28 research outputs found
On Critical Relative Distance of DNA Codes for Additive Stem Similarity
We consider DNA codes based on the nearest-neighbor (stem) similarity model
which adequately reflects the "hybridization potential" of two DNA sequences.
Our aim is to present a survey of bounds on the rate of DNA codes with respect
to a thermodynamically motivated similarity measure called an additive stem
similarity. These results yield a method to analyze and compare known samples
of the nearest neighbor "thermodynamic weights" associated to stacked pairs
that occurred in DNA secondary structures.Comment: 5 or 6 pages (compiler-dependable), 0 figures, submitted to 2010 IEEE
International Symposium on Information Theory (ISIT 2010), uses IEEEtran.cl
Group testing with Random Pools: Phase Transitions and Optimal Strategy
The problem of Group Testing is to identify defective items out of a set of
objects by means of pool queries of the form "Does the pool contain at least a
defective?". The aim is of course to perform detection with the fewest possible
queries, a problem which has relevant practical applications in different
fields including molecular biology and computer science. Here we study GT in
the probabilistic setting focusing on the regime of small defective probability
and large number of objects, and . We construct and
analyze one-stage algorithms for which we establish the occurrence of a
non-detection/detection phase transition resulting in a sharp threshold, , for the number of tests. By optimizing the pool design we construct
algorithms whose detection threshold follows the optimal scaling . Then we consider two-stages algorithms and analyze their
performance for different choices of the first stage pools. In particular, via
a proper random choice of the pools, we construct algorithms which attain the
optimal value (previously determined in Ref. [16]) for the mean number of tests
required for complete detection. We finally discuss the optimal pool design in
the case of finite
Improved Adaptive Group Testing Algorithms with Applications to Multiple Access Channels and Dead Sensor Diagnosis
We study group-testing algorithms for resolving broadcast conflicts on a
multiple access channel (MAC) and for identifying the dead sensors in a mobile
ad hoc wireless network. In group-testing algorithms, we are asked to identify
all the defective items in a set of items when we can test arbitrary subsets of
items. In the standard group-testing problem, the result of a test is
binary--the tested subset either contains defective items or not. In the more
generalized versions we study in this paper, the result of each test is
non-binary. For example, it may indicate whether the number of defective items
contained in the tested subset is zero, one, or at least two. We give adaptive
algorithms that are provably more efficient than previous group testing
algorithms. We also show how our algorithms can be applied to solve conflict
resolution on a MAC and dead sensor diagnosis. Dead sensor diagnosis poses an
interesting challenge compared to MAC resolution, because dead sensors are not
locally detectable, nor are they themselves active participants.Comment: Expanded version of a paper appearing in ACM Symposium on Parallelism
in Algorithms and Architectures (SPAA), and preliminary version of paper
appearing in Journal of Combinatorial Optimizatio
Lecturas cristianas
We consider DNA codes based on the concept of a weighted 2-stem similarity measure which reflects the ”hybridization potential” of two DNA sequences. A random coding bound on the rate of DNA codes with respect to a thermodynamic motivated similarity measure is proved. Ensembles of DNA strands whose sequence composition is restricted in a manner similar to the restrictions in binary Fibonacci sequences are introduced to obtain the bound