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In finance, implied volatility is an important indicator that reflects the market situation immediately. Many practitioners estimate volatility by using iteration methods, such as the Newton–Raphson (NR) method. However, if numerous implied volatilities must be computed frequently, the iteration methods easily reach the processing speed limit. Therefore, we emulate the NR method as a network by using PyTorch, a well-known deep learning package, and optimize the network further by using TensorRT, a package for optimizing deep learning models. Comparing the optimized emulation method with the benchmarks, implemented in two popular Python packages, we demonstrate that the emulation network is up to 1000 times faster than the benchmark functions.

IntroductionHyaluronan synthase 1 (HAS1) is a membrane-bound protein that is abundant in the epidermis and dermis, and it is important for skin function. However, its association with hearing loss has not yet been studied. Herein, we sought to evaluate the potential contribution of HAS1: c.1082G>A for genetic hearing loss. MethodsWe used whole-exome sequencing to analyze blood DNA samples of six patients of a family with autosomal dominant familial late-onset progressive hearing loss, which was revealed to be related to a variant of HAS1 gene. Confirmatory Sanger sequencing used samples from 10 members. A missense variant was detected in HAS1 (c.1082 G>A, p.Cys361Tyr). In silico analyses predicted this variant to result in the functional loss of HAS1. Immunostaining was conducted using wild type mouse samples for verifying HAS1 expression. ResultsHAS1 was detected in E10.5 otic cyst. In the pup, it was localized to the stria vascularis (SV), hair cells, supporting cells of the organ of Corti, and some spiral ganglion neurons. SV marginal cells markedly expressed HAS1 in the adult stage. The hearing threshold in the HAS1-depleted condition was investigated by accessing the International Mouse Phenotyping Consortium