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This paper studies a security problem for a class cloud-connected multi-agent systems, where autonomous agents coordinate via a combination of short-range ad-hoc communication links and long-range cloud services. We consider a simplified model for the dynamics of a cloud-connected multi-agent system and attacks, where the states evolve according to linear time-invariant impulsive dynamics, and attacks are modeled as exogenous inputs designed by an omniscent attacker that alters the continuous and impulsive updates. We propose a definition of attack detectability, characterize the existence of stealthy attacks as a function of the system parameters and attack properties, and design a family of undetectable attacks. We illustrate our results on a cloud-based surveillance example.
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Improving RNA Branching Predictions: Advances and Limitations
Minimum free energy prediction of RNA secondary structures is based on the Nearest Neighbor Thermodynamics Model. While such predictions are typically good, the accuracy can vary widely even for short sequences, and the branching thermodynamics are an important factor in this variance. Recently, the simplest model for multiloop energetics—a linear function of the number of branches and unpaired nucleotides—was found to be the best. Subsequently, a parametric analysis demonstrated that per family accuracy can be improved by changing the weightings in this linear function. However, the extent of improvement was not known due to the ad hoc method used to find the new parameters. Here we develop a branch-and-bound algorithm that finds the set of optimal parameters with the highest average accuracy for a given set of sequences. Our analysis shows that the previous ad hoc parameters are nearly optimal for tRNA and 5S rRNA sequences on both training and testing sets. Moreover, cross-family improvement is possible but more difficult because competing parameter regions favor different families. The results also indicate that restricting the unpaired nucleotide penalty to small values is warranted. This reduction makes analyzing longer sequences using the present techniques more feasible.
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- PAR ID:
- 10292289
- Date Published:
- Journal Name:
- Genes
- Volume:
- 12
- Issue:
- 4
- ISSN:
- 2073-4425
- Page Range / eLocation ID:
- 469
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/genes12040469
