Constraint Propagation and Explanation over Novel Types by Abstract Compilation

© Graeme Gange and Peter J. Stuckey. The appeal of constraint programming (CP) lies in compositionality - the ability to mix and match constraints as needed. However, this flexibility typically does not extend to the types of variables. Solvers usually support only a small set of pre-defined variable types, and extending this is not typically a simple exercise: not only must the solver engine be updated, but then the library of supported constraints must be re-implemented to support the new type. In this paper, we attempt to ease this second step. We describe a system for automatically deriving a native-code implementation of a global constraint (over novel variable types) from a declarative specification, complete with the ability to explain its propagation, a requirement if we want to make use of modern lazy clause generation CP solvers. We demonstrate this approach by adding support for wrapped-integer variables to chuffed, a lazy clause generation CP solver

Abstract Interpretation, Symbolic Execution and Constraints

Abstract interpretation is a static analysis framework for sound over-approximation of all possible runtime states of a program. Symbolic execution is a framework for reachability analysis which tries to explore all possible execution paths of a program. A shared feature between abstract interpretation and symbolic execution is that each - implicitly or explicitly - maintains constraints during execution, in the form of invariants or path conditions. We investigate the relations between the worlds of abstract interpretation, symbolic execution and constraint solving, to expose potential synergies

Identifying individuals with virologic failure after initiating effective antiretroviral therapy: The surprising value of mean corpuscular hemoglobin in a cross-sectional study

<p>Abstract</p> <p>Objective</p> <p>Recent studies have shown that the current guidelines suggesting immunologic monitoring to determine response to highly active antiretroviral therapy (HAART) are inadequate. We assessed whether routinely collected clinical markers could improve prediction of concurrent HIV RNA levels.</p> <p>Methods</p> <p>We included individuals followed within the Johns Hopkins HIV Clinical Cohort who initiated antiretroviral therapy and had concurrent HIV RNA and biomarker measurements ≥4 months after HAART. A two tiered approach to determine whether clinical markers could improve prediction included: 1) identification of predictors of HIV RNA levels >500 copies/ml and 2) construction and validation of a prediction model.</p> <p>Results</p> <p>Three markers (mean corpuscular hemoglobin [MCH], CD4, and change in percent CD4 from pre-HAART levels) in addition to the change in MCH from pre-HAART levels contained the most predictive information for identifying an HIV RNA >500 copies/ml. However, MCH and change in MCH were the two most predictive followed by CD4 and change in percent CD4. The logistic prediction model in the validation data had an area under the receiver operating characteristic curve of 0.85, and a sensitivity and specificity of 0.74 (95% CI: 0.69-0.79) and 0.89 (95% CI: 0.86-0.91), respectively.</p> <p>Conclusions</p> <p>Immunologic criteria have been shown to be a poor guideline for identifying individuals with high HIV RNA levels. MCH and change in MCH were the strongest predictors of HIV RNA levels >500. When combined with CD4 and percent CD4 as covariates in a model, a high level of discrimination between those with and without HIV RNA levels >500 was obtained. These data suggest an unexplored relationship between HIV RNA and MCH.</p

An Instrumental Variable Evaluation of Antidepressant Use on Employment Among HIV-Infected Women Using Highly-Active Antiretroviral Therapy in the United States: 1996-2004

This paper examines the effect of antidepressant use on the likelihood of being employed among HIV-positive women receiving highly active antiretroviral therapy (HAART) in the United States from 1994 to 2004. We use instrumental variables to predict antidepressant use independently of outcomes; thus, addressing potential sources of bias -- more depressed women are more likely to receive antidepressant treatment, but they are also more likely to be unemployed. The results show that antidepressant use has a positive effect on the employment probability of women living with HIV. The proposed instrumental variables can be used to identify antidepressant use in the WIHS population. Among women receiving HAART, and controlling for individual and local area labor market characteristics, the use of antidepressants is associated with a higher probability of being employed.

A Tool for Intersecting Context-Free Grammars and Its Applications

This paper describes a tool for intersecting context-free grammars. Since this problem is undecidable the tool follows a refinement-based approach and implements a novel refinement which is complete for regularly separable grammars. We show its effectiveness for safety verification of recursive multi-threaded programs