Model counting is the task of computing the number of assignments to variables V that satisfy a given propositional theory F . The model counting problem is denoted as #SAT. Model counting is an essential tool in probabilistic reasoning. In this paper, we introduce the problem of model counting projected on a subset of original variables that we call priority variables P ⊆ V. The task is to com...

Bounded Model Checking based on SAT methods has recently been introduced as a complementary technique to BDD-based Symbolic Model Checking. The basic idea is to search for a counter example in executions whose length is bounded by some integer k. The BMC problem can be e ciently reduced to a propositional satis ability problem, and can therefore be solved by SAT methods rather than BDDs. SAT pr...

We investigate the concept of speaker adaptive training (SAT) in the context of deep neural network (DNN) acoustic models. Previous studies have shown success of performing speaker adaptation for DNNs in speech recognition. In this paper, we apply SAT to DNNs by learning two types of feature mapping neural networks. Given an initial DNN model, these networks take speaker i-vectors as additional...

Bounded Model Checking based on SAT methods has recently been introduced as a complementary technique to BDD-based Symbolic Model Checking. The basic idea is to search for a counter example in executions whose length is bounded by some integer k. The BMC problem can be eeciently reduced to a propositional satissabil-ity problem, and can therefore be solved by SAT methods rather than BDDs. SAT p...

Bounded Model Checking (BMC) is one of the most paradigmatic practical applications of Boolean Satisfiability (SAT). The utilization of SAT in model checking has allowed significant performance gains and, as a consequence, a large number of commercial verification tools now include SAT-based model checkers. Recent work has provided SAT-based BMC with completeness conditions, and this is general...

Corroborating a prediction from statistical physics, we prove that the belief propagation message passing algorithm approximates partition function of random k-SAT model well for all clause/variable densities and inverse temperatures which modest absence long-range correlations condition is satisfied. This known as “replica symmetry” in physics language. From this result deduce replica symmetry...

The success of portfolio approaches in SAT solving relies on the observation that different SAT solving techniques perform better on different SAT instances. The Algorithm Selection Problem faces the problem of choosing, using a prediction model, the best algorithm from a predefined set, to solve a particular instance of a problem. Using Machine Learning techniques, this prediction is performed...

Abstract In this paper we study a variation of the random $k$ -SAT problem, called polarised -SAT, which contains both classical model and version monotone another well-known NP-complete SAT. there is polarisation parameter $p$ , in half clauses each variable occurs negated with probability pure otherwise, while other probabilities are interchanged. For $p=1/2$ get model, at extreme have fully ...

The random k-SAT model is the most important and well-studied distribution over k-SAT instances. It is closely connected to statistical physics and is a benchmark for satisfiability algorithms. We show that when k = Θ(log n), any Cutting Planes refutation for random k-SAT requires exponential size in the interesting regime where the number of clauses guarantees that the formula is unsatisfiable...