Entropy rate calculations of algebraic measures

Let $K = \{0,1,...,q-1\}$. We use a special class of translation invariant measures on $K^\mathbb{Z}$ called algebraic measures to study the entropy rate of a hidden Markov processes. Under some irreducibility assumptions of the Markov transition matrix we derive exact formulas for the entropy rate of a general $q$ state hidden Markov process derived from a Markov source corrupted by a specific noise model. We obtain upper bounds on the error when using an approximation to the formulas and numerically compute the entropy rates of two and three state hidden Markov models

Echos of the liquid-gas phase transition in multifragmentation

A general discussion is made concerning the ways in which one can get signatures about a possible liquid-gas phase transition in nuclear matter. Microcanonical temperature, heat capacity and second order derivative of the entropy versus energy formulas have been deduced in a general case. These formulas are {\em exact}, simply applicable and do not depend on any model assumption. Therefore, they are suitable to be applied on experimental data. The formulas are tested in various situations. It is evidenced that when the freeze-out constraint is of fluctuating volume type the deduced (heat capacity and second order derivative of the entropy versus energy) formulas will prompt the spinodal region through specific signals. Finally, the same microcanonical formulas are deduced for the case when an incomplete number of fragments per event are available. These formulas could overcome the freeze-out backtracking deficiencies.Comment: accepted to Nuclear Physics

The impact of Entropy and Solution Density on selected SAT heuristics

In a recent article [Oh'15], Oh examined the impact of various key heuristics (e.g., deletion strategy, restart policy, decay factor, database reduction) in competitive SAT solvers. His key findings are that their expected success depends on whether the input formula is satisfiable or not. To further investigate these findings, we focused on two properties of satisfiable formulas: the entropy of the formula, which approximates the freedom we have in assigning the variables, and the solution density, which is the number of solutions divided by the search space. We found that both predict better the effect of these heuristics, and that satisfiable formulas with small entropy `behave' similarly to unsatisfiable formulas