Recommendation systems represent one of the largest machine learning applications on the planet -- industry-scale recommendation models are trained with petabytes of data and serve billions of users every day. To utilize the rich user signals in the long user history, these models have been scaled up to unprecedented complexity, up to trillions of floating-point operations (TFLOPs) per example. This scale, coupled with the huge amount of training data, necessitates new storage and training algorithms to efficiently improve the quality of these complex recommendation systems.

The quality of pressure signals is intricately linked to the speed variation characteristics of the permanent magnet synchronous motor (PMSM) employed to drive the rotor of the continuous wave pulse generator. However, achieving precise control of PMSM is significantly challenged by complex and time-varying drilling operating conditions. A serialnADRC (SnADRC) is presented to increase the control motor's immunity and dynamic performance, where “n” indicates the number of sequential output stacking extended state observers (SOS-ESOs) equal to the highest order of the disturbance. Based on the deduced continuous wave pulse generator rotor model, first-order SOS-ESO is intended to detect the first-order disturbance, with the higher order SOS-ESO designed to anticipate the residual disturbances in order to accomplish real-time and precise hydraulic torque prediction. Moreover, the estimation performance of SOS-ESO is analyzed, and the intrinsic stability of SnADRC with the speed loop control system is rigorously demonstrated using the Lyapunov theory. Additionally, SOS-ESO is employed in simulation to resist polynomial disturbances, and it is discovered that thenthorder SOS-ESO may successfully suppress complex disturbances up to (n-1) order. Finally, the control performance of SnADRC is compared to that of classical control methods in both the simulation and experiment, and the results show that SnADRC has excellent rapidity and stability in motor control, which opens up a wide range of possibilities for practical applications in control and engineering fields.

Primary key and referential integrity are the most widely used integrity constraints in relational databases. Each has a sequenced analogue in temporal databases, in which the constraint must apply independently at every point in time. In this paper, we assume a stratum approach, which expresses the checking in conventional SQL, as triggers on period-stamped relations. We evaluate several novel approaches that exploit B/sup +/-tree indexes to enable efficient checking of sequenced primary key (SPK) and sequenced referential integrity (SRI) constraints. We start out with a brute-force SPK algorithm, then adapt the relational interval-tree overlap algorithm. After that, we propose a new method, the straight traversal algorithm, which utilizes the B/sup +/-tree more directly in order to identify when multiple key values are present. Our evaluation, on two platforms, shows that the straight traversal algorithm approaches the performance of built-in nontemporal primary key and referential integrity checking, with a constant time per tuple.