user interface. Feedback data packets are parsed by labeling the feedback data packets with a time period identifier. The feedback data packets are processed utilizing neural network technology to classify the feedback data according to one or more subject identifiers that are each associated with a subject vector. A descriptor analysis is used to process the subject vectors and the feedback data packets to generate descriptor sets comprising one or more descriptors as well as weighting data for each descriptor.

Abstract: A system and method for training an artificial intelligence engine for real-time monitoring to eliminate false positives is disclosed. The system includes at least one processor, a communication interface coupled to the processor, and a memory device storing executable code. Executing the executable code causes the processor to receive data from an AI security model, receive data from a false positive database, and correlate both sets of data. The correlated data is used to generate a training dataset and a test dataset used to train a false positive identification model. After evaluating the false positive identification model, an AI engine is applied to user registration. The AI engine includes an AI security model and the false positive identification model. Additionally, a system for evaluating the security of user registration utilizing the false positive identification model is disclosed.

Abstract: Disclosed are systems and methods that automatically classify, filter, and reduce large volumes of feedback data as a function of time using artificial intelligence technology. The aggregated feedback data is reduced by representing the feedback data as sets of descriptors corresponding to one or more time periods that are displayed on a graphical user interface. Feedback data packets are parsed by labeling the feedback data packets with a time period identifier. The feedback data packets are processed utilizing neural network technology to classify the feedback data according to one or more subject identifiers that are each associated with a subject vector. A descriptor analysis is used to process the subject vectors and the feedback data packets to generate descriptor sets comprising one or more descriptors as well as weighting data for each descriptor.

Abstract: Disclosed are systems and methods that automatically classify, filter, and reduce large volumes of feedback data as a function of time using artificial intelligence technology. The aggregated feedback data is reduced by representing the feedback data as sets of descriptors corresponding to one or more time periods that are displayed on a graphical user interface. Feedback data packets are parsed by labeling the feedback data packets with a time period identifier. The feedback data packets are processed utilizing neural network technology to classify the feedback data according to one or more subject identifiers that are each associated with a subject vector. A descriptor analysis is used to process the subject vectors and the feedback data packets to generate descriptor sets comprising one or more descriptors as well as weighting data for each descriptor.

Abstract: Disclosed are systems and methods that automatically classify, segment, and parse content data using artificial intelligence and natural language processing technology, and generate graphical user interfaces that allow end users to dynamically filter content data for display. The systems processes volumes of content data to identify interrogative data, content sources that generated the interrogative data, and subject identifiers relating to the content data. The system generates graphical user interfaces that allow end users to effectively filter the data by choosing between layouts that display one or more of the various categories of data, including the interrogative data, content source identifiers, and/or subject identifiers.

Abstract: A system and method is described for labelling data for trigger identification. The system and method comprising receiving data, transforming the data, extracting content from the data, processing data content through a classification machine learning model to receive a label, and trigger a secondary system based on the label. The system and method may further include maintaining a database of labeled data.

Abstract: Disclosed are systems and methods that automatically classify, segment, and parse content data using artificial intelligence and natural language processing technology, and generate graphical user interfaces that allow end users to dynamically filter content data for display. The systems processes volumes of content data to identify interrogative data, content sources that generated the interrogative data, and subject identifiers relating to the content data. The system generates graphical user interfaces that allow end users to effectively filter the data by choosing between layouts that display one or more of the various categories of data, including the interrogative data, content source identifiers, and/or subject identifiers.

Abstract: Disclosed are systems and methods that automatically classify, segment, and parse content data using artificial intelligence and natural language processing technology, and generate graphical user interfaces that allow end users to dynamically filter content data for display. The systems processes volumes of content data to identify interrogative data, content sources that generated the interrogative data, and subject identifiers relating to the content data. The system generates graphical user interfaces that allow end users to effectively filter the data by choosing between layouts that display one or more of the various categories of data, including the interrogative data, content source identifiers, and/or subject identifiers.

Abstract: A system and method is described for training a machine learning model to label data for trigger identification. The system and method comprising receiving data from a database, extracting content from the data, transforming the data, clustering the data, labeling the data, creating a training dataset and a test data set, and training a classification machine learning model to label data accordingly to identify a trigger.

Abstract: Various arrangements for performing health monitoring of cellular network functions in a cloud-computing environment are presented. A cloud computing region may be input for analysis. A 5G cellular network core may be implemented on a cloud computing platform. Input of a cellular core function for analysis may also be received. Instantiations of the cellular core function and pods of the cellular core function executed within the cloud computing region can be identified. A customized test query can be performed based on the input of the cellular core function. A status of the cellular core function can be output based on the customized test query.

Abstract: Systems and methods for performing a call trace with a cloud-implemented cellular network core are provided. In one example, a method includes: receiving input of a cloud computing region of a cloud computing platform for call trace, identifying a plurality of instantiations of a pod within a cellular network core function of a cellular network core, the cellular network core function being executed on the cloud-computing platform, and executing a call trace for the plurality of instantiations of the pod within the cellular network core function. The cellular network core of a cellular network is implemented across multiple cloud computing regions, and the cellular network core is implemented on the cloud computing platform that includes a plurality of cloud computing regions that includes the cloud computing region.