We survey several results and open problems related to synchronizing automata. In particular, we discuss some recent advances towards a solution of the Cerný conjecture.

/pdf/synchronizing-automata-and-the-cerny-conjecture-1lprq8wxe3.pdf

Synchronizing Automata and the Černý Conjecture

We present an algorithm that uses finite automata to find the common motifs with gaps occurring in all strings belonging to a finite set S = {S1,S2,...,Sr}. In order to find these common motifs we must first identify the factors that exist in each string. Therefore the algorithm begins by constructing a factor automaton for each string Si. To find the common factors of all the strings, the algorithm needs to gather all the factors from the strings together in one data structure and this is achieved by computing an automaton that accepts the union of the above-mentioned automata. Using this automaton we are able to create a new factor alphabet. Based on this factor alphabet a finite automaton is created for each string Si that accepts sequences of all non overlapping factors residing in each string. The intersection of the latter automata produces the finite automaton which accepts all the common subsequences with gaps over the factor alphabet that are present in all the strings of the set S = {S1,S2,...,Sr}. These common subsequences are the common motifs of the strings.

Finding common motifs with gaps using finite automata

In a data word or a data tree each position carries a label from a finite alphabet and a data value from some infinite domain. These models have been considered in the realm of semistructured data, timed automata and extended temporal logics.

This paper survey several know results on automata and logics manipulating data words and data trees, the focus being on their relative expressive power and decidability.

/pdf/automata-and-logics-for-words-and-trees-over-an-infinite-3hvmgnmvnk.pdf

Automata and logics for words and trees over an infinite alphabet

Elkhound is an implementation of the Generalized LR (GLR) parsing algorithm [Tomita86, Rekers92] that allows the user to associate arbitrary C++ code with reduction actions, while retaining full control of sharing and disambiguation. Elkhound specifications are similar to those of YACC for LALR(1) grammar fragments, exposing the details of the GLR reduction process only where the grammar requires it. Further, the algorithm itself can switch on a token-by-token basis between GLR and simple LR, achieving very good performance for unambiguous portions of the input. The resulting system is familiar to even casual users of tools like YACC or Bison, but powerful enough to elegantly handle the dark corners of real-world languages.

https://people.eecs.berkeley.edu/~necula/Papers/elkhound_cc04.pdf

Elkhound: A Fast, Practical GLR Parser Generator

Much work is focused upon music genre recognition (MGR) from audio recordings, symbolic data, and other modalities. While reviews have been written of some of this work before, no survey has been made of the approaches to evaluating approaches to MGR. This paper compiles a bibliography of work in MGR, and analyzes three aspects of evaluation: experimental designs, datasets, and figures of merit.

/pdf/a-survey-of-evaluation-in-music-genre-recognition-29v3l1icya.pdf

A survey of evaluation in music genre recognition

We prove a property of generalized LR (GLR) parsing – if the grammar is without right and hidden left recursions, then the number of consecutive reductions between the shifts of two adjacent symbols cannot be greater than a constant. Further, we show that this property can be used for constructing an optimized version of our GLR parser. Compared with a standard GLR parser, our optimized parser reads one symbol on every transition and performs significantly fewer stack operations. Our timings show that, especially for highly ambiguous grammars, our parser is significantly faster than a standard GLR parser.

Even faster generalized LR parsing

There are two ways of using the nondeterministic finite automata (NFA). The first one is the transformation to the equivalent deterministic finite automaton and the second one is the simulation of the run of NFA. In this paper we discuss the second way. We present an overview of the simulation methods that have been found in the approximate string matching. We generalize these simulation methods and form the rules for the usage of these methods.

Implementation of Nondeterministic Finite Automata for Approximate Pattern Matching

The problem of attribute evaluation during LR parsing is considered. Several definitions of LR-attributed grammars are presented. Relations of corresponding attribute grammar classes are analysed. Also the relations between LR-attributed grammars and LL-attributed grammars and between LR-attributed grammars and a class of one-pass attributed grammars based on left-corner grammars are considered.

Bořivoj Melichar

Papers

Finding common motifs with gaps using finite automata

Even faster generalized LR parsing

Implementation of Nondeterministic Finite Automata for Approximate Pattern Matching

The hierarchy of LR-Attributed Grammars

Approximate string matching using factor automata