Word-level Symbolic Trajectory Evaluation

Symbolic trajectory evaluation (STE) is a model checking technique that has been successfully used to verify industrial designs. Existing implementations of STE, however, reason at the level of bits, allowing signals to take values in {0, 1, X}. This limits the amount of abstraction that can be achieved, and presents inherent limitations to scaling. The main contribution of this paper is to show how much more abstract lattices can be derived automatically from RTL descriptions, and how a model checker for the general theory of STE instantiated with such abstract lattices can be implemented in practice. This gives us the first practical word-level STE engine, called STEWord. Experiments on a set of designs similar to those used in industry show that STEWord scales better than word-level BMC and also bit-level STE.Comment: 19 pages, 3 figures, 2 tables, full version of paper in International Conference on Computer-Aided Verification (CAV) 201

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Formal hardware verification of digital circuits

The use of formal methods to verify the correctness of digital circuits is less constrained by the growing complexity of digital circuits than conventional methods based on exhaustive simulation. This paper briefly outlines three main approaches to formal hardware verification: symbolic simulation, state machine analysis, and theorem-proving

Sensitive Detection and Early Prognostic Significance of p24 Antigen in Heat-Denatured Plasma of Human Immunodeficiency Virus Type 1-Infected Infants

Immune complex formation causes underdetection of p24 antigen in human immunodeficiencyvirus(HIV)infection.Brieflyboilingdilutedplasma releasesallcomplexedantigen, which can then be measured by some commercial assays. In a retrospective pediatric cohort study, the specificity of this procedure in 390 uninfected samples was 96.9% after initial testing and 100% after neutralization. Sensitivity among 125 postnatal infected samples was, at a detection of 2 pg/ml., 96.0% (97% neutralizable) compared with 47.7% for regular antigen (76% neutralizable), 96% for polymerase chain reaction, and 77% for viral culture. The high sensitivity and specificity of heat-denatured antigen was confirmed by prospectively testing 113 additional samples.Quantitativeanalysisofsamplesfrominfectedinfants showedlowlevelsofp24 antigen in 29% of cord blood sera, a postnatal increase to levels that were during the first 6 months of life inversely associated with survival, and persistence of antigenemia thereafter independent of clinical status. Prevalence and antigen levels were significantly lower in mothers. The persistent antigenemia in children indicates that their immune systems cannot restrict HIV expression as efficiently as those of adult

A variational approach to the stochastic aspects of cellular signal transduction

Cellular signaling networks have evolved to cope with intrinsic fluctuations, coming from the small numbers of constituents, and the environmental noise. Stochastic chemical kinetics equations govern the way biochemical networks process noisy signals. The essential difficulty associated with the master equation approach to solving the stochastic chemical kinetics problem is the enormous number of ordinary differential equations involved. In this work, we show how to achieve tremendous reduction in the dimensionality of specific reaction cascade dynamics by solving variationally an equivalent quantum field theoretic formulation of stochastic chemical kinetics. The present formulation avoids cumbersome commutator computations in the derivation of evolution equations, making more transparent the physical significance of the variational method. We propose novel time-dependent basis functions which work well over a wide range of rate parameters. We apply the new basis functions to describe stochastic signaling in several enzymatic cascades and compare the results so obtained with those from alternative solution techniques. The variational ansatz gives probability distributions that agree well with the exact ones, even when fluctuations are large and discreteness and nonlinearity are important. A numerical implementation of our technique is many orders of magnitude more efficient computationally compared with the traditional Monte Carlo simulation algorithms or the Langevin simulations.Comment: 15 pages, 11 figure