6,050 research outputs found
The posterior-Viterbi: a new decoding algorithm for hidden Markov models
Background: Hidden Markov models (HMM) are powerful machine learning tools
successfully applied to problems of computational Molecular Biology. In a
predictive task, the HMM is endowed with a decoding algorithm in order to
assign the most probable state path, and in turn the class labeling, to an
unknown sequence. The Viterbi and the posterior decoding algorithms are the
most common. The former is very efficient when one path dominates, while the
latter, even though does not guarantee to preserve the automaton grammar, is
more effective when several concurring paths have similar probabilities. A
third good alternative is 1-best, which was shown to perform equal or better
than Viterbi. Results: In this paper we introduce the posterior-Viterbi (PV) a
new decoding which combines the posterior and Viterbi algorithms. PV is a two
step process: first the posterior probability of each state is computed and
then the best posterior allowed path through the model is evaluated by a
Viterbi algorithm.
Conclusions: We show that PV decoding performs better than other algorithms
first on toy models and then on the computational biological problem of the
prediction of the topology of beta-barrel membrane proteins.Comment: 23 pages, 3 figure
An Efficient Probabilistic Context-Free Parsing Algorithm that Computes Prefix Probabilities
We describe an extension of Earley's parser for stochastic context-free
grammars that computes the following quantities given a stochastic context-free
grammar and an input string: a) probabilities of successive prefixes being
generated by the grammar; b) probabilities of substrings being generated by the
nonterminals, including the entire string being generated by the grammar; c)
most likely (Viterbi) parse of the string; d) posterior expected number of
applications of each grammar production, as required for reestimating rule
probabilities. (a) and (b) are computed incrementally in a single left-to-right
pass over the input. Our algorithm compares favorably to standard bottom-up
parsing methods for SCFGs in that it works efficiently on sparse grammars by
making use of Earley's top-down control structure. It can process any
context-free rule format without conversion to some normal form, and combines
computations for (a) through (d) in a single algorithm. Finally, the algorithm
has simple extensions for processing partially bracketed inputs, and for
finding partial parses and their likelihoods on ungrammatical inputs.Comment: 45 pages. Slightly shortened version to appear in Computational
Linguistics 2
Performance evaluation for ML sequence detection in ISI channels with Gauss Markov Noise
Inter-symbol interference (ISI) channels with data dependent Gauss Markov
noise have been used to model read channels in magnetic recording and other
data storage systems. The Viterbi algorithm can be adapted for performing
maximum likelihood sequence detection in such channels. However, the problem of
finding an analytical upper bound on the bit error rate of the Viterbi detector
in this case has not been fully investigated. Current techniques rely on an
exhaustive enumeration of short error events and determine the BER using a
union bound. In this work, we consider a subset of the class of ISI channels
with data dependent Gauss-Markov noise. We derive an upper bound on the
pairwise error probability (PEP) between the transmitted bit sequence and the
decoded bit sequence that can be expressed as a product of functions depending
on current and previous states in the (incorrect) decoded sequence and the
(correct) transmitted sequence. In general, the PEP is asymmetric. The average
BER over all possible bit sequences is then determined using a pairwise state
diagram. Simulations results which corroborate the analysis of upper bound,
demonstrate that analytic bound on BER is tight in high SNR regime. In the high
SNR regime, our proposed upper bound obviates the need for computationally
expensive simulation.Comment: This paper will appear in GlobeCom 201
Migration energy aware reconfigurations of virtual network function instances in NFV architectures
Network function virtualization (NFV) is a new network architecture framework that implements network functions in software running on a pool of shared commodity servers. NFV can provide the infrastructure flexibility and agility needed to successfully compete in today's evolving communications landscape. Any service is represented by a service function chain (SFC) that is a set of VNFs to be executed according to a given order. The running of VNFs needs the instantiation of VNF instances (VNFIs) that are software modules executed on virtual machines. This paper deals with the migration problem of the VNFIs needed in the low traffic periods to turn OFF servers and consequently to save energy consumption. Though the consolidation allows for energy saving, it has also negative effects as the quality of service degradation or the energy consumption needed for moving the memories associated to the VNFI to be migrated. We focus on cold migration in which virtual machines are redundant and suspended before performing migration. We propose a migration policy that determines when and where to migrate VNFI in response to changes to SFC request intensity. The objective is to minimize the total energy consumption given by the sum of the consolidation and migration energies. We formulate the energy aware VNFI migration problem and after proving that it is NP-hard, we propose a heuristic based on the Viterbi algorithm able to determine the migration policy with low computational complexity. The results obtained by the proposed heuristic show how the introduced policy allows for a reduction of the migration energy and consequently lower total energy consumption with respect to the traditional policies. The energy saving can be on the order of 40% with respect to a policy in which migration is not performed
Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome
The article presents an application of Hidden Markov Models (HMMs) for
pattern recognition on genome sequences. We apply HMM for identifying genes
encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma
brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa
causative agents of sleeping sickness and several diseases in domestic and wild
animals. These parasites have a peculiar strategy to evade the host's immune
system that consists in periodically changing their predominant cellular
surface protein (VSG). The motivation for using patterns recognition methods to
identify these genes, instead of traditional homology based ones, is that the
levels of sequence identity (amino acid and DNA sequence) amongst these genes
is often below of what is considered reliable in these methods. Among pattern
recognition approaches, HMM are particularly suitable to tackle this problem
because they can handle more naturally the determination of gene edges. We
evaluate the performance of the model using different number of states in the
Markov model, as well as several performance metrics. The model is applied
using public genomic data. Our empirical results show that the VSG genes on T.
brucei can be safely identified (high sensitivity and low rate of false
positives) using HMM.Comment: Accepted article in July, 2015 in Pattern Analysis and Applications,
Springer. The article contains 23 pages, 4 figures, 8 tables and 51
reference
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