Abstract: One implementation of the disclosure is directed to an Information Technology Service Intelligence (ITSI) that provides methods having operations of obtaining historical data for a metric including a plurality of time-series data sets spanning a common time period and each associated with a distinct entity, generating an aggregated time-series data set by applying an aggregation function to corresponding data points in each of the plurality of time-series data sets, selecting a seasonality pattern for the aggregated time-series data set from a plurality of candidate seasonality patterns by computing a silhouette score for each of the plurality of candidate seasonality patterns in view of the time-series data set, wherein a selected seasonality pattern has a highest silhouette score, and partitioning the aggregated time-series data set into subsequences according to the selected seasonality pattern, and generating a plurality of severity level thresholds for each subsequence.

Abstract: Examples disclosed herein may estimate locations of players not visible in a sporting broadcast video. A prediction model may be generated based on a training data set of in-venue tracking data that includes locations of all players at all times and the corresponding broadcast tracking data that may not necessarily contain the locations of all players at all times. The prediction model may be based on an algorithmic logic (e.g., a spline regression) or machine learning model (e.g., k-nearest neighbor, deep neural network). The generated predicted model may be used to estimate the unknown locations of players in broadcast tracking based on the known locations.

Abstract: Examples disclosed herein may estimate locations of players not visible in a sporting broadcast video. A prediction model may be generated based on a training data set of in-venue tracking data that includes locations of all players at all times and the corresponding broadcast tracking data that may not necessarily contain the locations of all players at all times. The prediction model may be based on an algorithmic logic (e.g., a spline regression) or machine learning model (e.g., k-nearest neighbor, deep neural network). The generated predicted model may be used to estimate the unknown locations of players in broadcast tracking based on the known locations.

Abstract: A computing system identifies broadcast video for a plurality of games in a first league. The broadcast video includes a plurality of video frames. The computing system generates tracking data for each game from the broadcast video of a corresponding game. The computing system enriches the tracking data. The enriching includes merging play-by-play data for the game with the tracking data of the corresponding game. The computing system generates padded tracking data based on the tracking data. The computing system projects player performance in a second league for each player based on the tracking data and the padded tracking data.

Abstract: A computing system identifies broadcast video data for a game. The computing system generates tracking data for the game from the broadcast video data using computer vision techniques. The tracking data includes coordinates of players during the game. The computing system generates optical character recognition data for the game from the broadcast video data by applying one or more optical character recognition techniques to each frame of the plurality of frames to extract score and time information from a scoreboard displayed in each frame. The computing system detects a plurality of events that occurred in the game by applying one or more machine learning techniques to the tracking data. The computing system receives play-by-play data for the game. The computing system generates enriched tracking data. The generating includes merging the play-by-play data with one or more of the tracking data, the optical character recognition data, and the plurality of events.

Abstract: A computing system identifies broadcast video data for a game. The computing system generates tracking data for the game from the broadcast video data using computer vision techniques. The tracking data includes coordinates of players during the game. The computing system generates optical character recognition data for the game from the broadcast video data by applying one or more optical character recognition techniques to each frame of the plurality of frames to extract score and time information from a scoreboard displayed in each frame. The computing system detects a plurality of events that occurred in the game by applying one or more machine learning techniques to the tracking data. The computing system receives play-by-play data for the game. The computing system generates enriched tracking data. The generating includes merging the play-by-play data with one or more of the tracking data, the optical character recognition data, and the plurality of events.

Abstract: The present embodiments relate to tracking player movements from a video broadcast and determining a defensive influence score from the tracked movements of the player. The present embodiments can implement one or more models to generate a defensive influence score that quantifies a defensive intensity of a player during the course of a game. The defensive influence score can include a frame-by-frame machine learning prediction that can be used to estimate the defensive pressure a player is having on another player during the course of the game. Additionally, the present embodiments can capture and estimate fitness metrics, such as sprints and efforts around detected plays such as pick-and-rolls and off-ball screens, which can be good proxies for player effort. Further, event detection outputs (both offensive and defensive metrics), can be used as features to estimate fitness metrics for the player (e.g., player load, sprints, jogs, etc.).

Abstract: A method for generating coupled play, drive, and game outcome predictions for a sporting event, the method including: inputting features for a sporting event into an initial model, the input features including historical game data and in-game data; determining a predicted outcome for at least one upcoming play with the initial model; determining, using the input features and predicted outcome, a predicted probability of each team winning the sporting event; and determining a probability of success for an action in the at least one upcoming play of the sporting event.

Abstract: A rotor assembly for use in a radial flux electric motor assembly is provided. The rotor assembly includes a rotor core having a plurality of circumferentially spaced rotor poles and a plurality of magnets alternately spaced with the plurality of rotor poles. The plurality of rotor poles define a radial aperture between each pair of circumferentially adjacent rotor poles, and each radial aperture is configured to receive a magnet of the plurality of magnets therein. The rotor assembly further includes an end cap coupled to the rotor core and to the plurality of core magnets. The end cap includes a plurality of deformable members positioned between the rotor core and the plurality of magnets and configured to restrict movement of the plurality of magnets within the radial apertures. The deformable members are configured to deform upon insertion into a cavity defined between the rotor core and the plurality of magnets.

