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Found 8 papers, 4 papers with code
On Integrating Prior Knowledge into Gaussian Processes for Prognostic Health Monitoring
We show that well-chosen mean and covariance functions, like those based on previous data, significantly increase look-ahead time and accuracy.
Image prediction of disease progression by style-based manifold extrapolation
We present a generic solution for this problem by a methodology that allows the prediction of progression risk and morphology in individuals using a latent extrapolation optimization approach.
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
Contrary to previous research on adversarially trained models, we found that the accuracy of such models was equal to standard models when sufficiently large datasets and dual batch norm training were used.
On the Potential of Extending Aircraft Service Time Using a Fatigue Damage Index
Aircraft structures experience various kinds of loads over their entire lifetime, typically leading to fatigue and ultimately structural failure.
Predicting Crack Growth and Fatigue Life with Surrogate Models
For a coupon test, we predict the time to failure and the crack growth with confidence intervals.
Small World with High Risks: A Study of Security Threats in the npm Ecosystem
Studying the potential for running vulnerable or malicious code due to third-party dependencies, we find that individual packages could impact large parts of the entire ecosystem.
Cryptography and Security
The ALICE analysis train system
In the ALICE experiment hundreds of users are analyzing big datasets on a Grid system.
High Energy Physics - Experiment Distributed, Parallel, and Cluster Computing
