18 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
Efficiently Decodable Non-Adaptive Threshold Group Testing
We consider non-adaptive threshold group testing for identification of up to
defective items in a set of items, where a test is positive if it
contains at least defective items, and negative otherwise.
The defective items can be identified using tests with
probability at least for any or tests with probability 1. The decoding time is
. This result significantly improves the
best known results for decoding non-adaptive threshold group testing:
for probabilistic decoding, where
, and for deterministic decoding
Group testing:an information theory perspective
The group testing problem concerns discovering a small number of defective
items within a large population by performing tests on pools of items. A test
is positive if the pool contains at least one defective, and negative if it
contains no defectives. This is a sparse inference problem with a combinatorial
flavour, with applications in medical testing, biology, telecommunications,
information technology, data science, and more. In this monograph, we survey
recent developments in the group testing problem from an information-theoretic
perspective. We cover several related developments: efficient algorithms with
practical storage and computation requirements, achievability bounds for
optimal decoding methods, and algorithm-independent converse bounds. We assess
the theoretical guarantees not only in terms of scaling laws, but also in terms
of the constant factors, leading to the notion of the {\em rate} of group
testing, indicating the amount of information learned per test. Considering
both noiseless and noisy settings, we identify several regimes where existing
algorithms are provably optimal or near-optimal, as well as regimes where there
remains greater potential for improvement. In addition, we survey results
concerning a number of variations on the standard group testing problem,
including partial recovery criteria, adaptive algorithms with a limited number
of stages, constrained test designs, and sublinear-time algorithms.Comment: Survey paper, 140 pages, 19 figures. To be published in Foundations
and Trends in Communications and Information Theor
Review : Deep learning in electron microscopy
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy