2,124 research outputs found
Construction of Almost Disjunct Matrices for Group Testing
In a \emph{group testing} scheme, a set of tests is designed to identify a
small number of defective items among a large set (of size ) of items.
In the non-adaptive scenario the set of tests has to be designed in one-shot.
In this setting, designing a testing scheme is equivalent to the construction
of a \emph{disjunct matrix}, an matrix where the union of supports
of any columns does not contain the support of any other column. In
principle, one wants to have such a matrix with minimum possible number of
rows (tests). One of the main ways of constructing disjunct matrices relies on
\emph{constant weight error-correcting codes} and their \emph{minimum
distance}. In this paper, we consider a relaxed definition of a disjunct matrix
known as \emph{almost disjunct matrix}. This concept is also studied under the
name of \emph{weakly separated design} in the literature. The relaxed
definition allows one to come up with group testing schemes where a
close-to-one fraction of all possible sets of defective items are identifiable.
Our main contribution is twofold. First, we go beyond the minimum distance
analysis and connect the \emph{average distance} of a constant weight code to
the parameters of an almost disjunct matrix constructed from it. Our second
contribution is to explicitly construct almost disjunct matrices based on our
average distance analysis, that have much smaller number of rows than any
previous explicit construction of disjunct matrices. The parameters of our
construction can be varied to cover a large range of relations for and .Comment: 15 Page
Derandomization and Group Testing
The rapid development of derandomization theory, which is a fundamental area
in theoretical computer science, has recently led to many surprising
applications outside its initial intention. We will review some recent such
developments related to combinatorial group testing. In its most basic setting,
the aim of group testing is to identify a set of "positive" individuals in a
population of items by taking groups of items and asking whether there is a
positive in each group.
In particular, we will discuss explicit constructions of optimal or
nearly-optimal group testing schemes using "randomness-conducting" functions.
Among such developments are constructions of error-correcting group testing
schemes using randomness extractors and condensers, as well as threshold group
testing schemes from lossless condensers.Comment: Invited Paper in Proceedings of 48th Annual Allerton Conference on
Communication, Control, and Computing, 201
Superselectors: Efficient Constructions and Applications
We introduce a new combinatorial structure: the superselector. We show that
superselectors subsume several important combinatorial structures used in the
past few years to solve problems in group testing, compressed sensing,
multi-channel conflict resolution and data security. We prove close upper and
lower bounds on the size of superselectors and we provide efficient algorithms
for their constructions. Albeit our bounds are very general, when they are
instantiated on the combinatorial structures that are particular cases of
superselectors (e.g., (p,k,n)-selectors, (d,\ell)-list-disjunct matrices,
MUT_k(r)-families, FUT(k, a)-families, etc.) they match the best known bounds
in terms of size of the structures (the relevant parameter in the
applications). For appropriate values of parameters, our results also provide
the first efficient deterministic algorithms for the construction of such
structures
Improved Combinatorial Group Testing Algorithms for Real-World Problem Sizes
We study practically efficient methods for performing combinatorial group
testing. We present efficient non-adaptive and two-stage combinatorial group
testing algorithms, which identify the at most d items out of a given set of n
items that are defective, using fewer tests for all practical set sizes. For
example, our two-stage algorithm matches the information theoretic lower bound
for the number of tests in a combinatorial group testing regimen.Comment: 18 pages; an abbreviated version of this paper is to appear at the
9th Worksh. Algorithms and Data Structure
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