Abstract: A computing system identifies broadcast video data for a game. The computing system generates tracking data for the game from the broadcast video data using computer vision techniques. The tracking data includes coordinates of players during the game. The computing system generates optical character recognition data for the game from the broadcast video data by applying one or more optical character recognition techniques to each frame of the plurality of frames to extract score and time information from a scoreboard displayed in each frame. The computing system detects a plurality of events that occurred in the game by applying one or more machine learning techniques to the tracking data. The computing system receives play-by-play data for the game. The computing system generates enriched tracking data. The generating includes merging the play-by-play data with one or more of the tracking data, the optical character recognition data, and the plurality of events.

Abstract: A computing system identifies broadcast video data for a game. The computing system generates tracking data for the game from the broadcast video data using computer vision techniques. The tracking data includes coordinates of players during the game. The computing system generates optical character recognition data for the game from the broadcast video data by applying one or more optical character recognition techniques to each frame of the plurality of frames to extract score and time information from a scoreboard displayed in each frame. The computing system detects a plurality of events that occurred in the game by applying one or more machine learning techniques to the tracking data. The computing system receives play-by-play data for the game. The computing system generates enriched tracking data. The generating includes merging the play-by-play data with one or more of the tracking data, the optical character recognition data, and the plurality of events.

Abstract: Methods and systems for control of a power-quality measuring or monitoring device, such as a transfer switch, are provided. An example method includes a transfer-switch controller of a transfer switch receiving an input command from a user. The method further includes, in response to receiving the input command, the transfer-switch controller entering a safe state, wherein in the safe state operational settings of the transfer switch remain unchanged. Still further, the method includes, after entering the safe state, the transfer-switch controller providing, based on operational data specific to the transfer switch, information regarding a feature of the transfer switch.

Abstract: A computing system identifies broadcast video for a plurality of games in a first league. The broadcast video includes a plurality of video frames. The computing system generates tracking data for each game from the broadcast video of a corresponding game. The computing system enriches the tracking data. The enriching includes merging play-by-play data for the game with the tracking data of the corresponding game. The computing system generates padded tracking data based on the tracking data. The computing system projects player performance in a second league for each player based on the tracking data and the padded tracking data.

Abstract: A rotor assembly for use in a radial flux electric motor assembly is provided. The rotor assembly includes a rotor core having a plurality of circumferentially spaced rotor poles and a plurality of magnets alternately spaced with the plurality of rotor poles. The plurality of rotor poles define a radial aperture between each pair of circumferentially adjacent rotor poles, and each radial aperture is configured to receive a magnet of the plurality of magnets therein. The rotor assembly further includes an end cap coupled to the rotor core and to the plurality of core magnets. The end cap includes a plurality of deformable members positioned between the rotor core and the plurality of magnets and configured to restrict movement of the plurality of magnets within the radial apertures. The deformable members are configured to deform upon insertion into a cavity defined between the rotor core and the plurality of magnets.

Abstract: A computing system identifies broadcast video for a plurality of games in a first league. The broadcast video includes a plurality of video frames. The computing system generates tracking data for each game from the broadcast video of a corresponding game. The computing system enriches the tracking data. The enriching includes merging play-by-play data for the game with the tracking data of the corresponding game. The computing system generates padded tracking data based on the tracking data. The computing system projects player performance in a second league for each player based on the tracking data and the padded tracking data.

Abstract: Examples disclosed herein may estimate locations of players not visible in a sporting broadcast video. A prediction model may be generated based on a training data set of in-venue tracking data that includes locations of all players at all times and the corresponding broadcast tracking data that may not necessarily contain the locations of all players at all times. The prediction model may be based on an algorithmic logic (e.g., a spline regression) or machine learning model (e.g., k-nearest neighbor, deep neural network). The generated predicted model may be used to estimate the unknown locations of players in broadcast tracking based on the known locations.

Abstract: A computing system receives a training data set that includes a first subset of labeled events and a second subset of unlabeled events for an event type. The computing system generates an event model configured to detect the event type and classify the event type by actively training the event model. The computing system receives a target game file for a target game. The target game file includes at least tracking data corresponding to players in the target game. The computing system identifies a plurality of instances of the event type in the target game using the event model. The computing system classifies each instance of the plurality of instances of the event type using the event model. The computing system generates an updated event game file based on the target game file and the plurality of instances.

Abstract: Physical assets in a network are graphically modeled and controlled using a monitoring and control interface. Prior to committing configuration changes to the physical assets, the draft configuration changes are analyzed to determine if they violate any logical configuration change rules. The logical configuration rules can be model-based according to target physical asset(s) for the draft configuration changes. A logical configuration change rule violation can be graphically displayed on the monitoring and control interface. The configuration changes can be stored to debug the physical assets and to roll back the configuration of the physical assets to a prior state.

Abstract: Physical assets in a network are graphically modeled and controlled using a monitoring and control interface. Prior to committing configuration changes to the physical assets, the draft configuration changes are analyzed to determine if they violate any logical configuration change rules. The logical configuration rules can be model-based according to target physical asset(s) for the draft configuration changes. A logical configuration change rule violation can be graphically displayed on the monitoring and control interface. The configuration changes can be stored to debug the physical assets and to roll back the configuration of the physical assets to a prior state.

Abstract: Provided herein, in some embodiments, are methods of purifying low-salt RNA compositions using denaturing oligo-dT chromatography.