Abstract: Aspects of the disclosure relate to generating scouting objectives in order to update map information used to control a fleet of vehicles in an autonomous driving mode. For instance, a notification from a vehicle of the fleet identifying a feature and a location of the feature may be received. A first bound for a scouting area may be identified based on the location of the feature. A second bound for the scouting area may be identified based on a lane closest to the feature. A scouting objective may be generated for the feature based on the first bound and the second bound.

Abstract: A mounting system for an image capture device that includes three pivotably connected arms is disclosed. The first arm includes a first fastener that is lockable and unlockable to allow for connection and disconnection of the image capture device, and a second fastener that is lockable and unlockable to regulate the position of the image capture device relative to the first arm. The mounting system further includes a third fastener that is lockable and unlockable to regulate the relative positioning between the first arm and the second arm, and a fourth fastener that is lockable and unlockable to regulate the relative positioning between the second arm and the third arm. Whereas the first fastener is removable, each of the second, third, and fourth fasteners is captive to (nonremovable from) the mounting system.

Abstract: Systems and methods are provided for efficiently performing processing intensive operations, such as those involving large volumes of data, that enable accelerated processing time of these operations. In at least one embodiment, a system includes a graphics processor unit (GPU) including a memory and a plurality of cores. The plurality of cores perform a plurality of data analytics operations on a respectively allocated portion of a dataset, each of the plurality of cores using only the memory to store data input for each of the plurality of data analytics operations performed by the plurality of cores. The data storage for the plurality of data analytics operations performed by the plurality of cores is also provided solely by the memory.

Abstract: Systems and methods are provided for efficiently performing processing intensive operations, such as those involving large volumes of data, that enable accelerated processing time of these operations. In at least one embodiment, a system includes a graphics processor unit (GPU) including a memory and a plurality of cores. The plurality of cores perform a plurality of data analytics operations on a respectively allocated portion of a dataset, each of the plurality of cores using only the memory to store data input for each of the plurality of data analytics operations performed by the plurality of cores. The data storage for the plurality of data analytics operations performed by the plurality of cores is also provided solely by the memory.

Abstract: Malicious threat detection through time-series graph analysis, in which a data analysis device receives a data file comprising multiple log data entries. The log data entries include parameters associated with a computer network event in a computing network. The data analysis device produces a graphical model of the computing network based on at least one parameter included in the log data. The data analysis device also identifies a parameter associated with a node of the computer network represented by the graphical model, and performs a time-series analysis on the parameter. The data analysis device further determines, based on the time-series analysis on the parameter, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Systems and methods are provided for efficiently performing processing intensive operations, such as those involving large volumes of data, that enable accelerated processing time of these operations. In at least one embodiment, a system includes a graphics processor unit (GPU) including a memory and a plurality of cores. The plurality of cores perform a plurality of data analytics operations on a respectively allocated portion of a dataset, each of the plurality of cores using only the memory to store data input for each of the plurality of data analytics operations performed by the plurality of cores. The data storage for the plurality of data analytics operations performed by the plurality of cores is also provided solely by the memory.

Abstract: Graph database analysis for network anomaly detection systems, in which a data analysis device receives multiple log data entries including parameters associated with a computer network event in a computing network. The data analysis device extracts one or more parameters in real-time and generates a network event graph based on at least one of a first graph metric or a second graph metric. The first and second graph metrics are based on the one or more extracted parameters. The data analysis device detects, based on queries performed on the network event graph, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Graph database analysis for network anomaly detection systems, in which a data analysis device receives multiple log data entries including parameters associated with a computer network event in a computing network. The data analysis device extracts one or more parameters in real-time and generates a network event graph based on at least one of a first graph metric or a second graph metric. The first and second graph metrics are based on the one or more extracted parameters. The data analysis device detects, based on queries performed on the network event graph, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Malicious threat detection through time-series graph analysis, in which a data analysis device receives a data file comprising multiple log data entries. The log data entries include parameters associated with a computer network event in a computing network. The data analysis device produces a graphical model of the computing network based on at least one parameter included in the log data. The data analysis device also identifies a parameter associated with a node of the computer network represented by the graphical model, and performs a time-series analysis on the parameter. The data analysis device further determines, based on the time-series analysis on the parameter, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Graph database analysis for network anomaly detection systems, in which a data analysis device receives multiple log data entries including parameters associated with a computer network event in a computing network. The data analysis device extracts one or more parameters in real-time and generates a network event graph based on at least one of a first graph metric or a second graph metric. The first and second graph metrics are based on the one or more extracted parameters. The data analysis device detects, based on queries performed on the network event graph, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Malicious threat detection through time-series graph analysis, in which a data analysis device receives a data file comprising multiple log data entries. The log data entries include parameters associated with a computer network event in a computing network. The data analysis device produces a graphical model of the computing network based on at least one parameter included in the log data. The data analysis device also identifies a parameter associated with a node of the computer network represented by the graphical model, and performs a time-series analysis on the parameter. The data analysis device further determines, based on the time-series analysis on the parameter, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: A system acquires geocoded data, smoothes the geocoded data, and displays the smoothed geocoded data in a graphical visualization representative of the physical locations of the acquired geocoded data. The system obtains an indication of operational parameters for smoothing geocoded data, such as a data precision level, viewing window, data characteristics, and smoothing parameters. The system generates an area map that includes entries for each location in the viewing window (or other predetermined area) according to the data precision level. The system may populate any entries without associated data values with a zero value. To process the geocoded data, the system may determine a vector of interest from the area map and generate, for example, a single smoothing matrix that smoothes each of the values in the vector of interest. The system applies the smoothing matrix to the vector of interest.

Abstract: Malicious threat detection through time-series graph analysis, in which a data analysis device receives a data file comprising multiple log data entries. The log data entries include parameters associated with a computer network event in a computing network. The data analysis device produces a graphical model of the computing network based on at least one parameter included in the log data. The data analysis device also identifies a parameter associated with a node of the computer network represented by the graphical model, and performs a time-series analysis on the parameter. The data analysis device further determines, based on the time-series analysis on the parameter, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: Graph database analysis for network anomaly detection systems, in which a data analysis device receives multiple log data entries including parameters associated with a computer network event in a computing network. The data analysis device extracts one or more parameters in real-time and generates a network event graph based on at least one of a first graph metric or a second graph metric. The first and second graph metrics are based on the one or more extracted parameters. The data analysis device detects, based on queries performed on the network event graph, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Abstract: A network security system detects anomalous network device behavior associated with a network device in a group of similar network devices based on baseline network device behavior determined for the group. A graphical visualization may be generated to analyze the groups.

Abstract: A network security system detects anomalous network device behavior associated with a network device in a group of similar network devices based on baseline network device behavior determined for the group. A graphical visualization may be generated to analyze the groups.

Abstract: Provided herein are compositions and methods including hydraulic cement, supplementary cementitious material, and/or self-cementing material. Methods for making the compositions and using the compositions are provided.

Abstract: Provided herein are compositions, methods, and systems for cementitious compositions containing calcium carbonate compositions and aggregate. The compositions find use in a variety of applications, including use in a variety of building materials and building applications.

Abstract: Provided herein are compositions and methods including hydraulic cement, supplementary cementitious material, and/or self-cementing material. Methods for making the compositions and using the compositions are provided.

Abstract: Provided herein are compositions and methods including hydraulic cement, supplementary cementitious material, and/or self-cementing material. Methods for making the compositions and using the compositions are provided.