Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Statistical choices and apparent work outcomes in auditing

The public accounting sector of the accounting profession has long been very concerned with the problem of employee recruitment and retention. As early as the 1970s, the then Big 8 firms funded extensive studies of the determinants of employee turnover. The problem is no less real today. Indeed, much has been written about the problem of the vanishing accounting student. If reducing employee turnover and dissatisfaction becomes important in order for the public accounting firms to fulfill their mission of helping to assure the quality of information that investors receive, then having tools that foster an understanding of the determinants of employee dissatisfaction, stress, and turnover is vital Sheds light on these issues by demonstrating how sophisticated statistical techniques can illuminate the underlying determinants of employee turnover and other important job attitudes. Applies structural equation modeling to Collins and Killough\u27s dataset in order to demonstrate how it can provide important additional substantive insights about relationships between the stressors and job outcomes in public accounting. This important interpretive information is not available, or is available in only limited fashion, in the comparison method of canonical correlation analysis