Fuzzy ω-automata

Reversible pushdown automata are deterministic pushdown automata having the property that any configuration occurring in any computation has exactly one predecessor. In this paper, the computational capacity of reversible computations in pushdown automata is investigated and turns out to lie properly in between the regular and deterministic context-free languages. Furthermore, it can be shown that a deterministic context-free language cannot be accepted reversibly if more than realtime is necessary for acceptance. Closure properties as well as decidability questions for reversible pushdown automata are studied. Finally, the question of whether a given (nondeterministic) pushdown automaton can be algorithmically tested for reversibility is answered in the affirmative, whereas it is shown to be undecidable whether the language accepted by a given nondeterministic pushdown automaton is reversible.

In general, quantum computation models are expected to be more powerful than classical counterparts. However, sometimes this is not the case.It is known that there exists some regular language which one-way quantum finite automata and one-way quantum counter automata cannot recognize. This is due to the restriction of reversibility which quantum models must satisfy. Thus, it is an interesting question: what kinds of quantum models suffer from/overcome the restriction? To tackle this problem, we focus on (empty-stack acceptance) one-way quantum pushdown automata, and show that there exists a regular language which one-way quantum pushdown automata cannot recognize. This implies adding a stack to one-way finite automata cannot overcome the restriction of reversibility if we adopt empty-stack acceptance as the acceptance mode.

Error detection in formal languages

Degree-languages: A new concept of acceptance

With the rise of machine learning and data analytics, the ability to process large and diverse sets of data efficiently has become crucial. Research has shown that data transformation is a key performance bottleneck for applications across a variety of domains, from data analytics to scientific computing. Custom hardware accelerators and GPU implementations targeting specific data transformation tasks can alleviate the problem, but suffer from narrow applicability and lack of generality.To tackle this problem, we propose a GPU-accelerated data transformation engine grounded on pushdown transducers. We define an extended pushdown transducer abstraction (effPDT) that allows expressing a wide range of data transformations in a memory-efficient fashion, and is thus amenable for GPU deployment. The effPDT execution engine utilizes a data streaming model that reduces the application’s memory requirements significantly, facilitating deployment on high- and low-end systems. We showcase our GPU-accelerated engine on a diverse set of transformation tasks covering data encoding/decoding, parsing and querying of structured data, and matrix transformation, and we evaluate it against publicly available CPU and GPU library implementations of the considered data transformation tasks. To understand the benefits of the effPDT abstraction, we extend our data transformation engine to also support finite state transducers (FSTs), we map the considered data transformation tasks on FSTs, and we compare the performance and resource requirements of the FST-based and the effPDT-based implementations.

Input-driven pushdown automata (IDPDA) are pushdown automata where the next action on the pushdown store (push, pop, nothing) is solely governed by the input symbol. Nowadays such devices are usually defined such that popping from the empty pushdown does not block the computation but continues it with empty pushdown. Here, we consider IDPDAs that have a more balanced behavior concerning pushing and popping. Digging input-driven pushdown automata (DIDPDA) are basically IDPDAs that, when forced to pop from the empty pushdown, dig a hole of the shape of the popped symbol in the bottom of the pushdown. Popping further symbols from a pushdown having a hole at the bottom deepens the current hole furthermore. The hole can only be filled up by pushing symbols previously popped. We study the impact of the new behavior of DIDPDAs on their power and compare their capacities with the capacities of ordinary IDPDAs and tinput-driven pushdown automata which are basically IDPDAs whose input may be preprocessed by length-preserving finite state transducers. It turns out that the capabilities are incomparable. We address the determinization of DIDPDAs and their descriptional complexity, closure properties, and decidability questions.

Meyer and Fischer b][MF] proved that nondeterministic finite automata (NFA) can be exponentially more concise than deterministic finite automata (DFA) in their representations of regular languages. Several variants of that basic finite state machine model are now being used to analyze parallelism and to build real-time software systems [HL+]. Even though these variants can sometimes represent regular languages in a more concise manner than NFA, the underlying models fundamentally differ from NFA in how they operate. Degree automata [W] (DA), however, differ from NFA only in their acceptance criteria and accept only regular languages. We show here that DA are also exponentially more concise than NFA on some sequences of regular languages. We also show that the conciseness of probabilistic automata [R] with isolated cutpoints can be unbounded over DA and, concurrently, i.e., over the same sequence of languages, those DA can be exponentially more concise than NFA.

Current trusted computing platform only verifies application’s static Hash value, it could not prevent application from being dynamic attacked. This paper gives one static analysis-based behavior model building method for trusted computing dynamic verification, including control flow graph (CFG) building, finite state automata (FSA) constructing, ɛ run cycle removing, ɛ transition removing, deterministic finite state (DFA) constructing, trivial FSA removing, and global push down automata (PDA) constructing. According to experiment, this model built is a reduced model for dynamic verification and covers all possible paths, because it is based on binary file static analysis.

