Hierarchy theorems for two-way finite state transducers

Summary For a family of languages , CAL() is defined as the family of images of under nondeterministic two-way finite state transducers, while FINITE · VISIT() is the closure of under deterministic two-way finite state transducers; CAL⁰()= and for n0, CAL ⁿ⁺¹()=CAL ⁿ (CAL()). For any semiAFL , if FINITE · VISIT() CAL(), then CAL ⁿ () forms a proper hierarchy and for every n0, FINITE · VISIT(CALⁿ()) CAL ⁿ⁺¹() FINITE · VISIT(CAL ⁿ⁺¹()). If is a SLIP semiAFL or a weakly k-iterative full semiAFL or a semiAFL contained in any full bounded AFL, then FINITE · VISIT() CAL() and in the last two cases, FINITE · VISIT(). If is a substitution closed full principal semiAFL and FINITE · VISIT(), then FINITE · VISIT() CAL(). If is a substitution closed full principal semiAFL generated by a language without an infinite regular set and ₁ is a full semiAFL, then is contained in CAL^m(₁) if and only if it is contained in ₁. Among the applications of these results are the following. For the following families , CAL ⁿ () forms a proper hierarchy: =INDEXED, =ETOL, and any semiAFL contained in CF. The family CF is incomparable with CAL ^m (NESA) where NESA is the family of one-way nonerasing stack languages and INDEXED is incomparable with CAL ^m (STACK) where STACK is the family of one-way stack languages.This work was supported in part by the National Science Foundation under Grants No. DCR74-15091 and MCS-78-04725     

Interval routing schemes

In this paper the problem of routing messages along shortest paths in a distributed network without using complete routing tables is considered. In particular, the complexity of deriving minimum (in terms of number of intervals) interval routing schemes is analyzed under different requirements. For all the cases considered NP-hardness proofs are given, while some approximability results are provided. Moreover, relations among the different cases considered are studied.This work was supported by the EEC ESPRIT II Basic Research Action Program under Contract No. 7141 Algorithms and Complexity II, by the EEC Human Capital and Mobility MAP project, and by the Italian MURST 40% project Algoritmi, Modelli di Calcolo e Strutture Informative.     

Constructing sets of functions which have a givenF-cardinality

The termF-cardinality of (=F-card()) is introduced whereF: ^{n n} is a partial function and is a set of partial functionsf: ^{n n} . TheF-cardinality yields a lower bound for the worst-case complexity of computingF if only functionsf can be evaluated by the underlying abstract automaton without conditional jumps. This complexity bound isindependent from the oracles available for the abstract machine. Thus it is shown that any automaton which can only apply the four basic arithmetic operations needs (n logn) worst-case time to sortn numbers; this result is even true if conditional jumps witharbitrary conditions are possible. The main result of this paper is the following: Given a total functionF: ^{n n} and a natural numberk, it is almost always possible to construct a set such that itsF-cardinality has the valuek; in addition, can be required to be closed under composition of functionsf,g . Moreover, ifF is continuous, then consists of continuous functions.     

Separating complexity classes related to bounded alternating ω-branching programs

We develop a theory of communication within branching programs that provides exponential lower bounds on the size of branching programs that are bounded alternating. Our theory is based on the algebraic concept of -branching programs, : , a semiring homomorphism, that generalizes ordinary branching programs, -branching programs [M2] andMOD _p-branching programs [DKMW].Due to certain exponential lower and polynomial upper bounds on the size of bounded alternating -branching programs we are able to separate the corresponding complexity classesN _ba ,co-N _{ba ba} , andMOD _p - _ba ,p prime, from each other, and from that classes corresponding to oblivious linear length-bounded branching programs investigated in the past.     

Stable adaptive control for a plant with randomly varying parameters

Adaptive control is considered for a two-dimensional linear discrete-time plant with randomly drifting parameters. The certainty equivalent minimum variance control law along with the projection-like identification algorithm are used. The stability of the parameter estimates and exponential stability of the closed-loop system are proved in the absence of any persistent excitation assumption.     

Trainable Articulatory Control Models for Visual Speech Synthesis

This paper deals with the problem of modelling the dynamics of articulation for a parameterised talking head based on phonetic input. Four different models are implemented and trained to reproduce the articulatory patterns of a real speaker, based on a corpus of optical measurements. Two of the models, (Cohen-Massaro and Öhman) are based on coarticulation models from speech production theory and two are based on artificial neural networks, one of which is specially intended for streaming real-time applications. The different models are evaluated through comparison between predicted and measured trajectories, which shows that the Cohen-Massaro model produces trajectories that best matches the measurements. A perceptual intelligibility experiment is also carried out, where the four data-driven models are compared against a rule-based model as well as an audio-alone condition. Results show that all models give significantly increased speech intelligibility over the audio-alone case, with the rule-based model yielding highest intelligibility score.     

