A fine-tuned ResNet18 classifies images of Aβ-treated versus untreated neurons with 99.6% accuracy and screens 36 compounds, none of which showed a protective effect.
Derivatives pricing using signature payoffs
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abstract
We introduce signature payoffs, a family of path-dependent derivatives that are given in terms of the signature of the price path of the underlying asset. We show that these derivatives are dense in the space of continuous payoffs, a result that is exploited to quickly price arbitrary continuous payoffs. This approach to pricing derivatives is then tested with European options, American options, Asian options, lookback options and variance swaps. As we show, signature payoffs can be used to price these derivatives with very high accuracy.
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Deep Learning for Estimating Synaptic Health of Primary Neuronal Cell Culture
A fine-tuned ResNet18 classifies images of Aβ-treated versus untreated neurons with 99.6% accuracy and screens 36 compounds, none of which showed a protective effect.