Tracing a Single User - PowerPoint PPT Presentation

1 / 21
About This Presentation
Title:

Tracing a Single User

Description:

Instead, one can test pools that contain blood from a set of samples. ... We can use a method similar to that of the blood testing problem. Molecular Biology (cont. ... – PowerPoint PPT presentation

Number of Views:13
Avg rating:3.0/5.0
Slides: 22
Provided by: ver54
Category:
Tags: blood | single | tests | tracing | user

less

Transcript and Presenter's Notes

Title: Tracing a Single User


1
Tracing a Single User
  • Joint work with Noga Alon

2
Group Testing
  • Dorfman raised the following problem in 1941
  • All American inductees gave blood samples, that
    were tested for the presence of a syphilitic
    antigen.
  • We assume that the number of infected blood
    samples r is much smaller than the total number
    m.
  • Testing each sample separately requires m tests.

3
Group Testing (cont.)
  • Instead, one can test pools that contain blood
    from a set of samples.
  • If the outcome is negative none of the samples
    in the pool is infected.
  • Otherwise, the pool contains at least one
    infected sample, which can be determined by
    further tests.
  • This way, less than m tests are needed.

4
Molecular Biology
  • In recent years this problem has gained
    popularity again in the field of molecular
    biology.
  • For example, when we are given a large set of DNA
    sequences, and we look for all those that contain
    a specific short subsequence.
  • We can use a method similar to that of the blood
    testing problem.

5
Molecular Biology (cont.)
  • In some applications, we are interested in
    finding one sequence that contains the short
    subsequence, rather than all of them.

6
Parallelization
  • Often, we would prefer to conduct all experiments
    simultaneously, even at the cost of increasing
    the number of experiments.
  • Thus, we need our tests to be non-adaptive, i.e.
    the pool tested in each experiment is independent
    of the outcomes of other experiments.

7
Non-Adaptive Tests
a1 a2 . . . . am
T1 0 1 1 1 1 0 0
T2 1 1 1 1 0 0 1
. 0 1 0 0 1 1 0
. 1 0 1 0 0 0 0
. 1 0 0 1 1 0 1
Tn 0 0 1 0 0 1 1
1
1
1
0
1
0
8
r-SUT Definition
  • Definition Let F be a family of subsets ofn
    1,,n. F is called r-single-user-tracing
    superimposed (r-SUT) if ?F1,,Fk?F with Fi?r,
  • In other words, given the union of up to r sets
    from F, one can identify at least one of those
    sets.

9
Communication
  • Suppose that m users share a common channel.
  • Each user is associated with a vector in 0,1n.
  • All active users transmit their vectors, and a
    single receiver gets the OR of all transmitted
    vectors.
  • Given that at most r users are active
    simultaneously, we would like the receiver to be
    able to identify at least one of them.

10
Maximal r-SUT Families
  • Let g(n,r) denote the maximum size of anr-SUT
    family of subsets of n.
  • Let Rg(r) lim sup n?? log g(n,r) / n.
  • Csurös and Ruszinkó There exist constants
    c1,c2gt0 s.t.
  • .
  • Our result Rg(r) ??(1/r) (and hence ?(1/r)).

11
Lower Bound
  • Let m 2n/(20r).
  • We construct a family FF1,,Fm of subsets of
    n at random as follows
  • ? 1 i m and 1 j n independently, put j in
    Fi with probability 1/r.

12
Lower Bound (cont.)
  • We show that F is r-SUT with positive
    probability.
  • We say a configuration of F1,,Fk?F with Fi?r
    and is bad if all the unions
    are equal.
  • We show that with positive probability there are
    no bad configurations.

13
Lower Bound (cont.)
  • We show that with probability gt ½ no small
    configuration is bad, and that with probability gt
    ½ no large configuration is bad.
  • Therefore, with positive probability there is no
    bad configuration.

14
Small Configurations
  • Proposition With probability gt ½ the following
    holds?slt2r and distinct A1,,As?F, ?j?n that
    belongs to exactly one of the sets A1,,As.
  • Corollary With probability gt ½ no small
    configuration is bad.

15
Small Configurations (cont.)
A5
A7
A2
A1
A8
A4
A9
A3
A6
16
Large Configurations
  • Proposition With probability gt ½ the following
    holds. For all distinct A1,,Ar,B1,,Br?F,
  • Corollary With probability gt ½ no large
    configuration is bad.

17
Large Configurations (cont.)
B3
B2
B1
B1
Ai
B3
A1
A3
B2
A2
18
Tracing Multiple Users
  • Recently, Laczay and Ruszinkó have introduced the
    following generalization of r-SUT families.
  • For integers n, r?2, and 1?k?r, a family F of
    subsets of n is called k-out-of-r
    multiple-user-tracing superimposed (MUTk(r)) if
    given the union of any l?r sets from F, one can
    identify at least min(k,l) of them.

19
Tracing Multiple Users (cont.)
  • Let h(n,r,k) denote the maximum size of aMUTk(r)
    family of subsets of n.
  • Let Rh(r,k) lim sup n?? log h(n,r,k) / n.
  • We have shown that there are constants
    c1,c2,c3,c4gt0 s.t.
    .

20
Open Problems
  • We have shown that Rg(r) ?(1/r), but the
    question of finding the exact constant is still
    open.
  • This problem is open even for the case of r 2.
  • 1/3 ? Rg(2) ? 1/2o(1).

Follows from a result of Coppersmith and Shearer
By a careful analysis of the random construction
21
Open Problems (cont.)
  • We show how to construct an r-SUT family in time
    mO(r), where m is the size of the family.It
    would be interesting to find explicit
    constructions for all r.
  • There are other related problems for which there
    are still gaps between lower and upper bounds
  • Multiple-user tracing families
  • r-superimposed families
  • Disjointly r-superimposed families
  • Graph identifying codes
Write a Comment
User Comments (0)
About PowerShow.com