Language analysis has always been a popular subject in computer science; henceforth a vast knowledge base for compiler construction is available nowadays and consequently one can devise several models that provide efficient methods to determine the acceptance of a programming language, either at a lexical, syntactic or semantic level. By evaluating and analyzing existing techniques, we have noticed that certain algorithm complexity or grammar adaptation was required to accomplish an efficient solution. Therefore here lies the motivation for the current research. We are going to present an implementation based on a lexical and syntactic analysis model seeking to fulfill the following requisites: simplicity and expressiveness, algorithm efficiency and reduced grammar adaptation. Lexical resolution was mainly supported by regular language acceptance, whereas the subsequent syntactic analysis was performed with a pushdown automaton allowing an efficient method to recognize a context free language, avoiding additional elements, such as action-goto tables, often used in bottom-up parsers. The proposed model highlights the underlying relationship between language interpretation and automata theory. Then you may encounter an interesting approach which will provide a way to go through the most basic concepts of formal language theory and how these are wielded for solving syntactic analysis problems.

This is the second in a series of papers on the inherent power of bounded cooperative concurrency, whereby an automaton can be in some bounded number of states that cooperate in accepting the input. In this paper, we consider pushdown automata. We are interested in differences in power of expression and in exponential (or higher) discrepancies in succinctness between variants of pda's that incorporate nondeterminism (E), pure parallelism (A), and bounded cooperative concurrency (C). Technically, the results are proved for cooperating push-down automata with cooperating states, but they hold for appropriate versions of most concurrent models of computation. We exhibit exhaustive sets of upper and lower bounds on the relative succinctness of these features for three classes of languages: deterministic context-free, regular, and finite. For example, we show that C represents exponential savings in succinctness in all cases except when both E and A are present (i.e., except for alternating automata), and that E and A represent unlimited savings in succinctness in all cases.

The erasure of each bit of information by a computing device has an intrinsic energy cost. Although any Turing machine can be rewritten to be thermodynamically reversible without changing the recognized language, finite automata that are restricted to scan their input once in “real-time” fashion can only recognize the members of a proper subset of the class of regular languages in this reversible manner. We use a general quantum finite automaton model to study the thermodynamic cost per step associated with the recognition of different regular languages. We show that zero-error quantum finite automata have no energy cost advantage over their classical deterministic counterparts, and prove an upper bound for the cost that holds for all regular languages. We also demonstrate languages for which “error can be traded for energy”, i.e. whose zero-error recognition is associated with provably bigger energy cost per step when compared to their bounded-error recognition by real-time finite-memory quantum devices.KeywordsQuantum finite automataReversibility

We compare pushdown automata (PDAs for short) against other representations. First, we show that there is a family of PDAs over a unary alphabet with \(n\) states and \(p \ge 2n + 4\) stack symbols that accepts one single long word for which every equivalent context-free grammar needs \(\varOmega (n^2(p-2n-4))\) variables. This family shows that the classical algorithm for converting a PDA into an equivalent context-free grammar is optimal even when the alphabet is unary. Moreover, we observe that language equivalence and Parikh equivalence, which ignores the ordering between symbols, coincide for this family. We conclude that, when assuming this weaker equivalence, the conversion algorithm is also optimal. Second, Parikh’s theorem motivates the comparison of PDAs against finite state automata. In particular, the same family of unary PDAs gives a lower bound on the number of states of every Parikh-equivalent finite state automaton. Finally, we look into the case of unary deterministic PDAs. We show a new construction converting a unary deterministic PDA into an equivalent context-free grammar that achieves best known bounds.

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.

We study a model where two opposing provers debate over the membership status of a given string in a language, trying to convince a weak verifier whose coins are visible to all. We show that the incorporation of just two qubits to an otherwise classical constant-space verifier raises the class of debatable languages from at most NP to the collection of all Turing-decidable languages (recursive languages). When the verifier is further constrained to make the correct decision with probability 1, the corresponding class goes up from the regular languages up to at least E. We also show that the quantum model outperforms its classical counterpart when restricted to run in polynomial time, and demonstrate some non-context-free languages which have such short debates with quantum verifiers.

Let DPDA(k) (resp. NPDA(k)) be the class of languages recognized by one-way k-head deterministic (resp. nondeterministic) pushdown automata. The main result of this paper is that for each k>0 DPDA(k) ⪇ DPDA (k+1) and DPDA(k) ⪇ NPDA(k).