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Article . 2007 . Peer-reviewed
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Conference object . 2007 . Peer-reviewed
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A Robust Class of Context-Sensitive Languages

Authors: LA TORRE, Salvatore; MADHUSUDAN P; PARLATO, GENNARO;

A Robust Class of Context-Sensitive Languages

Abstract

We define a new class of languages defined by multi-stack automata that forms a robust subclass of context-sensitive languages, with decidable emptiness and closure under boolean operations. This class, called multi-stack visibly pushdown languages (MVPLs), is defined using multi-stack pushdown automata with two restrictions: (a) the pushdown automaton is visible, i.e. the input letter determines the operation on the stacks, and (b) any computation of the machine can be split into k stages, where in each stage, there is at most one stack that is popped. MVPLs are an extension of visibly pushdown languages that captures noncontext free behaviors, and has applications in analyzing abstractions of multithreaded recursive programs, signifi- cantly enlarging the search space that can be explored for them. We show that MVPLs are closed under boolean operations, and problems such as emptiness and inclusion are decidable. We characterize MVPLs using monadic second-order logic over appropriate structures, and exhibit a Parikh theorem for them.

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Italy, United Kingdom, Italy
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
68
Top 10%
Top 10%
Top 10%