07401 Abstracts Collection -- Deduction and Decision Procedures

From 01.10. to 05.10.2007, the Dagstuhl Seminar 07401 ``Deduction and Decision Procedures\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper

Context-Bounded Analysis For Concurrent Programs With Dynamic Creation of Threads

Context-bounded analysis has been shown to be both efficient and effective at finding bugs in concurrent programs. According to its original definition, context-bounded analysis explores all behaviors of a concurrent program up to some fixed number of context switches between threads. This definition is inadequate for programs that create threads dynamically because bounding the number of context switches in a computation also bounds the number of threads involved in the computation. In this paper, we propose a more general definition of context-bounded analysis useful for programs with dynamic thread creation. The idea is to bound the number of context switches for each thread instead of bounding the number of switches of all threads. We consider several variants based on this new definition, and we establish decidability and complexity results for the analysis induced by them

Partitioning Strategies for Distributed SMT Solving

For many users of Satisfiability Modulo Theories (SMT) solvers, the solver's performance is the main bottleneck in their application. One promising approach for improving performance is to leverage the increasing availability of parallel and cloud computing. However, despite many efforts, the best parallel approach to date consists of running a portfolio of solvers, meaning that performance is still limited by the best possible sequential performance. In this paper, we revisit divide-and-conquer approaches to parallel SMT, in which a challenging problem is partitioned into several subproblems. We introduce several new partitioning strategies and evaluate their performance, both alone as well as within portfolios, on a large set of difficult SMT benchmarks. We show that hybrid portfolios that include our new strategies can significantly outperform traditional portfolios for parallel SMT.Comment: Submitted to FMCAD 202

LNCS

Systems ought to behave reasonably even in circumstances that are not anticipated in their specifications. We propose a definition of robustness for liveness specifications which prescribes, for any number of environment assumptions that are violated, a minimal number of system guarantees that must still be fulfilled. This notion of robustness can be formulated and realized using a Generalized Reactivity formula. We present an algorithm for synthesizing robust systems from such formulas. For the important special case of Generalized Reactivity formulas of rank 1, our algorithm improves the complexity of [PPS06] for large specifications with a small number of assumptions and guarantees

Ranking function synthesis for bit-vector relations

Abstract. Ranking function synthesis is a key aspect to the success of modern termination provers for imperative programs. While it is wellknown how to generate linear ranking functions for relations over (mathematical) integers or rationals, efficient synthesis of ranking functions for machine-level integers (bit-vectors) is an open problem. This is particularly relevant for the verification of low-level code. We propose several novel algorithms to generate ranking functions for relations over machine integers: a complete method based on a reduction to Presburger arithmetic, and a template-matching approach for predefined classes of ranking functions based on reduction to SAT-and QBF-solving. The utility of our algorithms is demonstrated on examples drawn from Windows device drivers

Drug target optimization in chronic myeloid leukemia using innovative computational platform.

Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. The BioModelAnalyzer (BMA) is a user-friendly computational tool that allows us to do exactly that. We used BMA to build a CML network-model composed of 54 nodes linked by 104 interactions that encapsulates experimental data collected from 160 publications. While previous studies were limited by their focus on a single pathway or cellular process, our executable model allowed us to probe dynamic interactions between multiple pathways and cellular outcomes, suggest new combinatorial therapeutic targets, and highlight previously unexplored sensitivities to Interleukin-3.We would like to thank the members of the Fisher laboratory, in particular to Gavin Smyth and Caroline Dahl for their help with the BMA development, and Alex Hajnal for valuable comments on the manuscript and insightful discussions. Research in BG laboratory is supported by the Medical Research Council, Leukaemia and Lymphoma Research, The Leukemia and Lymphoma Society, Microsoft Research and core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust-MRC Cambridge Stem Cell Institute.This is the final published version. It was originally published in Scientific Reports 5: 8190. DOI: 10.1038/srep08190

Zapato: Automatic theorem proving for

Counterexample-driven abstraction refinement is an automatic process that produces abstract models of finite and infinite-state systems. When this process is applied to software, an automatic theorem prover for quantifier-free first-order logic helps to determine the feasibility of program paths and to refine the abstraction. In this paper we report on a fast, lightweight, and automatic theorem prover called Zapato which we have built specifically to solve the queries produced during the abstraction refinement process

High-fat diets and seizure control in myoclonic-astatic epilepsy: A single center's experience

AbstractPurposeTo determine the efficacy of the Modified Atkins Diet (MAD) and Ketogenic Diet (KD) in seizure control within a population of myoclonic-astatic epilepsy (MAE) patients.MethodsThis was a retrospective, single center study evaluating the seizure control by high fat diets. Seizure diaries kept by the parents performed seizure counts. All patients met the clinical criteria for MAE.ResultsNine patients met the clinical criteria. We found that both the MAD and KD were efficacious in complete seizure control and allowed other medications to be stopped in seven patients. Two patients had greater than 90% seizure control without medications, one on the KD and the other on the MAD. Seizure freedom has ranged from 13 to 36 months, and during this time four patients have been fully weaned off of diet management. One patient was found to have a mutation in SLC2A1.ConclusionOur results suggest that strictly defined MAE patients respond to the MAD with prolonged seizure control. Some patients may require the KD for seizure freedom, suggesting a common pathway of increased requirement for fats. Once controlled, those fully responsive to the Diet(s) could be weaned off traditional seizure medications and in many, subsequently off the MAD or KD

Learning to Verify the Heap

Abstract. We present a data-driven verification framework to automatically prove memory safety and functional correctness of heap programs. For this, we introduce a novel statistical machine learning technique that maps observed program states to (possibly disjunctive) separation logic formulas describing the invariant shape of (possibly nested) data structures at relevant program locations. We then attempt to verify these predictions using a theorem prover, where counterexamples to a predicted invariant are used as additional input to the shape predictor in a refinement loop. After obtaining valid shape invariants, we use a second learning algorithm to strengthen them with data invariants, again employing a refinement loop using the underlying theorem prover. We have implemented our techniques in Cricket, an extension of the GRASShopper verification tool. Cricket is able to automatically prove memory safety and correctness of implementations of a variety of classical heap-manipulating programs such as insertionsort, quicksort and traversals of nested data structures

Ethanol’s Effect on Coq7 Expression in the Hippocampus of Mice

Coenzyme Q (CoQ) is a well-studied molecule, present in every cell membrane in the body, best known for its roles as a mitochondrial electron transporter and a potent membrane anti-oxidant. Much of the previous work was done in vitro in yeast and more recent work has suggested that CoQ may have additional roles prompting calls for a re-assessment of its role using in vivo systems in mammals. Here we investigated the putative role of Coenzyme Q in ethanol-induced effects in vivo using BXD RI mice. We examined hippocampal expression of Coq7 in saline controls and after an acute ethanol treatment, noting enriched biologic processes and pathways following ethanol administration. We also identified 45 ethanol-related phenotypes that were significantly correlated with Coq7 expression, including six phenotypes related to conditioned taste aversion and ethanol preference. This analysis highlights the need for further investigation of Coq7 and related genes in vivo as well as previously unrecognized roles that it may play in the hippocampus