Adaptive Cloud Application Tuning with Enhanced Structural Bayesian Optimization

The 20S Proteasome is a macromolecule responsible for the chemical step in the ubiquitin-proteasome system of degrading unnecessary and unused proteins of the cell. It plays a central role both in the rapid growth of cancer cells as well as in viral infection cycles. Herein, we present a computational study of the acid-base equilibria in an active site of the human proteasome, an aspect which is often neglected despite the crucial role protons play in the catalysis. As example substrates, we take the inhibition by epoxy and boronic acid containing warheads. We have combined cluster quantum mechanical calculations, replica exchange molecular dynamics and Bayesian optimization of non-bonded potential terms in the inhibitors. In relation to the latter, we propose an easily scalable approach to the reevaluation of non-bonded potentials making use of QM/MM dynamics information. Our results show that coupled acid-base equilibria need to be considered when modeling the inhibition mechanism. The coupling between a neighboring lysine and the reacting threonine is not affected by the presence of the inhibitor.

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Autonomous Multi-Cloud Application Deployment and Optimized Management Using Open Source Frameworks

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) ◽

10.1109/acsos-c51401.2020.00072 ◽

2020 ◽

Author(s):

Marta Rozanska ◽

Geir Horn

Keyword(s):

Open Source ◽

Cloud Application ◽

Application Deployment ◽

Multi Cloud

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Verifying Controllers Against Adversarial Examples with Bayesian Optimization

2018 IEEE International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra.2018.8460635 ◽

2018 ◽

Cited By ~ 7

Author(s):

Shromona Ghosh ◽

Felix Berkenkamp ◽

Gireeja Ranade ◽

Shaz Qadeer ◽

Ashish Kapoor

Keyword(s):

Bayesian Optimization ◽

Adversarial Examples

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Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers

The International Journal of Robotics Research ◽

10.1177/02783649211033317 ◽

2021 ◽

pp. 027836492110333

Author(s):

Gilhyun Ryou ◽

Ezra Tal ◽

Sertac Karaman

Keyword(s):

Real World ◽

High Speed ◽

Analytical Approximation ◽

Black Box ◽

Bayesian Optimization ◽

Optimization Framework ◽

Quadrotor Aircraft ◽

Time Optimal ◽

Feasibility Constraints ◽

Generation Problem

We consider the problem of generating a time-optimal quadrotor trajectory for highly maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because the optimal trajectory is located on the boundary of the set of dynamically feasible trajectories. This boundary is hard to model as it involves limitations of the entire system, including complex aerodynamic and electromechanical phenomena, in agile high-speed flight. In this work, we propose a multi-fidelity Bayesian optimization framework that models the feasibility constraints based on analytical approximation, numerical simulation, and real-world flight experiments. By combining evaluations at different fidelities, trajectory time is optimized while the number of costly flight experiments is kept to a minimum. The algorithm is thoroughly evaluated for the trajectory generation problem in two different scenarios: (1) connecting predetermined waypoints; (2) planning in obstacle-rich environments. For each scenario, we conduct both simulation and real-world flight experiments at speeds up to 11 m/s. Resulting trajectories were found to be significantly faster than those obtained through minimum-snap trajectory planning.

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