In this paper, we continue the research on the power of contextual grammars with selection languages from subfamilies of the family of regular languages. We investigate infix-, prefix-, and suffix-free languages (referred to as idefix-free languages) and compare such language families to some other subregular families of languages (finite, monoidal, nilpotent, combinational, (symmetric) definite, ordered, non-counting, power-separating, commutative, circular, union-free, star, and comet languages). Further, we compare the families of the hierarchies obtained for external contextual grammars with the language families defined by these new types for the selection. In this way, we extend the existing hierarchies by new language families.
Finite automata with translucent input letters are a recent model of discontinuous input processing. Basically, classical finite automata are equipped with a translucency function that defines, depending on the state, the set of translucent input symbols. While processing the input, translucent symbols are skipped and only visible symbols are read and processed. It is distinguished between deterministic and nondeterministic models and, in addition, between returning and non-returning models. In the former case, the automaton restarts from the left end of the input after having consumed some visible symbol, whereas in the latter case the automaton restarts from the left end of the input when the right endmarker symbol is seen. Returning finite automata with translucent letters have been introduced by Nagy and Otto and its non-returning variant has been introduced by Mraz and Otto. Many results concerning the computational capacity, relations between deterministic and nondeterministic models, and relations between returning and non-returning models are known. Moreover, some results on closure properties and decidability questions have been obtained as well. However, some questions have still been open since many years. In this paper, we will give answers to some of these open questions. In particular, we show the non-closure under concatenation, Kleene star, reversal, and inverse homomorphism for the non-returning deterministic as well as nondeterministic model. We also obtain non-closure under inverse homomorphism for the returning deterministic and nondeterministic model. Finally, we investigate the emptiness problem for non-returning finite automata with translucent input letters and show the decidability of the problem in case of deterministic as well as nondeterministic automata.
In the past literature, families of two-way finite automata and pushdown automata having limited state complexity (i.e., the total number of inner states) and stack-state complexity (i.e., the total number of inner states multiplied by the total number of strings "pushable" to a stack), have been studied in direct connection to (mainstream) space-bounded complexity classes equipped with Karp-Lipton style advice of limited size when all inputs given to the automata have bounded length. Here, we acknowledge two major factors – size and ceiling – of such families, which have a significant impact on the complexity of finite and pushdown automata families, where the "size" refers to (stack-)state complexity and the "ceiling" refers to an input's length bound. In this line of study, we further explore those effects caused by different sizes and ceilings.
Regular expressions with counting operations (c-regexes) offer a compact representation of repeating patterns by allowing numerical bounds to be added to subexpressions. Recent work introduced the counting-set data structure, which allows simultaneous updates of multiple counter values for efficient matching. However, this approach suffers from a performance bottleneck when counting-sets must be replicated due to the presence of branching transitions. We propose a sparse counting-set approach, which reduces the replication overhead by maintaining only essential counter values, thereby yielding a more efficient matching algorithm.
Backtracking regular expression matchers are widely used due to their expressive power but may exhibit exponential worst-case matching time. Memoization provides a principled method for eliminating redundant computation and ensuring linear matching time, but full memoization is memory-intensive and impractical. We introduce the Minimum Feedback Node (MFN) memoization scheme, a selective memoization strategy based on computing a minimum feedback vertex set of an automaton. We establish relationships with existing memoization schemes and analyze their behaviour under both Thompson and Glushkov automaton constructions.
