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Register a new userEfficiently Decodable Non-Adaptive Threshold Group Testing
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Thach V. Bui
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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We consider non-adaptive threshold group testing for identification of up to $d$ defective items in a set of $n$ items, where a test is positive if it contains at least $2 leq u leq d$ defective items, and negative otherwise. The defective items can be identified using $t = O left( left( frac{d}{u} right)^u left( frac{d}{d - u} right)^{d-u} left(u log{frac{d}{u}} + log{frac{1}{epsilon}} right) cdot d^2 log{n} right)$ tests with probability at least $1 - epsilon$ for any $epsilon > 0$ or $t = O left( left( frac{d}{u} right)^u left( frac{d}{d -u} right)^{d - u} d^3 log{n} cdot log{frac{n}{d}} right)$ tests with probability 1. The decoding time is $t times mathrm{poly}(d^2 log{n})$. This result significantly improves the best known results for decoding non-adaptive threshold group testing: $O(nlog{n} + n log{frac{1}{epsilon}})$ for probabilistic decoding, where $epsilon > 0$, and $O(n^u log{n})$ for deterministic decoding.
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The goal of threshold group testing is to identify up to $d$ defective items among a population of $n$ items, where $d$ is usually much smaller than $n$. A test is positive if it has at least $u$ defective items and negative otherwise. Our objective is to identify defective items in sublinear time the number of items, e.g., $mathrm{poly}(d, ln{n}),$ by using the number of tests as low as possible. In this paper, we reduce the number of tests to $O left( h times frac{d^2 ln^2{n}}{mathsf{W}^2(d ln{n})} right)$ and the decoding time to $O left( mathrm{dec}_0 times h right),$ where $mathrm{dec}_0 = O left( frac{d^{3.57} ln^{6.26}{n}}{mathsf{W}^{6.26}(d ln{n})} right) + O left( frac{d^6 ln^4{n}}{mathsf{W}^4(d ln{n})} right)$, $h = Oleft( frac{d_0^2 ln{frac{n}{d_0}}}{(1-p)^2} right)$ , $d_0 = max{u, d - u }$, $p in [0, 1),$ and $mathsf{W}(x) = Theta left( ln{x} - ln{ln{x}} right).$ If the number of tests is increased to $Oleft( h times frac{d^2ln^3{n}}{mathsf{W}^2(d ln{n})} right),$ the decoding complexity is reduced to $O left(mathrm{dec}_1 times h right),$ where $mathrm{dec}_1 = max left{ frac{d^2 ln^3{n}}{mathsf{W}^2(d ln{n})}, frac{ud ln^4{n}}{mathsf{W}^3(d ln{n})} right}.$ Moreover, our proposed scheme is capable of handling errors in test outcomes.
We consider an efficiently decodable non-adaptive group testing (NAGT) problem that meets theoretical bounds. The problem is to find a few specific items (at most $d$) satisfying certain characteristics in a colossal number of $N$ items as quickly as possible. Those $d$ specific items are called textit{defective items}. The idea of NAGT is to pool a group of items, which is called textit{a test}, then run a test on them. If the test outcome is textit{positive}, there exists at least one defective item in the test, and if it is textit{negative}, there exists no defective items. Formally, a binary $t times N$ measurement matrix $mathcal{M} = (m_{ij})$ is the representation for $t$ tests where row $i$ stands for test $i$ and $m_{ij} = 1$ if and only if item $j$ belongs to test $i$. There are three main objectives in NAGT: minimize the number of tests $t$, construct matrix $mathcal{M}$, and identify defective items as quickly as possible. In this paper, we present a strongly explicit construction of $mathcal{M}$ for when the number of defective items is at most 2, with the number of tests $t simeq 16 log{N} = O(log{N})$. In particular, we need only $K simeq N times 16log{N} = O(Nlog{N})$ bits to construct such matrices, which is optimal. Furthermore, given these $K$ bits, any entry in the matrix can be constructed in time $O left(ln{N}/ ln{ln{N}} right)$. Moreover, $mathcal{M}$ can be decoded with high probability in time $Oleft( frac{ln^2{N}}{ln^2{ln{N}}} right)$. When the number of defective items is greater than 2, we present a scheme that can identify at least $(1-epsilon)d$ defective items with $t simeq 32 C(epsilon) d log{N} = O(d log{N})$ in time $O left( d frac{ln^2{N}}{ln^2{ln{N}}} right)$ for any close-to-zero $epsilon$, where $C(epsilon)$ is a constant that depends only on $epsilon$.
The basic goal of threshold group testing is to identify up to $d$ defective items among a population of $n$ items, where $d$ is usually much smaller than $n$. The outcome of a test on a subset of items is positive if the subset has at least $u$ defective items, negative if it has up to $ell$ defective items, where $0leqell<u$, and arbitrary otherwise. This is called threshold group testing. The parameter $g=u-ell-1$ is called textit{the gap}. In this paper, we focus on the case $g>0$, i.e., threshold group testing with a gap. Note that the results presented here are also applicable to the case $g = 0$; however, the results are not as efficient as those in related work. Currently, a few reported studies have investigated test designs and decoding algorithms for identifying defective items. Most of the previous studies have not been feasible because there are numerous constraints on their problem settings or the decoding complexities of their proposed schemes are relatively large. Therefore, it is compulsory to reduce the number of tests as well as the decoding complexity, i.e., the time for identifying the defective items, for achieving practical schemes. The work presented here makes five contributions. The first is a more accurate theorem for a non-adaptive algorithm for threshold group testing proposed by Chen and Fu. The second is an improvement in the construction of disjunct matrices, which are the main tools for tackling (threshold) group testing and other tasks such as constructing cover-free families or learning hidden graphs. The third and fourth contributions are a reduced exact upper bound on the number of tests and a reduced asymptotic bound on the decoding time for identifying defective items in a noisy setting on test outcomes. The fifth contribution is a simulation on the number of tests of the resulting improvements for previous work and the proposed theorems.
We will discuss superimposed codes and non-adaptive group testing designs arising from the potentialities of compressed genotyping models in molecular biology. The given paper was motivated by the 30th anniversary of Dyachkov-Rykov recurrent upper bound on the rate of superimposed codes published in 1982. We were also inspired by recent results obtained for non-adaptive threshold group testing which develop the theory of superimposed codes
In this paper, we consider the problem of noiseless non-adaptive probabilistic group testing, in which the goal is high-probability recovery of the defective set. We show that in the case of $n$ items among which $k$ are defective, the smallest possible number of tests equals $min{ C_{k,n} k log n, n}$ up to lower-order asymptotic terms, where $C_{k,n}$ is a uniformly bounded constant (varying depending on the scaling of $k$ with respect to $n$) with a simple explicit expression. The algorithmic upper bound follows from a minor adaptation of an existing analysis of the Definite Defectives (DD) algorithm, and the algorithm-independent lower bound builds on existing works for the regimes $k le n^{1-Omega(1)}$ and $k = Theta(n)$. In sufficiently sparse regimes (including $k = obig( frac{n}{log n} big)$), our main result generalizes that of Coja-Oghlan {em et al.} (2020) by avoiding the assumption $k le n^{1-Omega(1)}$, whereas in sufficiently dense regimes (including $k = omegabig( frac{n}{log n} big)$), our main result shows that individual testing is asymptotically optimal for any non-zero target success probability, thus strengthening an existing result of Aldridge (2019) in terms of both the error probability and the assumed scaling of $k$.
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