The goal of this paper is to develop an estimate for the entropy of random long-range correlated symbolic sequences with elements belonging to a finite alphabet. As a plausible model, we use the high-order additive stationary ergodic Markov chain. Supposing that the correlations between random elements of the chain are weak we express the differential entropy of the sequence by means of the sym...

Real-time software, often used to control event-driven process control systems, is usually structured as a set of concurrent and interacting tasks. Therefore, output values of real-time software depend not only on the input values but also on internal and nondeterministic execution patterns caused by task synchronization. In order to test real-time software effectively, one must generate test c...

We present a new technique called Monotonic Partial Order Reduction (MPOR) that effectively combines dynamic partial order reduction with symbolic state space exploration for model checking concurrent software. Our technique hinges on a new characterization of partial orders defined by computations of a concurrent program in terms of quasi-monotonic sequences of thread-ids. This characterizatio...

We describe the application of a hybrid symbolic/connectionist machine learning algorithm to the task of recognizing important genetic sequences. The symbolic portion of the KBANN system utilizes inference rules that provide a roughly-correct method for recognizing a class of DNA sequences known as eukaryotic splice-junctions. We then map this "domain theory" into a neural network and provide t...

We study statistical properties of the Jensen-Shannon divergence D, which quantifies the difference between probability distributions, and which has been widely applied to analyses of symbolic sequences. We present three interpretations of D in the framework of statistical physics, information theory, and mathematical statistics, and obtain approximations of the mean, the variance, and the prob...

We describe the application of a hybrid symbolic/connectionist machine learning algorithm to the task of recognizing important genetic sequences. The symbolic portion of the KBANN system utilizes inference rules that provide a roughly-correct method for recognizing a class of DNA sequences known as eukaryotic splice-junctions. We then map this "domain theory" into a neural network and provide t...

In this paper, a method to construct topological template in terms of symbolic dynamics for the diamagnetic Kepler problem is proposed. To confirm the topological template, rotation numbers of invariant manifolds around unstable periodic orbits in a phase space are taken as an object of comparison. The rotation numbers are determined from the definition and connected with symbolic sequences enc...

This paper studies one application of mutual information to symbolic sequence: the mutual information function M(d). This function is compared with the more frequently used correlation function (d). An exact relation between M(d) and (d) is derived for binary sequences. For sequences with more than two symbols, no such general relation exists; in particular, (d) = 0 may or may not lead to M(d) ...