We identify and study a natural and frequently occurring subclass of Concurrent-Read, Exclusive-Write Parallel Random Access Machines (CREW-PRAMs). Called Concurrent-Read, Owner-Write, or CROW-PRAMs, these are machines in which each global memory location is assigned a unique "owner" processor, which is the only processor allowed to write into it. Considering the difficulties that would be involved in physically realizing a full CREW-PRAM model, it is interesting to observe that in fact, most known CREW-PRAM algorithms satisfy the CROW restriction or can be easily modified to do so. This paper makes three main contributions. First, we formally define the CROW-PRAM model and demonstrate its stability

Abstract. The problem of learning context-free languages is studied, in which a subclass called c-deterministic context-free languages is introduced. The class of c-deterministic context-free languages properly contains the class of regular sets. It is shown that the class of c-deterministic

We investigate the computational power of several types of restarting automata - nondeterministic, deterministic; monotonic; normal - and compare them to (deterministic) context-free languages. The relation between restarting automata and Marcus contextual grammars is discussed and a class

It is shown that suffix recognition for deterministic context-free languages can be done on a PRAM multi-processor within the upper complexity bounds of the graph reachability problem.

"Meaning " may be assigned to a string in a context-free language by defining "at-tributes " of the symbols in a derivation tree for that string. The attributes can be de-fined by functions associated with each production in the grammar. This paper examines the implications

ABSTRACT. Several recent works have studied subfamilies of deterministic context-free languages with good closure properties, for instance the families of input-driven or visibly pushdown languages,ormoregenerallyfamiliesoflanguagesacceptedbypushdownautomatawhosestackheight can be uniquely

; it has an n 2 bound for unambiguous grammars; and it runs in linear time on a large class of grammars, which seems to include most practical context-free programming language grammars. In an empirical comparison it appears to be superior to the top-down and bottom-up algorithms studied by Griffiths

of learning deterministic context-free languages (e.g., anbn and parenthesis languages) from examples. However, the learning task is computationally intensive. In this paper we discus some ways in which a priori knowledge about the task and data could be used for efficient learning. We also observe

deterministic context-free language (CFL) in a single pass, as long as the grammar describing the CFL belongs to the LR(k), k ≥ 1 subset of CFGs. However, obtaining a suitable LR(k) description for a lan-guage is not an easy task — especially when k = 1 — and usually entails an increase in complexity

We introduce a hierarchy of monotonic rewriting automata with a restart operation and show that its deterministic version collapses in a sequence of characterizations of deterministic context-free languages. The nondeterministic version of it gives a proper hierarchy of classes of languages