Abstract: A method of generating a player prediction is disclosed herein. A computing system retrieves data from a data store. The computing system generates a predictive model using an artificial neural network. The artificial neural network generates one or more personalized embeddings that include player-specific information based on historical performance. The computing system selects, from the data, one or more features related to each shot attempt captured in the data. The artificial neural network learns an outcome of each shot attempt based at least on the one or more personalized embeddings and the one or more features related to each shot attempt.

Abstract: A lighting apparatus a first group of at least one first solid state emitter, each first solid state emitter including a first light emitting diode (“LED”) that, when excited, emits light having a peak wavelength in a range between about 440 nm and about 475 nm, and a second group of at least one second solid state emitter, each second solid state emitter comprising a second LED that, when excited, emits light having a peak wavelength in a range between about 390 nm and about 415 nm. Between about 2% and about 15% of a spectral power of light emitted from the lighting apparatus is light having wavelengths in the range between about 390 nm and about 415 nm.

Abstract: A computing system retrieves ball-by-ball data for a plurality of sporting events. The computing system generates a trained neural network based on ball-by-ball data supplemented with ball-by-ball data with ball-by-ball match context features and personalized embeddings based on a batsman and a bowler for each delivery. The computing system receives a target batsman and a target bowler for a pitch to be delivered in a target event. The computing system identifies target ball-by-ball data for a window of pitches preceding the to be delivered pitch. The computing system retrieves historical ball-by-ball data for each of the target batsman and the target bowler. The computing system generates personalized embeddings for both the target batsman and the target bowler based on the historical ball-by-ball data. The computing system predicts a shot type for the pitch to be delivered based on the target ball-by-ball data and the personalized embeddings.

Abstract: A lighting apparatus a first group of at least one first solid state emitter, each first solid state emitter including a first light emitting diode (“LED”) that, when excited, emits light having a peak wavelength in a range between about 440 nm and about 475 nm, and a second group of at least one second solid state emitter, each second solid state emitter comprising a second LED that, when excited, emits light having a peak wavelength in a range between about 390 nm and about 415 nm. Between about 2% and about 15% of a spectral power of light emitted from the lighting apparatus is light having wavelengths in the range between about 390 nm and about 415 nm.

Abstract: A computing system retrieves ball-by-ball data for a plurality of sporting events. The computing system generates a trained neural network based on ball-by-ball data supplemented with ball-by-ball data with ball-by-ball match context features and personalized embeddings based on a batsman and a bowler for each delivery. The computing system receives a target batsman and a target bowler for a pitch to be delivered in a target event. The computing system identifies target ball-by-ball data for a window of pitches preceding the to be delivered pitch. The computing system retrieves historical ball-by-ball data for each of the target batsman and the target bowler. The computing system generates personalized embeddings for both the target batsman and the target bowler based on the historical ball-by-ball data. The computing system predicts a shot type for the pitch to be delivered based on the target ball-by-ball data and the personalized embeddings.

Abstract: A method of designing a standard cell layout includes determining a performance metric for the standard cell layout and executing an artificial intelligence (AI) algorithm. The executing of the AI algorithm includes extracting out a parameter of the standard cell layout having a different weighting with respect to optimizing the performance metric, adjusting the parameters of the standard cell layout, evaluating the performance metric based on the adjusted parameter of the standard cell layout, and continuing to adjust the one or more parameters until the performance metric reaches a desired value.

Abstract: A method includes providing a carrier substrate having a die bonded thereto, where the die includes a first alignment mark on a first surface. The method includes positioning a target substrate with a second surface on a substrate stage, where the target substrate includes a second alignment mark on the second surface. The method includes positioning the carrier substrate with respect to a die handler, where the die handler includes a third alignment mark. The method includes coupling the die to the die handler, where the step of coupling includes aligning the first alignment mark with the third alignment mark. The method includes positioning the coupled die and the die handler over the target substrate, where the step of positioning includes aligning the second alignment mark with at least one of the first alignment mark and the third alignment mark. The method includes bonding the first surface with the second surface.

Abstract: A computing system retrieves ball-by-ball data for a plurality of sporting events. The computing system generates a trained neural network based on ball-by-ball data supplemented with ball-by-ball data with ball-by-ball match context features and personalized embeddings based on a batsman and a bowler for each delivery. The computing system receives a target batsman and a target bowler for a pitch to be delivered in a target event. The computing system identifies target ball-by-ball data for a window of pitches preceding the to be delivered pitch. The computing system retrieves historical ball-by-ball data for each of the target batsman and the target bowler. The computing system generates personalized embeddings for both the target batsman and the target bowler based on the historical ball-by-ball data. The computing system predicts a shot type for the pitch to be delivered based on the target ball-by-ball data and the personalized embeddings.

Abstract: A process includes forming, over a dielectric layer, a hardmask stack including a first layer below a second layer below a third layer below a fourth layer. The first and third layers include a different hardmask material from the second and fourth layers. A trench pattern including sidewall spacer structures is formed over the hardmask stack. The fourth layer is etched in a first region. The fourth and third layers are etched in a second region. The fourth and third layers are etched in a third region. The fourth layer is etched in a fourth region. The second and first layers are etched in the second and third regions. The third layer is etched in the first and fourth regions. In the dielectric layer, trenches are formed in the first and fourth regions, and via openings, deeper than the trenches, are formed in the second and third regions.

Abstract: A method of generating a player prediction is disclosed herein. A computing system retrieves data from a data store. The computing system generates a predictive model using an artificial neural network. The artificial neural network generates one or more personalized embeddings that include player-specific information based on historical performance. The computing system selects, from the data, one or more features related to each shot attempt captured in the data. The artificial neural network learns an outcome of each shot attempt based at least on the one or more personalized embeddings and the one or more features related to each shot attempt.

