5 research outputs found
A framework for generalized group testing with inhibitors and its potential application in neuroscience
The main goal of group testing with inhibitors (GTI) is to efficiently
identify a small number of defective items and inhibitor items in a large set
of items. A test on a subset of items is positive if the subset satisfies some
specific properties. Inhibitor items cancel the effects of defective items,
which often make the outcome of a test containing defective items negative.
Different GTI models can be formulated by considering how specific properties
have different cancellation effects. This work introduces generalized GTI
(GGTI) in which a new type of items is added, i.e., hybrid items. A hybrid item
plays the roles of both defectives items and inhibitor items. Since the number
of instances of GGTI is large (more than 7 million), we introduce a framework
for classifying all types of items non-adaptively, i.e., all tests are designed
in advance. We then explain how GGTI can be used to classify neurons in
neuroscience. Finally, we show how to realize our proposed scheme in practice
Code Construction and Decoding Algorithms for Semi-Quantitative Group Testing with Nonuniform Thresholds
We analyze a new group testing scheme, termed semi-quantitative group
testing, which may be viewed as a concatenation of an adder channel and a
discrete quantizer. Our focus is on non-uniform quantizers with arbitrary
thresholds. For the most general semi-quantitative group testing model, we
define three new families of sequences capturing the constraints on the code
design imposed by the choice of the thresholds. The sequences represent
extensions and generalizations of Bh and certain types of super-increasing and
lexicographically ordered sequences, and they lead to code structures amenable
for efficient recursive decoding. We describe the decoding methods and provide
an accompanying computational complexity and performance analysis