Grzegorz Rozenberg

First-Order Programming Theory

Mathematical Background.- 1. Logic and Model Theory.- 2. Inductive Definability.- I Computability.- 3. Introduction to Part I.- 4. Main Properties of Program Schemas.- 5. Extension of Program Schemas.- 6. Program Schemas with Stacks.- 7. Computability.- 8. On Inductive Definability of 1- and 2-Computable Relations.- II Extended Dynamic Logics.- 9. Introduction to Part II.- 10. Description of Program Properties.- 11. Den-based Descriptive Languages.- 12. The Problem of Completeness.- 13. Dynamic Logic Generated by Extension.- 14. Continuous Denotational Semantics.- 15. Definable Denotational Semantics.- III Temporal Characterization of Programs.- 16. Introduction to Part III.- 17. Temporal Logic.- 18. Temporal Logical Description of Program Properties.- 19. Is Temporal Logic Expressible in Dynamic Logic?.- 20. Is Dynamic Logic Expressible in Temporal Logic?.- 21. The Case of Enumerable Models.- 22. Temporal Axiomatization of Program Verification Methods.- IV Programming Logic with Explicit Time.- 23. Introduction to Part IV.- 24. Time Logic.- 25. Definability in Regular Time Theories.- 26. Expressive Power of Time.- Epilogue.- References.- Notations.

WMC 2003 - 4th International Workshop on Membrane Computing

We propose here a (biologically inspired) model of P system called proton pumping P system that is a special case of evolution– communication P system. In cell biology there are transport mechanisms, involving protons. We generalize this idea by considering a few different types of protons. A proton pumping P system is, essentially, an evolution–communication P system where a special subset of symbolobjects (called protons) is used. In such a system we have simple evolution rules (classical evolution rules without target indications), symport and antiport rules that exchange some objects (among them, possibly, other protons) for a proton; taking inspiration from biology, this particular type of antiports is often called proton pumping rules. We show that, as expected, the new model is universal, using noncooperative rules, symport and antiport rules of weight one, and enough types of protons available for the computation. If we decrease the number of types of protons to one or two, then the model is at least as powerful as ET0L system, provided that (total) weak or strong priority of antiport rules over symport and evolution rules are used. Finally, we consider some descriptional complexity measures (again, inspired from biology) for the newly introduced model.

Membrane computing: 16th international conference, CMC 2015 Valencia, Spain, August 17–21, 2015 revised selected papers

Classical concepts of Information Theory are quickly summarized and their application to the computational analysis of genomes is outlined. Genomes are long strings, and this open the possibility of considering them as information sources. From this viewpoint, it turns out that information entropy, mutual information, entropic divergences, codes, and dictionaries (finite formal languages) are fundamental tools for extracting the biological information on which biological functionalities are based on. The importance of random genomes is also motivated, and some genomic distributions are presented and discussed.