Abstract: Systems and methods for predicting changes in risk based on interventions are presented herein. In an example computer-implemented method, a computing device may receive, from a first source, first information and receive, from a second source, second information. The computing device may generate patient information by linking the first information and the second information using a linkage, corresponding to a patient. The computing device may generate using one or more trained machine learning models, a risk prediction for the patient and a change in risk prediction for the patient corresponding to an intervention. The computing device may output the risk prediction and the change in risk prediction for the patient.

Abstract: Systems and methods for operationalizing predicted changes in risk based on interventions are presented herein. In an example computer-implemented method, a computing device may store information about patients as analyzed text. The computing device can receive, from a machine learning model, intervention information for a patient. The computing device may generate a prioritized intervention list according to the intervention information and provide remote access to the prioritized intervention list. The computing device may receive updated patient engagement data as freeform text. The computing device can convert the freeform text of the updated patient engagement data into analyzed text. The computing device may receive, from the machine learning model, updated intervention information, wherein the machine learning model is re-trained based on the updated information. The computing device can generate an updated prioritized intervention list according to the intervention information.

Abstract: An architecture for dynamically repairing a semiconductor memory, such as a Dynamic Random Access Memory (DRAM), includes circuitry for dynamically storing memory element remapping information. Memory is tested for errors by writing, then reading a plurality of memory blocks, such as rows or columns, in parallel. Memory is dynamically reprogrammed in order to remap unused spare memory elements for failed memory elements when errors are detected. Unused spare memory elements are remapped utilizing a circuit that overrides unblown fuses or antifuses.

Abstract: An inflatable device serving in one or more roles as furniture and/or equipment for exercise, recreation, and/or therapeutic equipment, or in some combination of these functions is disclosed. The device consists of an inflatable base (2) with one or more cavities or holes, with rounded inflatable spheroid(s) (1) in said cavities or holes. The device may optionally include a system of straps (3 and 4) or a form-fitting covering around the base, which may serve to correct an asymmetry in said base. The device may include a weighted base (11). The device may also include means for generating lights, sound, and/or video (6, 7, 8, 9, 10, 12, and/or 13) responsive to the actions of users on or around the device.

Abstract: An architecture for dynamically repairing a semiconductor memory, such as a Dynamic Random Access Memory (DRAM), includes circuitry for dynamically storing memory element remapping information. Memory is tested for errors by writing, then reading a plurality of memory blocks, such as rows or columns, in parallel. Memory is dynamically reprogrammed in order to remap unused spare memory elements for failed memory elements when errors are detected. Unused spare memory elements are remapped utilizing a circuit that overrides unblown fuses or antifuses.

Abstract: An architecture for dynamically repairing a semiconductor memory, such as a Dynamic Random Access Memory (DRAM), includes circuitry for dynamically storing memory element remapping information. Memory is tested for errors by writing, then reading a plurality of memory blocks, such as rows or columns, in parallel. Memory is dynamically reprogrammed in order to remap unused spare memory elements for failed memory elements when errors are detected. Unused spare memory elements are remapped utilizing a circuit that overrides unblown fuses or antifuses.

Abstract: A system and method of providing media recommendations and media segments based on expert choice lists is disclosed. Expert choice lists consisting of media segment references are retrieved through a data network and stored cumulatively in a database as records with text descriptor fields. Users of the suggestion system make requests in the form of text search descriptors and a desired output descriptor type. Descriptors of the output type in the expert choice list database are scored by the frequency with which they appear in expert choice lists possessing matches to the search descriptors. A list of the top-scoring descriptors is returned. In an alternate preferred embodiment, media segment references are scored by the frequency of their appearance in lists with matches to the search descriptors. The highest-scoring segment references are used to generate a playlist so that the recommended media segments can be presented to the user automatically.