Abstract: An apparatus for facilitating processing of plant material is disclosed. The apparatus includes a base, a cutter disc rotatably coupled to the base such that the cutter disc is configured to rotate about an axis of rotation relative to the base, the cutter disc having an upper side including a plurality of radially spaced teeth, each of the plurality of teeth configured to travel on a respective cutting path to engage and shear the plant material when the cutter disc rotates. The apparatus includes a shearing governor rigidly coupled to the base, the shearing governor including one or more cutting edges, each of the one or more cutting edges extending adjacent to the cutting paths of at least two of the plurality of teeth and configured to shear the plant material when the cutter disc rotates. Other systems and apparatuses are disclosed.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for sparse tensor storage for neural network accelerators. An example apparatus includes sparsity map generating circuitry to generate a sparsity map corresponding to a tensor, the sparsity map to indicate whether a data point of the tensor is zero, static storage controlling circuitry to divide the tensor into one or more storage elements, and a compressor to perform a first compression of the one or more storage elements to generate one or more compressed storage elements, the first compression to remove zero points of the one or more storage elements based on the sparsity map and perform a second compression of the one or more compressed storage elements, the second compression to store the one or more compressed storage elements contiguously in memory.

Abstract: The invention relates to electronic indexing, and more particularly, to the indexing, in a cloud, data held in a cloud. Systems and methods of the invention index data by accessing the data in place in the cloud and breaking a job into work items and sending the work items to multiple cloud processes that can each determine whether to index data associated with the work item or to create a new work item and have a different cloud process index the data. Each cloud process is proximal to an item that it indexes. This gives the system scale as well as an internal load-balancing.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to debug a hardware accelerator such as a neural network accelerator for executing Artificial Intelligence computational workloads. An example apparatus includes a core with a core input and a core output to execute executable code based on a machine-learning model to generate a data output based on a data input, and debug circuitry coupled to the core. The debug circuitry is configured to detect a breakpoint associated with the machine-learning model, compile executable code based on at least one of the machine-learning model or the breakpoint. In response to the triggering of the breakpoint, the debug circuitry is to stop the execution of the executable code and output data such as the data input, data output and the breakpoint for debugging the hardware accelerator.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for sparse tensor storage for neural network accelerators. An example apparatus includes sparsity map generating circuitry to generate a sparsity map corresponding to a tensor, the sparsity map to indicate whether a data point of the tensor is zero, static storage controlling circuitry to divide the tensor into one or more storage elements, and a compressor to perform a first compression of the one or more storage elements to generate one or more compressed storage elements, the first compression to remove zero points of the one or more storage elements based on the sparsity map and perform a second compression of the one or more compressed storage elements, the second compression to store the one or more compressed storage elements contiguously in memory.

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.

Abstract: A computing system retrieves tracking data from a data store. The computing system converts the tracking data into a plurality of graph-based representations. The prediction engine learns to model defensive behavior based on the plurality of graph-based representations. The computing system generates a trained prediction engine based on the learnings. The computing system receives target tracking data for a target event. The target tracking data includes a plurality of target frames. The computing system converts the target tracking data to a plurality of target graph-based representations. The computing system models, via the trained graph neural network, defensive behavior of a team in the target event based on plurality of graph-based representations.

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.