On Parallel Scalable Uniform SAT Witness Generation

Chakraborty, Supratik; Fremont, Daniel J.; Meel, Kuldeep S.; Seshia, Sanjit A.; Vardi, Moshe Y.

doi:10.1007/978-3-662-46681-0_25

Cited by 72 publications

(100 citation statements)

References 20 publications

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“…The hardness of the counting problem #TNI remains open, whereas the given reduction may be used to show #P-completeness when the co-transmission number is fixed. Our method leverages recent progress in approximate counting and sampling of SATISFIABIL-ITY [31][32][33][34]. We envision that other previously considered counting [35][36][37][38][39] and sampling [31,32,40] problems in computational biology can benefit similarly.…”

Section: Discussionmentioning

confidence: 99%

SharpTNI: Counting and Sampling Parsimonious Transmission Networks under a Weak Bottleneck

Sashittal

El-Kebir

2019

Preprint

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Background: Technological advances in genomic sequencing are facilitating the reconstruction of transmission histories during outbreaks in the fight against infectious diseases. However, accurate disease transmission inference using this data is hindered by a number of challenges due to within-host pathogen diversity and weak transmission bottlenecks, where multiple genetically-distinct pathogenic strains co-transmit. Results: We formulate a combinatorial optimization problem for transmission network inference under a weak bottleneck from a given timed phylogeny and establish hardness results. We present SharpTNI, a method to approximately count and almost uniformly sample from the solution space. Using simulated data, we show that SharpTNI accurately quantifies and uniformly samples from the solution space of parsimonious transmission networks, scaling to large datasets. We demonstrate that SharpTNI identifies co-transmissions during the 2014 Ebola outbreak that are corroborated by epidemiological information collected by previous studies. Conclusions: Accounting for weak transmission bottlenecks is crucial for accurate inference of transmission histories during outbreaks. SharpTNI is a parsimony-based method to reconstruct transmission networks for diseases with long incubation times and large inocula given timed phylogenies. The model and theoretical work of this paper pave the way for novel maximum likelihood methods to co-estimate timed phylogenies and transmission networks under a weak bottleneck.

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Section: Discussionmentioning

confidence: 99%

SharpTNI: Counting and Sampling Parsimonious Transmission Networks under a Weak Bottleneck

Sashittal

El-Kebir

2019

Preprint

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“…On the other hand, our basic strategy allows us to capture some diversity and to explore di↵erent areas in the configuration space. Sophisticated methods for uniform generation of configurations [7] can even be considered, but deserve to scale for our cases in which we have numerous numerical options. Another possible direction is to use expert knowledge to eventually guide the sampling.…”

Section: Discussionmentioning

confidence: 99%

Using machine learning to infer constraints for product lines

Temple

Galindo

Acher

et al. 2016

Proceedings of the 20th International Systems and Software Product Line Conference

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Variability intensive systems may include several thousand features allowing for an enormous number of possible configurations, including wrong ones (e.g. the derived product does not compile). For years, engineers have been using constraints to a priori restrict the space of possible configurations, i.e. to exclude configurations that would violate these constraints. The challenge is to find the set of constraints that would be both precise (allow all correct configurations) and complete (never allow a wrong configuration with respect to some oracle). In this paper, we propose the use of a machine learning approach to infer such product-line constraints from an oracle that is able to assess whether a given product is correct. We propose to randomly generate products from the product line, keeping for each of them its resolution model. Then we classify these products accord-ing to the oracle, and use their resolution models to infer crosstree constraints over the product-line. We validate our approach on a product-line video generator, using a simple computer vision algorithm as an oracle. We show that an interesting set of cross-tree constraint can be generated, with reasonable precision and recall.

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“…Progress in statistical program analysis includes a scalable algorithm for uniform generation of sample from a distribution defined as constraints [43,44], with applications to constrained-random program verification.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Statistical Estimation of Mutual Information for Quantifying Information Flow

Kawamoto

Biondi

Legay

2016

FM 2016: Formal Methods

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Abstract. Analysis of a probabilistic system often requires to learn the joint probability distribution of its random variables. The computation of the exact distribution is usually an exhaustive precise analysis on all executions of the system. To avoid the high computational cost of such an exhaustive search, statistical analysis has been studied to efficiently obtain approximate estimates by analyzing only a small but representative subset of the system's behavior. In this paper we propose a hybrid statistical estimation method that combines precise and statistical analyses to estimate mutual information and its confidence interval. We show how to combine the analyses on different components of the system with different precision to obtain an estimate for the whole system. The new method performs weighted statistical analysis with different sample sizes over different components and dynamically finds their optimal sample sizes. Moreover it can reduce sample sizes by using prior knowledge about systems and a new abstraction-then-sampling technique based on qualitative analysis. We show the new method outperforms the state of the art in quantifying information leakage.

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On Parallel Scalable Uniform SAT Witness Generation

Cited by 72 publications

References 20 publications

SharpTNI: Counting and Sampling Parsimonious Transmission Networks under a Weak Bottleneck

SharpTNI: Counting and Sampling Parsimonious Transmission Networks under a Weak Bottleneck

Using machine learning to infer constraints for product lines

Hybrid Statistical Estimation of Mutual Information for Quantifying Information Flow

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