Abstract: Increasing transparency of one or more micro-displays. A method includes attaching a transparent cover to at least a portion of a semiconductor wafer. The at least a portion of the semiconductor wafer includes the one or more micro-displays. The one or more micro-displays include one or more active silicon areas. The method further includes, after the transparent cover has been attached to the at least a portion of the semiconductor wafer, removing silicon between one or more of the active silicon areas, causing the one or more micro-displays to have a transparency of at least 50%.

Abstract: Machine-learning (ML) techniques and models are described for predicting the number and severity of network attacks within a specified timeframe, such as the next fifteen minutes. In some embodiments, the techniques including training a ML model based on features extracted from a training dataset and applying the trained ML model to estimate (a) the probability of an attack happening on an account within a specified timeframe; (b) how many attacks are predicted to occur in the specified timeframe (if any); and/or (c) the severity of the attacks predicted to occur. A system may deploy preventative measures based on the ML model output to counter or mitigate the effects of predicted and coordinated network attacks.

Abstract: Machine-learning techniques and models are described for alerting users to attacks on accounts in real-time or near real-time. In some embodiments, an attack detection model uses Natural Language Processing (NLP) and multi-level classification techniques to monitor login attempts and detect attacks. The model may use NLP to convert text associated with account activity to numerical vectors, where the vectors include scores and/or other numerical values computed based on the meaning of the converted text. The model may further include a set of classifiers trained to learn patterns in the numerical vectors that are predictive of a network attack. The model may assign labels to events based on the predicted likelihood that the event is an attack. The system may deploy real-time preventative or corrective measures based on the ML model output to counter or mitigate the effects of an attack.

Abstract: Increasing transparency of one or more micro-displays. A method includes attaching a transparent cover to at least a portion of a semiconductor wafer. The at least a portion of the semiconductor wafer includes the one or more micro-displays. The one or more micro-displays include one or more active silicon areas. The method further includes, after the transparent cover has been attached to the at least a portion of the semiconductor wafer, removing silicon between one or more of the active silicon areas, causing the one or more micro-displays to have a transparency of at least 50%.

Abstract: Increasing transparency of one or more micro-displays. A method includes attaching a transparent cover to at least a portion of a semiconductor wafer. The at least a portion of the semiconductor wafer includes the one or more micro-displays. The one or more micro-displays include one or more active silicon areas. The method further includes, after the transparent cover has been attached to the at least a portion of the semiconductor wafer, removing silicon between one or more of the active silicon areas.

Abstract: Increasing transparency of one or more micro-displays. A method includes attaching a transparent cover to at least a portion of a semiconductor wafer. The at least a portion of the semiconductor wafer includes the one or more micro-displays. The one or more micro-displays include one or more active silicon areas. The method further includes, after the transparent cover has been attached to the at least a portion of the semiconductor wafer, removing silicon between one or more of the active silicon areas.

Abstract: A method of preparing a catalytic structure the method including the steps of: providing a solution of a precursor compound in a solvent at ambient conditions; providing a suspension of a support material having a specific surface area of at least 1 m2/g in a solvent at ambient conditions; mixing the solution of the precursor compound and the suspension of the support material; providing a reactive solvent in a supercritical or subcritical state; admixing the mixture of the solution of the precursor compound and the suspension of the support material in the supercritical or subcritical reactive solvent to form a reaction solution; injecting the reaction solution into a reactor tube via an inlet; allowing a reaction of the precursor compound in the supercritical or subcritical reactive solvent in the reactor tube to form the catalyst nanoparticles on the support material to provide the catalytic structure.

Abstract: A method of preparing a catalytic structure the method including the steps of: providing a solution of a precursor compound in a solvent at ambient conditions; providing a suspension of a support material having a specific surface area of at least 1 m2/g in a solvent at ambient conditions; mixing the solution of the precursor compound and the suspension of the support material; providing a reactive solvent in a supercritical or subcritical state; admixing the mixture of the solution of the precursor compound and the suspension of the support material in the supercritical or subcritical reactive solvent to form a reaction solution; injecting the reaction solution into a reactor tube via an inlet; allowing a reaction of the precursor compound in the supercritical or subcritical reactive solvent in the reactor tube to form the catalyst nanoparticles on the support material to provide the catalytic structure.

Abstract: A system for monitoring urinary system operation comprises a sensor for detecting a level of one or more compounds in tissues of a subject's bladder. A processor identifies a pattern in a time variation of the level detected by the sensor. The processor generates an indicator of bladder function based at least in part on the identified pattern. In some embodiments the processor generates a possible diagnosis based upon the identified pattern and one or more pieces of additional information. The additional information may comprise one or more of: a residual amount of urine after urination; a maximum uroflow rate; and, a delay in onset of urination.