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The two-volume set LNCS 6755 and LNCS 6756 constitutes the refereed lawsuits of the thirty eighth foreign Colloquium on Automata, Languages and Programming, ICALP 2011, held in Zürich, Switzerland, in July 2011. The 114 revised complete papers (68 papers for song A, 29 for music B, and 17 for song C) offered including four invited talks, three most sensible scholar papers, and three most sensible papers have been rigorously reviewed and chosen from a complete of 398 submissions. The papers are grouped in 3 significant tracks on algorithms, complexity and video games; on common sense, semantics, automata, and thought of programming; in addition to on foundations of networked computation: types, algorithms and knowledge management.

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**Example text**

Proof. We use the notation |α(x)| to denote the number of output symbols in the expression α(x). We deﬁne |α| = x (|α(x)|). , the maximum number of output symbols added by any transition in T . Let uw be the shorthand for some output of T upon reading the string w. We prove the result by induction on the length of the input. The base case is valid; since if |w| = 0, the length of uw is zero. |w|. a, and let q be the state of T after reading w. Let E be the subset of E containing transitions of the form (q, a, β, q ).

Proof. Recall that a nmsos transducer W has a ﬁnite copy set C, and a ﬁnite set of parameters X = {X1 , . . , second order variables ranging over sets of vertices. This proof combines two main ideas: (a) a given run of a nsst T can be encoded in the ﬁnite copy set of a nmsos transducer W , and, (b) each run of T can be guessed by W by guessing a valuation for its parameters. The construction for part (a) is as discussed, for details see [1]. We focus on part (b). As T is nondeterministic, there may be multiple transitions on a given input symbol that are enabled in a state q.

Two distributions that are -close assign essentially the same probability to all events. In particular, randomized algorithms and protocols retain their useful properties when run with distributions that are close to uniform (rather than uniform). The motivation given in Section 1 leads to the following formal deﬁnition of an extractor (we also deﬁne a weaker object called a “disperser”). Deﬁnition 2 (deterministic extractors and dispersers). Let m ≤ n be integers and let ≥ 0 be a parameter. Let E : {0, 1}n → {0, 1}m be a function and X be a distribution over {0, 1}n.