Abstract: Systems and techniques are described for implementing autonomous control of powered earth-moving vehicles, including to automatically determine and control movement around a site having potential obstacles. For example, the systems/techniques may determine and implement autonomous operations of powered earth-moving vehicle(s) (e.g., obtain/integrate data from sensors of multiple types on a powered earth-moving vehicle, and use it to determine and control movement of the powered earth-moving vehicle around a site), including in some situations to implement coordinated actions of multiple powered earth-moving vehicles and/or of a powered earth-moving vehicle with one or more other types of construction vehicles.

Abstract: Systems and techniques are described for implementing autonomous control of powered earth-moving vehicles, including to automatically determine and control movement around a site having potential obstacles. For example, the systems/techniques may determine and implement autonomous operations of powered earth-moving vehicle(s) (e.g., obtain/integrate data from sensors of multiple types on a powered earth-moving vehicle, and use it to determine and control movement of the powered earth-moving vehicle around a site), including in some situations to implement coordinated actions of multiple powered earth-moving vehicles and/or of a powered earth-moving vehicle with one or more other types of construction vehicles.

Abstract: Systems and techniques are described for implementing autonomous control of earth-moving vehicles, including to automatically determine and control movement around a site having potential obstacles. For example, the systems/techniques may determine and implement autonomous operations of excavator vehicle(s) (e.g., obtain/integrate data from sensors of multiple types, and use it to determine and control movement of the excavator vehicle around a site), including in some situations to implement coordinated actions of multiple excavator vehicles and/or of an excavator vehicle with one or more other types of earth-moving vehicles. The described techniques may further include determining current location and positioning of the excavator vehicle on the site, determining a target destination location and/or route of the excavator vehicle, identifying and classifying obstacles (if any) along a desired route or otherwise between current and destination locations, and implementing actions to address any such obstacles.

Abstract: Systems and techniques are described for implementing autonomous control of earth-moving construction and/or mining vehicles, including to automatically determine and control autonomous movement (e.g., of a vehicle's hydraulic arm(s), tool attachment(s), tracks/wheels, rotatable chassis, etc.) to move materials or perform other actions based at least in part on data about an environment around the vehicle(s). A perception system on a vehicle that includes at least a LiDAR component may be used to repeatedly map a surrounding environment and determine a 3D point cloud with 3D data points reflecting the surrounding ground and nearby objects, with the LiDAR component mounted on a component part of the vehicle that is moved independently of the vehicle chassis to gather additional data about the environment. GPS data from receivers on the vehicle may further be used to calculate absolute locations of the 3D data points.

Abstract: Systems and techniques are described for implementing autonomous control of earth-moving construction and/or mining vehicles, including to automatically determine and control autonomous movement (e.g., of a vehicle's hydraulic arm(s), tool attachment(s), tracks/wheels, rotatable chassis, etc.) to move materials or perform other actions based at least in part on data about an environment around the vehicle(s). A perception system on a vehicle that includes at least a LiDAR component may be used to repeatedly map a surrounding environment and determine a 3D point cloud with 3D data points reflecting the surrounding ground and nearby objects, with the LiDAR component mounted on a component part of the vehicle that is moved independently of the vehicle chassis to gather additional data about the environment. GPS data from receivers on the vehicle may further be used to calculate absolute locations of the 3D data points.

Abstract: Predictive modeling using statistical evaluation combined with federated learning is described to enable incident map creation while preserving anonymity and achieving fine granularity. Machine-learned models (116) are trained to infer when persons associated with individual user devices (102) do or do not have a condition. The models (116) are deployed to the user devices (102) and return generalized location data and aggregated statistics about inferences made by the models. A remote system collects the aggregated statistics (120) and builds an incidence map (122-1, 122-2) identifying hotspots and coldspots for the condition. The incidence map identifies affected regions, with fine granularity down to a neighborhood or street level. The subregion-level information is regularly updated as new inferences, and new aggregated statistics, are made. Hence, the incidence map (122-1, 122-2) is current and highly detailed.

Abstract: The present disclosure provides systems and methods that leverage machine-learned models in conjunction with user-associated data and disease prevalence mapping to predict disease infections with improved user privacy. In one example, a computer-implemented method can include obtaining, by a user computing device associated with a user, a machine-learned prediction model configured to predict a probability that the user may be infected with a disease based at least in part on user-associated data associated with the user. The method can further include receiving, by the user computing device, the user-associated data associated with the user. The method can further include providing, by the user computing device, the user-associated data as input to the machine-learned prediction model, the machine-learned prediction model being implemented on the user computing device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining internet search data, the search data indicating internet searches performed by a population of users. Obtaining location data associated with each user in the population where the location data represents one or more geographic locations of each user over a period of time. Identifying a subset of the population who are likely carrying a contagion based on the search data. Determining an exposure level of a user to the contagion based on a correlation of a first location data associated with the user with a second location data associated with one or more users in the subset of the population who are likely carrying the contagion. Determining whether the user is likely to be or become ill based on the exposure level. Providing a notification indicating that the user has been exposed to the contagion.

Abstract: The present disclosure provides systems and methods that leverage machine-learned models in conjunction with online data to monitor and detect the spread of a disease, such as, for example, a communicable illness. In one example, a computing system can include or otherwise leverage a machine-learned disease detection model. The computing system can input search engine data and, optionally, location data respectively associated with a first plurality of users into the machine-learned disease detection model. The computing system can receive identification of a second plurality of users predicted to have the disease as an output of the machine-learned disease detection model. The second plurality of users can be a subset of the first plurality of users. The computing system can identify one or more locations associated with elevated levels of the disease based at least in part on the location data respectively associated with at least the second plurality of users.

Abstract: Systems and methods for using social network information to predict complex phenomena. According to one embodiment the system or method comprises a Support Vector Machine classifier utilized to infer a pre-determined state of an individual, location, or event based on information gathered from a social network dataset. A conditional random field model can then be used to predict an individual's propensity toward that pre-determined state using features derived from the social network dataset. Performance of the conditional random field model can be enhanced by including features that are not only based on the status of net work connections, but are also based on the estimated encounters with individuals having the pre-determined state, including individuals other than network connections.