137,904 research outputs found
rbrothers: R Package for Bayesian Multiple Change-Point Recombination Detection.
Phylogenetic recombination detection is a fundamental task in bioinformatics and evolutionary biology. Most of the computational tools developed to attack this important problem are not integrated into the growing suite of R packages for statistical analysis of molecular sequences. Here, we present an R package, rbrothers, that makes a Bayesian multiple change-point model, one of the most sophisticated model-based phylogenetic recombination tools, available to R users. Moreover, we equip the Bayesian change-point model with a set of pre- and post- processing routines that will broaden the application domain of this recombination detection framework. Specifically, we implement an algorithm that forms the set of input trees required by multiple change-point models. We also provide functionality for checking Markov chain Monte Carlo convergence and creating estimation result summaries and graphics. Using rbrothers, we perform a comparative analysis of two Salmonella enterica genes, fimA and fimH, that encode major and adhesive subunits of the type 1 fimbriae, respectively. We believe that rbrothers, available at R-Forge: http://evolmod.r-forge.r-project.org/, will allow researchers to incorporate recombination detection into phylogenetic workflows already implemented in R
Verification of interlocking systems using statistical model checking
In the railway domain, an interlocking is the system ensuring safe train
traffic inside a station by controlling its active elements such as the signals
or points. Modern interlockings are configured using particular data, called
application data, reflecting the track layout and defining the actions that the
interlocking can take. The safety of the train traffic relies thereby on
application data correctness, errors inside them can cause safety issues such
as derailments or collisions. Given the high level of safety required by such a
system, its verification is a critical concern. In addition to the safety, an
interlocking must also ensure that availability properties, stating that no
train would be stopped forever in a station, are satisfied. Most of the
research dealing with this verification relies on model checking. However, due
to the state space explosion problem, this approach does not scale for large
stations. More recently, a discrete event simulation approach limiting the
verification to a set of likely scenarios, was proposed. The simulation enables
the verification of larger stations, but with no proof that all the interesting
scenarios are covered by the simulation. In this paper, we apply an
intermediate statistical model checking approach, offering both the advantages
of model checking and simulation. Even if exhaustiveness is not obtained,
statistical model checking evaluates with a parametrizable confidence the
reliability and the availability of the entire system.Comment: 12 pages, 3 figures, 2 table
Analysing oscillatory trends of discrete-state stochastic processes through HASL statistical model checking
The application of formal methods to the analysis of stochastic oscillators
has been at the focus of several research works in recent times. In this paper
we provide insights on the application of an expressive temporal logic
formalism, namely the Hybrid Automata Stochastic Logic (HASL), to that issue.
We show how one can take advantage of the expressive power of the HASL logic to
define and assess relevant characteristics of (stochastic) oscillators
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
Deep neural networks (DNN) have been shown to be useful in a wide range of
applications. However, they are also known to be vulnerable to adversarial
samples. By transforming a normal sample with some carefully crafted human
imperceptible perturbations, even highly accurate DNN make wrong decisions.
Multiple defense mechanisms have been proposed which aim to hinder the
generation of such adversarial samples. However, a recent work show that most
of them are ineffective. In this work, we propose an alternative approach to
detect adversarial samples at runtime. Our main observation is that adversarial
samples are much more sensitive than normal samples if we impose random
mutations on the DNN. We thus first propose a measure of `sensitivity' and show
empirically that normal samples and adversarial samples have distinguishable
sensitivity. We then integrate statistical hypothesis testing and model
mutation testing to check whether an input sample is likely to be normal or
adversarial at runtime by measuring its sensitivity. We evaluated our approach
on the MNIST and CIFAR10 datasets. The results show that our approach detects
adversarial samples generated by state-of-the-art attacking methods efficiently
and accurately.Comment: Accepted by ICSE 201
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