Abstract: A method for manufacturing a coating-free press hardening steel (CFPHS) component comprises heating a CFPHS blank at a first predetermined heating rate to a first predetermined temperature in a first predetermined temperature range using a heater; transferring the CFPHS blank to a furnace; soaking the CFPHS blank in the furnace at a second predetermined temperature in a second predetermined temperature range for a predetermined period; and pressing and forming the CFPHS blank in a stamp/press to form a CFPHS component.

Abstract: A method to achieve variable properties of a component using a coating free press hardened steel comprises: transferring a blank of a coating free press hardened steel (CFPHS) material having base blank properties into a heating unit having multiple induction heating coils; heating the blank within the heating unit to create a modified blank having differing modified blank properties throughout the modified blank compared to base blank properties; moving the modified blank out of the heating unit into a die; and forming a shaped part by operation of the die creating a finished CFPHS part.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods to generate sketches for clearing-bid values and bid-success rates based on multi-dimensional targeting criteria for a digital-content campaign and dynamically determine predicted values for the digital-content campaign based on the sketches. To illustrate, the disclosed systems can use a running-average-tuple-sketch to generate tuple sketches of historical clearing-bid values and tuple sketches of historical bid-success-rates from historical auction data. Based on the tuple sketches, the disclosed systems can determine one or more of a predicted cost per quantity of impressions, a predicted number of impressions, or a predicted expenditure for the digital-content campaign—according to user-input targeting criteria and expenditure constraints.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods to generate sketches for clearing-bid values and bid-success rates based on multi-dimensional targeting criteria for a digital-content campaign and dynamically determine predicted values for the digital-content campaign based on the sketches. To illustrate, the disclosed systems can use a running-average-tuple-sketch to generate tuple sketches of historical clearing-bid values and tuple sketches of historical bid-success-rates from historical auction data. Based on the tuple sketches, the disclosed systems can determine one or more of a predicted cost per quantity of impressions, a predicted number of impressions, or a predicted expenditure for the digital-content campaign—according to user-input targeting criteria and expenditure constraints.