Abstract: A database system is operable to generate query output based on executing a plurality of operators of a query operator execution flow for a query. Executing each operator can include generating a set of output query rows based on processing a set of input query rows, maintaining execution tracking data for the each operator that includes at least one counter tracking at least one event type, and generating a set of execution tracking rows based on sampling the execution tracking data. The query output includes a final set of rows corresponding to a query resultant and a final set of execution tracking rows generated by at least one final operator based on an aggregation of a plurality of sets of execution tracking rows generated by the plurality of operators.

Abstract: A node of a database system is operable to determine, at a first time, to prepare for a shutdown. The shutdown of the node is performed at a second time that is a period of time after the first time based on the node delaying the shutdown until a plurality of currently running processes being run by the node that initiated prior to the first time are determined to be complete. During the period of time after the first time and prior to performing the shutdown, a set of new processing requests are rejected by the node.

Abstract: A database system is operable to determine a query for execution. The database system can execute a left join operation for the query based on receiving a plurality of left input rows, and forwarding all of the plurality of left input rows are forwarded. It is determined whether any right input rows match with each given left input row based on a hash map. When one right input row is identified as matching with the given left input row, a value of right input row is copied and emitted in conjunction with the given left input row. When no right input rows are identified as matching with the given left input row, a null value is emitted in conjunction with the given left input row.

Abstract: Color images of food a user consumes, text information associated with the food and audio information associated with the food may be received. Color images are converted into hyperspectral images. A machine learning model classifies the hyperspectral images into features comprising at least taste, nutrient content and chemical composition. A database of food consumption pattern associated with the user is created based on classification features associated with the hyperspectral images, the text information and the audio information. A color image of local food may be received and converted into a hyperspectral image. The machine learning model is run with the hyperspectral image as input, and outputs classification features associated with the local food. Based on whether the classification features associated with the local food satisfies the food consumption pattern associated with the user, the local food may be recommended.

Abstract: A computer-implemented method for determining routes includes receiving a user's location and health measures, for a plurality of users, analyzing the plurality of users and their corresponding locations to determine health behaviors at a given location and time, receiving a request from a user for a route, developing a route to the user's destination from a current location to obtain a target health behavior within a given threshold or constraint based on the plurality of user's healthy behaviors, and presenting the a route on a computing device.

Abstract: Providing an activity for a participant may include receiving at least location data specifying a location of the participant. An engagement level of the participant may be predicted based on the location data. Sensor data associated with the participant may be received, the sensor data comprising at least current physiological data associated with the participant. Based at least on the predicted engagement level and the sensor data, an exercise for the participant to perform may be determined. A notification signal may be transmitted to the participant to perform the exercise.

Abstract: A method for neuromodulation includes monitoring brain activity of a patient using one or more electrodes attached to the patient, and using a first machine learning model to predict whether a patient will have a seizure based on the monitored brain activity of the patient. The method also includes, responsive to the first machine learning model predicting that the patient will have a seizure, using a second machine learning model to determine a neuromodulation signal pattern for preventing the predicted seizure. The method further includes using a neurostimulator to apply the determined neuromodulation signal pattern to the patient. The method also includes, after applying the determined neuromodulation signal pattern to the patient, detecting whether the patient had the predicted seizure based on the monitored brain activity of the patient. The method further includes adjusting at least the second machine learning model based on whether the patient had the predicted seizure.

Abstract: Color images of food a user consumes, text information associated with the food and audio information associated with the food may be received. Color images are converted into hyperspectral images. A machine learning model classifies the hyperspectral images into features comprising at least taste, nutrient content and chemical composition. A database of food consumption pattern associated with the user is created based on classification features associated with the hyperspectral images, the text information and the audio information. A color image of local food may be received and converted into a hyperspectral image. The machine learning model is run with the hyperspectral image as input, and outputs classification features associated with the local food. Based on whether the classification features associated with the local food satisfies the food consumption pattern associated with the user, the local food may be recommended.

Abstract: A method for neuromodulation includes monitoring brain activity of a patient using one or more electrodes attached to the patient, and using a first machine learning model to predict whether a patient will have a seizure based on the monitored brain activity of the patient. The method also includes, responsive to the first machine learning model predicting that the patient will have a seizure, using a second machine learning model to determine a neuromodulation signal pattern for preventing the predicted seizure. The method further includes using a neurostimulator to apply the determined neuromodulation signal pattern to the patient. The method also includes, after applying the determined neuromodulation signal pattern to the patient, detecting whether the patient had the predicted seizure based on the monitored brain activity of the patient. The method further includes adjusting at least the second machine learning model based on whether the patient had the predicted seizure.

Abstract: A computer-implemented method for determining routes includes receiving a user's location and health measures, for a plurality of users, analyzing the plurality of users and their corresponding locations to determine health behaviors at a given location and time, receiving a request from a user for a route, developing a route to the user's destination from a current location to obtain a target health behavior within a given threshold or constraint based on the plurality of user's healthy behaviors, and presenting the a route on a computing device.

Abstract: A system to monitor visual health includes a processor operatively coupled to memory. The system further includes one or more subsystems configured to record data related to eye-health. The processor is configured to analyze the recorded data, detect a likelihood of eyestrain based on the analysis, and generate an alert to perform at least one action in response to the detection and based on the analysis.

Abstract: Providing heath activity for a participant in a conference call may include receiving data associated with the conference call and location data specifying a location of the participant conducting the conference call. An engagement level of the participant that is to participate in the conference call may be predicted based on the received data and the location data. Sensor data associated with the participant may be received, the sensor data comprising at least current physiological data associated with the participant. The participant's fitness goal may be identified. Based on the predicted engagement level, the sensor data and the participant's fitness goal, an exercise for the participant to perform during the conference call may be determined. A notification signal may be transmitted to the participant to perform the exercise.

Abstract: A system to monitor visual health includes a processor operatively coupled to memory. The system further includes one or more subsystems configured to record data related to eye-health. The processor is configured to analyze the recorded data, detect a likelihood of eyestrain based on the analysis, and generate an alert to perform at least one action in response to the detection and based on the analysis.