SAFFRON: A Fast, Efficient, and Robust Framework for Group Testing Based on Sparse-Graph Codes

Lee, Kangwook; Chandrasekher, Kabir Aladin; Pedarsani, Ramtin; Ramchandran, Kannan

doi:10.1109/tsp.2019.2929938

Cited by 43 publications

(88 citation statements)

References 48 publications

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“…Second, we would like to explore general (as opposed to opportunistic group testing) group testing [38,46] to identify problematic data elements when a dataset has been identified as a root cause.…”

Section: Discussionmentioning

confidence: 99%

BugDoc

Lourenço

Freire

Simon³

et al. 2022

The VLDB Journal

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Applications in domains ranging from large-scale simulations in astrophysics and biology to enterprise analytics rely on computational pipelines. A pipeline consists of modules and their associated parameters, data inputs, and outputs, which are orchestrated to produce a set of results. If some modules derive unexpected outputs, the pipeline can crash or lead to incorrect results. Debugging these pipelines is difficult since there are many potential sources of errors including: bugs in the code, input data, software updates, and improper parameter settings. We present BugDoc, a system that automatically infers the root causes and derive succinct explanations of failures for black-box pipelines. BugDoc does so by using provenance from previous runs of a given pipeline to derive hypotheses for the errors, and then iteratively runs new pipeline configurations to test these hypotheses. Besides identifying issues associated with computational modules in a pipeline, we also propose methods for: "opportunistic group testing" to identify portions of data inputs that might be responsible for failed executions (what we call ), helping users narrow down the cause of failure; and "selective instrumentation" to determine nodes in pipelines that should be instrumented to improve efficiency and reduce the number of iterations to test. Through a case study of deployed workflows at a software company and an experimental evaluation using synthetic pipelines, we assess the effectiveness of BugDoc and show that it requires fewer iterations to derive root causes and/or achieves higher quality results than previous approaches.

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Section: Discussionmentioning

confidence: 99%

BugDoc

Lourenço

Freire

Simon³

et al. 2022

The VLDB Journal

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“…Second, ETI saff is achieved by an idea inspired by the SAFFRON scheme of [12] (see also [1]). Suppose for the moment that we have T = 2 log 2 n/k tests.…”

Section: Results For the Theoretical Modelmentioning

confidence: 99%

Pooled testing to isolate infected individuals

Aldridge

2021

2021 55th Annual Conference on Information Sciences and Systems (CISS)

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The usual problem for group testing is this: For a given number of individuals and a given prevalence, how many tests T * are required to find every infected individual? In real life, however, the problem is usually different: For a given number of individuals, a given prevalence, and a limited number of tests T much smaller than T * , how can these tests best be used?In this conference paper, we outline some recent results on this problem for two models. First, the 'practical' model, which is relevant for screening for COVID-19 and has tests that are highly specific but imperfectly sensitive, shows that simple algorithms can be outperformed at low prevalence and high sensitivity. Second, the 'theoretical' model of very low prevalence with perfect tests gives interesting new mathematical results.

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“…For QGT, the only work that consider noisy observations is by Bshouty and Mazzawi [17] where they assume that the results of certain tests can be erroneous/erased. For boolean group testing, several non-adaptive [18,19,20] and adaptive [21,22,23] algorithms have been proposed for noisy group testing. However, these algorithms rely on the knowl-edge of the underlying noise model.…”

Section: Introductionmentioning

confidence: 99%

An Order-Optimal Adaptive Test Plan for Noisy Group Testing Under Unknown Noise Models

Salgia

Zhao

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We consider the problem of noisy group testing where the test results are corrupted by noise with an unknown distribution. We propose an adaptive test plan consisting of a hierarchy of biased random walks guided by a local sequential test which together lend adaptivity and agnosticism to the unknown noise model. We show that the proposed test plan is order optimal in both the population size and the error rate.

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SAFFRON: A Fast, Efficient, and Robust Framework for Group Testing Based on Sparse-Graph Codes

Cited by 43 publications

References 48 publications

BugDoc

BugDoc

Pooled testing to isolate infected individuals

An Order-Optimal Adaptive Test Plan for Noisy Group Testing Under Unknown Noise Models

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