Abstract: A computer-implemented method includes receiving user input including codified knowledge management information and/or engine data; and processing the input using one or more trained machine learning models to generate one or more living documents. A computing system includes one or more processors; and a memory having stored thereon instructions that, when executed, cause the computing system to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents.

Abstract: The following relates generally to computer security, and more particularly relates to computer security in a virtual environment, such as a metaverse. In some embodiments, one or more processors: receive a set of known events (e.g., security threats) including event classifications; receive data of layers of the virtual environment; detect events in the data of the layers of the virtual environment; and determine correlations between the events in the data of the layers of the virtual environment. The correlations may be between events in different layers of the virtual environment. The one or more processors may also predict future events by analyzing the detected events.

Abstract: Machine learning methods and systems for developing security governance recommendations are disclosed.

Abstract: Machine learning methods and systems for developing security governance recommendations are disclosed.

Abstract: A computer-implemented method includes receiving user input including codified knowledge management information and/or engine data; and processing the input using one or more trained machine learning models to generate one or more living documents. A computing system includes one or more processors; and a memory having stored thereon instructions that, when executed, cause the computing system to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents.

Abstract: A method includes receiving a plurality of user use cases; analyzing the use cases using an AI engine to order the use cases; generating an optimized machine learning model; and causing an optimized deployment option to be displayed. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed. A non-transitory computer-readable storage medium stores executable instructions that, when executed by a processor, cause a computer to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed.

Abstract: Inter-device communication with a pulse-amplitude modulation (PAM) signal can have at least two data eyes with different Vref levels. A physical interface (PHY) can be trained for the PAM signal by training a first data eye separately from the second data eye. The training can include adjusting the first Vref level separately from the second Vref level to center each reference voltage on its respective data eye.

Abstract: A method includes receiving a computing system future state description in response to prompting a user; determining specific properties; predicting a solution architecture based on the specific properties; and generating infrastructure-as-code. A computing system includes a processor; and a memory having stored thereon instructions that, when executed, cause the computing system to: prompt a user to describe a future state of a computing system; receive a description of the future state; determine specific properties; predict a solution architecture based on the specific properties; and generate infrastructure-as-code.

Abstract: A method includes receiving a plurality of user use cases; analyzing the use cases using an AI engine to order the use cases; generating an optimized machine learning model; and causing an optimized deployment option to be displayed. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed. A non-transitory computer-readable storage medium stores executable instructions that, when executed by a processor, cause a computer to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed.

Abstract: A method includes receiving first input, analyzing the first input using a first model, receiving second input, analyzing the second input using a second model; and generating infrastructure-as-code. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause a computer to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code.

Abstract: ML methods and systems for cataloging and making recommendations based on domain-specific knowledge are disclosed. An example method includes: cataloging, using knowledge engines, data to develop knowledge repositories for respective domains; obtain current domain state data; obtain future domain state data; analyze, using first ML models, one or more of (i) data from the knowledge repositories, (ii) the first domain state data, and (iii) the second domain state data to identify a recommended set of one or more regulations, standards, policies and/or rules for a desired second domain state; analyze, using second ML models, (i) the recommended set and (ii) a current data and architecture state for a current computing environment to generate a summary of one or more cloud deployment options for migrating a current computing environment to a future computing environment for a future domain state; and cause the summary to be displayed on a computing device.

Abstract: A method includes receiving first input, analyzing the first input using a first model, receiving second input, analyzing the second input using a second model; and generating infrastructure-as-code. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause a computer to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code.

Abstract: A method includes collecting current data and architecture state, collecting future data and architecture state; analyzing the current and/or future data and architecture state to generate deployment options; and causing the summary of options to be displayed. A computing system includes a processor and a memory comprising instructions, that when executed, cause the system to collect current data and architecture state, collect future data and architecture state; analyze the current and/or future data and architecture state to generate deployment options; and cause the summary of options to be displayed. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to collect current data and architecture state, collect future data and architecture state; analyze the current and/or future data and architecture state to generate deployment options; and cause the summary of options to be displayed.

Abstract: A method includes receiving user inputs; receiving codified knowledge management information; receiving engine data; and processing the user inputs, the codified knowledge management information and engine data using a trained machine learning model to generate a living document. A computing system includes one or more processors; and a memory comprising instructions that, when executed, cause the computing system to: receive user inputs; receive codified knowledge management information; receive engine data; and process the user inputs, the codified knowledge management information and engine data using a trained machine learning model to generate a living document.

Abstract: A method includes receiving first input, analyzing the first input using a first model, receiving second input, analyzing the second input using a second model; and generating infrastructure-as-code. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause a computer to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code.

Abstract: A method comprises: using a plurality of gain stages cascaded in a ring topology to form a ring oscillator configured to output an oscillation signal; controlling a supply voltage of said ring oscillator using a low-speed DAC (digital-to-analog converter) in accordance with a coarse control word; providing a capacitive load at an inter-stage node of said ring oscillator using a varactor array controlled by a control voltage array; establishing said control voltage array using a high-speed DAC array in accordance with a fine control word; adjusting the coarse control word upon a start-up to make an oscillation frequency of said oscillation signal approximately equal to target value; and adjusting the fine control word in a closed loop manner in accordance with a detection of a timing error of said oscillation signal.

Abstract: A receiver includes a passive CTLE (continuous-time linear equalizer) configured to receive a first voltage signal from a first node and output a current signal to a second node in accordance with a first control signal; a CG (common-gate) amplifier configured to receive the current signal and output a second voltage signal at a third node in accordance with a second control signal; a first active inductor configured to provide an inductive load at the third node; a CS (common-source) CTLE configured to receive the second voltage signal and output a third voltage signal at a fourth node in accordance with a third control signal; a second active inductor configured to provide an inductive load at the fourth node; and a decision circuit configured to receive the third voltage signal and output a decision in accordance with a clock signal.

Abstract: A source device includes an adaptive link training circuity. The link training circuitry includes source capability information for link training of a link between the source device and a sink device. The source device includes a transmitter coupled to the adaptive link training circuitry to transmit the source capability information to the sink device. The adaptive link training circuitry is to initiate link training between the source device and the sink device, determine whether the link training between the source device and the sink device is unsuccessful, and in response to determining that the link training is unsuccessful, automatically adapt a setting of the link training based on the source capability information.

Abstract: Embodiments of the invention relate to bearing apparatuses including a bearing assembly having a continuous superhard bearing element including a continuous superhard bearing surface and a tilting pad bearing assembly. The disclosed bearing apparatuses may be employed in pumps, turbines or other mechanical systems. In an embodiment, the bearing apparatus includes a first and second bearing assembly. The first bearing assembly includes a first support ring and a plurality of tilting pads. Each tilting pad is tilted and/or tiltably secured relative to the first support ring. The second bearing assembly includes a continuous superhard bearing element. The continuous superhard bearing element includes a continuous superhard bearing surface facing the plurality of tilting pads and exhibits a maximum lateral width greater than about 2 inches.

Abstract: Embodiments disclosed herein relate to bearing apparatuses including a bearing assembly having a continuous superhard bearing element including a continuous superhard bearing surface and a tilting pad bearing assembly. The disclosed bearing apparatuses may be employed in pumps, turbines or other mechanical systems. In an embodiment, the bearing apparatus includes a first and second bearing assembly. The first bearing assembly includes a first support ring and a plurality of tilting pads. Each tilting pad is tilted and/or tiltably secured relative to the first support ring. The second bearing assembly includes a continuous superhard bearing element. The continuous superhard bearing element includes a continuous superhard bearing surface facing the plurality of tilting pads and exhibits a maximum lateral width greater than about 2 inches.