Abstract: A diagnostic system to aid in diagnosing conditions underneath a subject's skin that predict intravascular device failure is provided. The diagnostic system includes an ultrasound unit that uses ultrasonic energy to obtain images underneath the subject's skin surrounding the insertion site of an intravascular device. The ultrasound unit is in electronic communication with a computing device that collects and stores data generated by the ultrasound unit. The computing device utilizes machine learning or artificial intelligence techniques to identify conditions underneath the subject's skin that predict intravascular device failure, and through a user interface, indicates to the user that subcutaneous conditions predictive of intravascular device failure are present.

Abstract: A diagnostic system to aid in diagnosing conditions underneath a subject's skin that predict intravascular device failure is provided. The diagnostic system includes an ultrasound unit that uses ultrasonic energy to obtain images underneath the subject's skin surrounding the insertion site of an intravascular device. The ultrasound unit is in electronic communication with a computing device that collects and stores data generated by the ultrasound unit. The computing device utilizes machine learning or artificial intelligence techniques to identify conditions underneath the subject's skin that predict intravascular device failure, and through a user interface, indicates to the user that subcutaneous conditions predictive of intravascular device failure are present.

Abstract: A diagnostic system to aid in diagnosing conditions underneath a subject's skin that predict intravascular device failure is provided. The diagnostic system includes an ultrasound unit that uses ultrasonic energy to obtain images underneath the subject's skin surrounding the insertion site of an intravascular device. The ultrasound unit is in electronic communication with a computing device that collects and stores data generated by the ultrasound unit. The computing device utilizes machine learning or artificial intelligence techniques to identify conditions underneath the subject's skin that predict intravascular device failure, and through a user interface, indicates to the user that subcutaneous conditions predictive of intravascular device failure are present.

Abstract: In one embodiment, a method includes identifying a plurality of dyslexic users on an online social network. The plurality of dyslexic users may be identified based on content objects posted by these users over a particular time period, where the content objects may include one or more of word-level errors or sentence-level errors. A machine-learning model may be trained for text correction using a corpus of social network data, which may include at least the content objects with one or more of word-level errors or sentence-level errors, and a corresponding set of corrected content objects. A text string including one or more errors may be received from a client system associated with a first user. The text string may be transformed into a vector representation using an encoder of the machine-learning model. A corrected text string may be generated from the vector representation using a decoder of the machine-learning model.

Abstract: In one embodiment, a method includes identifying a plurality of dyslexic users on an online social network. The plurality of dyslexic users may be identified based on content objects posted by these users over a particular time period, where the content objects may include one or more of word-level errors or sentence-level errors. A machine-learning model may be trained for text correction using a corpus of social network data, which may include at least the content objects with one or more of word-level errors or sentence-level errors, and a corresponding set of corrected content objects. A text string including one or more errors may be received from a client system associated with a first user. The text string may be transformed into a vector representation using an encoder of the machine-learning model. A corrected text string may be generated from the vector representation using a decoder of the machine-learning model.

Abstract: Health status signals generated by components of a power supply system, including health status signals generated by a control processor, are logically combined together using discrete electronic components and which provide a single, highly-reliable enable/disable signal to a master switch. The master switch connects and disconnects the power supply to and from electrical loads.

Abstract: Health status signals generated by components of a power supply system, including health status signals generated by a control processor, are logically combined together using discrete electronic components and which provide a single, highly-reliable enable/disable signal to a master switch. The master switch connects and disconnects the power supply to and from electrical loads.

Abstract: Some embodiments include a method of defining a concept taxonomy. The concept taxonomy can be a mechanism to identify user activities that is relevant to a content analysis study. For example, the method can include identify one or more explicit concept identifiers to include in a concept taxonomy on a user interface. The method can include generating a relevant concepts network by identifying one or more potential concept candidates in past user activities within a time window. The relevant concepts network can include the potential concept candidates and the explicit concept identifiers as nodes. A concept taxonomy system can then select at least a subset of the potential concept candidates to present on the user interface as concept recommendations to supplement the concept taxonomy by identifying commonalities between the nodes of the relevant concepts network.

Abstract: Some embodiments include a method of performing a content analysis study around a central theme utilizing a concept study system. The concept study system can generate a classifier machine corresponding to the content analysis study based on a super topic taxonomy including one or more concept identifiers. The concept study system can process a content object, associated with a user activity in a social networking system, through the classifier machine to determine whether to assign the user activity to the content analysis study. The concept study system can aggregate at least an attribute derived from the user activity in a study-specific data container associated with the content analysis study and compute a statistical or analytical insight based on aggregated attributes in the study-specific data container.