A Note on the Rational Closure of Knowledge Bases with Both Positive and Negative Knowledge

The notion of the rational closure of a positive knowledge base K of conditional assertions | (standing for if then normally ) was first introduced by Lehmann (1989) and developed by Lehmann and Magidor (1992). Following those authors we would also argue that the rational closure is, in a strong sense, the minimal information, or simplest, rational consequence relation satisfying K. In practice, however, one might expect a knowledge base to consist not just of positive conditional assertions, | , but also negative conditional assertions, _i (standing for not if then normally . Restricting ourselves to a finite language we show that the rational closure still exists for satisfiable knowledge bases containing both positive and negative conditional assertions and has similar properties to those exhibited in Lehmann and Magidor (1992). In particular an algorithm in Lehmann and Magidor (1992) which constructs the rational closure can be adapted to this case and yields, in turn, completeness theorems for the conditional assertions entailed by such a mixed knowledge base.     

Prequential and Cross-Validated Regression Estimation

Prequential model selection and delete-one cross-validation are data-driven methodologies for choosing between rival models on the basis of their predictive abilities. For a given set of observations, the predictive ability of a model is measured by the model's accumulated prediction error and by the model's average-out-of-sample prediction error, respectively, for prequential model selection and for cross-validation. In this paper, given i.i.d. observations, we propose nonparametric regression estimators—based on neural networks—that select the number of hidden units (or neurons) using either prequential model selection or delete-one cross-validation. As our main contributions: (i) we establish rates of convergence for the integrated mean-squared errors in estimating the regression function using off-line or batch versions of the proposed estimators and (ii) we establish rates of convergence for the time-averaged expected prediction errors in using on-line versions of the proposed estimators. We also present computer simulations (i) empirically validating the proposed estimators and (ii) empirically comparing the proposed estimators with certain novel prequential and cross-validated mixture regression estimators.     

Sometime = always + recursion ≡ always on the equivalence of the intermittent and invariant assertions methods for proving inevitability properties of programs

Summary We propose and compare two induction principles called always and sometime for proving inevitability properties of programs. They are respective formalizations and generalizations of Floyd invariant assertions and Burstall intermittent assertions methods for proving total correctness of sequential programs whose methodological advantages or disadvantages have been discussed in a number of previous papers. Both principles are formalized in the abstract setting of arbitrary nondeterministic transition systems and illustrated by appropriate examples. The sometime method is interpreted as a recursive application of the always method. Hence always can be considered as a special case of sometime. These proof methods are strongly equivalent in the sense that a proof by one induction principle can be rewritten into a proof by the other one. The first two theorems of the paper show that an invariant for the always method can be translated into an invariant for the sometime method even if every recursive application of the later is required to be of finite length. The third and main theorem of the paper shows how to translate an invariant for the sometime method into an invariant for the always method. It is emphasized that this translation technique follows the idea of transforming recursive programs into iterative ones. Of course, a general translation technique does not imply that the original sometime invariant and the resulting always invariant are equally understandable. This is illustrated by an example.     

Formal verification by symbolic evaluation of partially-ordered trajectories

Symbolic trajectory evaluation provides a means to formally verify properties of a sequential system by a modified form of symbolic simulation. The desired system properties are expressed in a notation combining Boolean expressions and the temporal logic next-time operator. In its simplest form, each property is expressed as an assertion [AC], where the antecedentA expresses some assumed conditions on the system state over a bounded time period, and the consequentC expresses conditions that should result. A generalization allows simple invariants to be established and proven automatically.The verifier operates on system models in which the state space is ordered by information content. By suitable restrictions to the specification notation, we guarantee that for every trajectory formula, there is a unique weakest state trajectory that satisfies it. Therefore, we can verify an assertion [AC] by simulating the system over the weakest trajectory forA and testing adherence toC. Also, establishing invariants correspond to simple fixed point calculations.This paper presents the general theory underlying symbolic trajectory evaluation. It also illustrates the application of the theory to the taks of verifying switch-level circuits as well as more abstract implementations.This research was supported by the Defense Advanced Research Projects Agency, ARPA Order Number 4976, by the National Science Foundation, under grant number MIP-8913667, by operating grant OGPO 109688 from the Natural Sciences and Engineering Research Council of Canada, and by a fellowship from the British Columbia Advanced Systems Institute.