38,507 research outputs found
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
Cyber-physical systems (CPS), such as automotive systems, are starting to
include sophisticated machine learning (ML) components. Their correctness,
therefore, depends on properties of the inner ML modules. While learning
algorithms aim to generalize from examples, they are only as good as the
examples provided, and recent efforts have shown that they can produce
inconsistent output under small adversarial perturbations. This raises the
question: can the output from learning components can lead to a failure of the
entire CPS? In this work, we address this question by formulating it as a
problem of falsifying signal temporal logic (STL) specifications for CPS with
ML components. We propose a compositional falsification framework where a
temporal logic falsifier and a machine learning analyzer cooperate with the aim
of finding falsifying executions of the considered model. The efficacy of the
proposed technique is shown on an automatic emergency braking system model with
a perception component based on deep neural networks
Pricing and Risk Management with High-Dimensional Quasi Monte Carlo and Global Sensitivity Analysis
We review and apply Quasi Monte Carlo (QMC) and Global Sensitivity Analysis
(GSA) techniques to pricing and risk management (greeks) of representative
financial instruments of increasing complexity. We compare QMC vs standard
Monte Carlo (MC) results in great detail, using high-dimensional Sobol' low
discrepancy sequences, different discretization methods, and specific analyses
of convergence, performance, speed up, stability, and error optimization for
finite differences greeks. We find that our QMC outperforms MC in most cases,
including the highest-dimensional simulations and greeks calculations, showing
faster and more stable convergence to exact or almost exact results. Using GSA,
we are able to fully explain our findings in terms of reduced effective
dimension of our QMC simulation, allowed in most cases, but not always, by
Brownian bridge discretization. We conclude that, beyond pricing, QMC is a very
promising technique also for computing risk figures, greeks in particular, as
it allows to reduce the computational effort of high-dimensional Monte Carlo
simulations typical of modern risk management.Comment: 43 pages, 21 figures, 6 table
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