Abstract: Techniques for providing remote guidance to a vehicle operating in an environment, by an operator located in the environment, are described herein. The operator can include a safety observer configured to observe one or more vehicles operating in the environment and may identify a scenario that requires a modification to a vehicle operation (e.g., stop forward movement, change direction of travel, modify a maximum speed, etc.). The operator may access a graphical user interface (GUI) via an operator computing device, and may input a constraint to modify the vehicle operation. The vehicle computing system may receive a control signal including the constraint, and may modify a vehicle trajectory based on the constraint. The vehicle computing system may later determine that a condition associated with the constraint is satisfied, and may continue vehicular operation in absence of the constraint.

Abstract: Systems, methods and apparatuses are described herein that employ machine learning techniques to assess a likelihood or risk that one or more patients will experience an adverse outcome, such as a decline in renal function, within one or more timeframes. The embodiments may utilize patient data relating to demographics, vital signs, diagnoses, procedures, diagnostic tests, biomarker assays, genetic tests, behaviors, and/or patient symptoms, to determine risk information, such as important predictive features and patient risk scores. And the embodiments may automatically execute patient workflows, such as providing treatment recommendations to providers and/or patients, based on determined risk scores.

Abstract: Systems, methods and apparatuses are described herein that employ machine learning techniques to assess a likelihood or risk that one or more patients will experience an adverse outcome, such as a decline in renal function, within one or more timeframes. The embodiments may utilize patient data relating to demographics, vital signs, diagnoses, procedures, diagnostic tests, biomarker assays, genetic tests, behaviors, and/or patient symptoms, to determine risk information, such as important predictive features and patient risk scores. And the embodiments may automatically execute patient workflows, such as providing treatment recommendations to providers and/or patients, based on determined risk scores.

Abstract: Systems, methods and apparatuses are described herein that employ machine learning techniques to assess a likelihood or risk that one or more patients will experience an adverse outcome, such as a decline in renal function, within one or more timeframes. The embodiments may utilize patient data relating to demographics, vital signs, diagnoses, procedures, diagnostic tests, biomarker assays, genetic tests, behaviors, and/or patient symptoms, to determine risk information, such as important predictive features and patient risk scores. And the embodiments may automatically execute patient workflows, such as providing treatment recommendations to providers and/or patients, based on determined risk scores.

Abstract: Apparatus for connecting and disconnecting a computer or other device to the Internet includes switching means which has an input and output. The switching means is operative in a first mode to connect the input to the output and in a second mode to disconnect the input from the output. The network output from the computer is coupled to the input of the switching means with the output of the switching means coupled to the network. There are means coupled to the switching means to operate the switching means in either said first or second mode. The switching means utilizes first and second switches each of which are momentary switches. The first switch is associated with a one-shot which triggers the switching means in the first mode while the second switch is associated with a flip-flop which operates the switching means in the second mode.