Abstract: Example implementations are directed to systems and methods for a context aware conversational agent for self-learning. In an example implementation, a method includes generating a journaling model based on activity data and engagement data associated with one or more tasks of a user. The journaling model uses machine-learning to identify a context pattern using the activity data, and maps performance associated with the one or more tasks based at least on the engagement data. The method adaptively provides a prompt to gather additional engagement data based on the context pattern in view of real-time activity data, where the prompt is generated based on the journaling model. The journaling model is updated based on the additional engagement data.

Abstract: Methods, systems, and apparatuses are described causing light to be emitted, causing a frequency at which the light is emitted to vary, receiving, based on the frequency variation, a user input, determining a critical flicker frequency (CFF) corresponding to the user input, and determining, based on the CFF, a disease state.

Abstract: Technology for generating and presenting distance cartograms, which use region partitioning for scalable construction of time space, is disclosed. A representative method includes obtaining a geospatial graph, receiving a selection of an origin location within the graph, and dividing the first graph into a plurality of regions based on a granularity level. Recursively for each region, the representative method includes determining a travel time variance within the region. If the variance exceeds a threshold, the region is further partitioned into new regions based on a new granularity level. The representative method further includes generating a distance cartogram with respect to the origin location based on a final composition of partitioned regions that may have resulted from different levels of partitioning.

Abstract: Example implementations are directed to systems and methods for a context aware conversational agent for self-learning. In an example implementation, a method includes generating a journaling model based on activity data and engagement data associated with one or more tasks of a user. The journaling model uses machine-learning to identify a context pattern using the activity data, and maps performance associated with the one or more tasks based at least on the engagement data. The method adaptively provides a prompt to gather additional engagement data based on the context pattern in view of real-time activity data, where the prompt is generated based on the journaling model. The journaling model is updated based on the additional engagement data.