Community Structure in Industrial SAT Instances

Modern SAT solvers have experienced a remarkable progress on solving industrial instances. Most of the techniques have been developed after an intensive experimental process. It is believed that these techniques exploit the underlying structure of industrial instances. However, there are few works trying to exactly characterize the main features of this structure. The research community on complex networks has developed techniques of analysis and algorithms to study real-world graphs that can be used by the SAT community. Recently, there have been some attempts to analyze the structure of industrial SAT instances in terms of complex networks, with the aim of explaining the success of SAT solving techniques, and possibly improving them. In this paper, inspired by the results on complex networks, we study the community structure, or modularity, of industrial SAT instances. In a graph with clear community structure, or high modularity, we can find a partition of its nodes into communities such that most edges connect variables of the same community. In our analysis, we represent SAT instances as graphs, and we show that most application benchmarks are characterized by a high modularity. On the contrary, random SAT instances are closer to the classical Erd\"os-R\'enyi random graph model, where no structure can be observed. We also analyze how this structure evolves by the effects of the execution of a CDCL SAT solver. In particular, we use the community structure to detect that new clauses learned by the solver during the search contribute to destroy the original structure of the formula. This is, learned clauses tend to contain variables of distinct communities

Complex demonstratives, hidden arguments, and presupposition

Standard semantic theories predict that non-deictic readings for complex demonstratives should be much more widely available than they in fact are. If such readings are the result of a lexical ambiguity, as Kaplan (1977) and others suggest, we should expect them to be available wherever a definite description can be used. The same prediction follows from ‘hidden argument’ theories like the ones described by King (2001) and Elbourne (2005). Wolter (2006), however, has shown that complex demonstratives admit non-deictic interpretations only when a precise set of structural constrains are met. In this paper, I argue that Wolter’s results, properly understood, upend the philosophical status quo. They fatally undermine the ambiguity theory and demand a fundamental rethinking of the hidden argument approach

Exploiting microvariation: How to make the best of your incomplete data

n this article we discuss the use of big corpuses or databases as a first step for qualitative analysis of linguistic data. We concentrate on ASIt, the Syntactic Atlas of Italy, and take into consideration the different types of dialectal data that can be collected from similar corpora and databases. We analyze all the methodological problems derived from the necessary compromise between the strict requirements imposed by a scientific inquiry and the management of big amounts of data. As a possible solution, we propose that the type of variation is per se a tool to derive meaningful generalizations. To implement this idea, we examine three different types of variation patterns that can be used in the study of morpho-syntax: the geographical distribution of properties (and their total or partial overlapping, or complementary distribution), the so-called leopard spots variation, and the lexical variation index, which can be used to determine the internal complexity of functional items