arXiv subjects

Michael Wagner

Publications and source records attributed to Michael Wagner.

21 records · Page 2Linked to original sources

arXiv2020

Positive Trust Balance for Self-Driving Car Deployment

The crucial decision about when self-driving cars are ready to deploy is likely to be made with insufficient lagging metric data to provide high confidence in an acceptable safety outcome. A Positive Trust Balance approach can help with making a responsible deployment decision despite this uncertainty. With this approach, a reasonable initial expectation of safety is based on a combination of a practicable amount of testing, engineering rigor, safety culture, and a strong commitment to use post-deployment operational feedback to further reduce uncertainty. This can enable faster deployment than would be required by more traditional safety approaches by reducing the confidence necessary at time of deployment in exchange for a more stringent requirement for Safety Performance Indicator (SPI) field feedback in the context of a strong safety culture.

cs.CY↗

arXiv2017

Efficient Computation of Sparse Matrix Functions for Large-Scale Electronic Structure Calculations: The CheSS Library

We present CheSS, the "Chebyshev Sparse Solvers" library, which has been designed to solve typical problems arising in large-scale electronic structure calculations using localized basis sets. The library is based on a flexible and efficient expansion in terms of Chebyshev polynomials and presently features the calculation of the density matrix, the calculation of matrix powers for arbitrary powers, and the extraction of eigenvalues in a selected interval. CheSS is able to exploit the sparsity of the matrices and scales linearly with respect to the number of nonzero entries, making it well-suited for large-scale calculations. The approach is particularly adapted for setups leading to small spectral widths of the involved matrices and outperforms alternative methods in this regime. By coupling CheSS to the DFT code BigDFT, we show that such a favorable setup is indeed possible in practice. In addition, the approach based on Chebyshev polynomials can be massively parallelized, and CheSS exhibits excellent scaling up to thousands of cores even for relatively small matrix sizes.

physics.chem-ph↗

arXiv2012

Bayesian Biosurveillance of Disease Outbreaks

Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be performed in real time as population data stream in. We describe techniques we have applied to address both the modeling and inference challenges. A key contribution of this paper is the explication of assumptions and techniques that are sufficient to allow the scaling of Bayesian network modeling and inference to millions of nodes for real-time surveillance applications. The results reported here provide a proof-of-concept that Bayesian networks can serve as the foundation of a system that effectively performs Bayesian biosurveillance of disease outbreaks.

stat.AP↗