Abstract: A method of patterning a substrate, where the method includes: forming first structures over a memorization layer, the first structures including a first row of lines that are parallel with each other and spaced apart from each other; executing a first anti-spacer formation process to form first trenches along sidewalls of the first structures and sidewalls of a first fill material, the first trenches defining a first etch pattern; transferring the first etch pattern into the memorization layer and removing materials above the memorization layer; forming second structures over the memorization layer, the second structures including a second row of lines that are parallel with each other and spaced apart, placement of the second row of lines being shifted relative to the first row of lines; executing a second anti-spacer formation process to form second trenches formed along sidewalls of the second structures and sidewalls of a second fill material, the second trenches defining a second etch pattern; and trans

Abstract: A computing system retrieves ball-by-ball data for a plurality of sporting events. The computing system generates a trained neural network based on ball-by-ball data supplemented with ball-by-ball data with ball-by-ball match context features and personalized embeddings based on a batsman and a bowler for each delivery. The computing system receives a target batsman and a target bowler for a pitch to be delivered in a target event. The computing system identifies target ball-by-ball data for a window of pitches preceding the to be delivered pitch. The computing system retrieves historical ball-by-ball data for each of the target batsman and the target bowler. The computing system generates personalized embeddings for both the target batsman and the target bowler based on the historical ball-by-ball data. The computing system predicts a shot type for the pitch to be delivered based on the target ball-by-ball data and the personalized embeddings.

Abstract: A method of generating a player prediction is disclosed herein. A computing system retrieves data from a data store. The computing system generates a predictive model using an artificial neural network. The artificial neural network generates one or more personalized embeddings that include player-specific information based on historical performance. The computing system selects, from the data, one or more features related to each shot attempt captured in the data. The artificial neural network learns an outcome of each shot attempt based at least on the one or more personalized embeddings and the one or more features related to each shot attempt.

Abstract: A lighting apparatus a first group of at least one first solid state emitter, each first solid state emitter including a first light emitting diode (“LED”) that, when excited, emits light having a peak wavelength in a range between about 440 nm and about 475 nm, and a second group of at least one second solid state emitter, each second solid state emitter comprising a second LED that, when excited, emits light having a peak wavelength in a range between about 390 nm and about 415 nm. Between about 2% and about 15% of a spectral power of light emitted from the lighting apparatus is light having wavelengths in the range between about 390 nm and about 415 nm.

Abstract: A data processing apparatus comprises a processing unit to generate image data for rendering data for display as an image frame, a memory to store the image data, the image data comprising data for a given object in the image frame, detection circuitry to detect, for the data for the given object, one or more properties associated with the given object in the image frame, prediction circuitry to generate, based on one or more of the properties, a confidence score for the data for the given object indicative of a likelihood that the data for the given object is to be used for more than a threshold number of image frames in a sequence of image frames, and allocation circuitry to allocate indicator data to the data for the given object, the indicator data indicative of a magnitude of the confidence score for the data for the given object, and to store the data for the given object in the memory in association with the corresponding indicator data.

Abstract: A lighting apparatus a first group of at least one first solid state emitter, each first solid state emitter including a first light emitting diode (“LED”) that, when excited, emits light having a peak wavelength in a range between about 440 nm and about 475 nm, and a second group of at least one second solid state emitter, each second solid state emitter comprising a second LED that, when excited, emits light having a peak wavelength in a range between about 390 nm and about 415 nm. Between about 2% and about 15% of a spectral power of light emitted from the lighting apparatus is light having wavelengths in the range between about 390 nm and about 415 nm.

Abstract: A computing system receives a plurality of game files corresponding to a plurality of games across a plurality of seasons. The computing system generates a prediction model configured to generate a possession value for an event. The computing system receives a target event, in real-time or near real-time, from a tracking system monitoring a target game. The computing system generates target features for the target event based on target event data associated with the target event. The computing system generates, via the prediction model, a target possession value for the target event based on the target event data and the target features. The target possession value represents a likelihood that a team with possession will score within a following x-seconds after the target event.

Abstract: A method of manufacturing a hypoid gear includes face hobbing a gear blank and forming a green hypoid gear with gear teeth, heat treating the green hypoid gear to form a heat treated hypoid gear with heat treated gear teeth, and hard hobbing the heat treated gear teeth to form a hard finished hypoid gear. Critical non-tooth features on the heat treated hypoid gear are hard finished. Also, the critical non-tooth features on the heat treated hypoid gear can be hard finished prior to hard hobbing the heat treated gear teeth. The heat treating includes at least one of carburizing and induction hardening the green hypoid gear, a surface of the heat treated gear teeth has a hardness greater than or equal to 58 HRC, and the hard hobbing removes heat distortion from the heat treated gear teeth.

Abstract: A computing system receives a request to project a performance of a first player from a current team on a destination team. The computing system generates, based on the request, player-position features corresponding to the first player. The computing system generates team features corresponding to the first player. The computing system generates rating features for the first player. The computing system generates, via a prediction model, a player box score prediction based on the player-position features, the team features, and the rating features. The player box score prediction includes a plurality of per game metrics of the first player on the destination team.

Abstract: A computing system receives a pre-game lineup against a target opponent. The pre-game lineup includes a representation of each player starting a game against the target opponent. The computing system retrieves a first set of historical data for each player in the pre-game lineup and team-specific information. The computing system retrieves a second set of historical data for each player of the target opponent and target opponent-specific information. The computing system predicts an outcome for the game based on the first set of historical data and the second set of historical data. The computing system projects a future effect of the pre-game lineup on at least one season of play by simulating team and player performance. The computing system generates a graphical output reflecting the predicted outcome of the game and the simulation of team and player performance over the at least one season of play.