82,139 research outputs found
Functional Dynamics I : Articulation Process
The articulation process of dynamical networks is studied with a functional
map, a minimal model for the dynamic change of relationships through iteration.
The model is a dynamical system of a function , not of variables, having a
self-reference term , introduced by recalling that operation in a
biological system is often applied to itself, as is typically seen in rules in
the natural language or genes. Starting from an inarticulate network, two types
of fixed points are formed as an invariant structure with iterations. The
function is folded with time, until it has finite or infinite piecewise-flat
segments of fixed points, regarded as articulation. For an initial logistic
map, attracted functions are classified into step, folded step, fractal, and
random phases, according to the degree of folding. Oscillatory dynamics are
also found, where function values are mapped to several fixed points
periodically. The significance of our results to prototype categorization in
language is discussed.Comment: 48 pages, 15 figeres (5 gif files
Compiling ER Specifications into Declarative Programs
This paper proposes an environment to support high-level database programming
in a declarative programming language. In order to ensure safe database
updates, all access and update operations related to the database are generated
from high-level descriptions in the entity- relationship (ER) model. We propose
a representation of ER diagrams in the declarative language Curry so that they
can be constructed by various tools and then translated into this
representation. Furthermore, we have implemented a compiler from this
representation into a Curry program that provides access and update operations
based on a high-level API for database programming.Comment: Paper presented at the 17th Workshop on Logic-based Methods in
Programming Environments (WLPE2007
Digital implementation of the cellular sensor-computers
Two different kinds of cellular sensor-processor architectures are used nowadays in various
applications. The first is the traditional sensor-processor architecture, where the sensor and the
processor arrays are mapped into each other. The second is the foveal architecture, in which a
small active fovea is navigating in a large sensor array. This second architecture is introduced
and compared here. Both of these architectures can be implemented with analog and digital
processor arrays. The efficiency of the different implementation types, depending on the used
CMOS technology, is analyzed. It turned out, that the finer the technology is, the better to use
digital implementation rather than analog
Topological Semantics and Decidability
It is well-known that the basic modal logic of all topological spaces is
. However, the structure of basic modal and hybrid logics of classes of
spaces satisfying various separation axioms was until present unclear. We prove
that modal logics of , and topological spaces coincide and are
S4T_1 spaces coincide.Comment: presentation changes, results about concrete structure adde
Invariant Synthesis for Incomplete Verification Engines
We propose a framework for synthesizing inductive invariants for incomplete
verification engines, which soundly reduce logical problems in undecidable
theories to decidable theories. Our framework is based on the counter-example
guided inductive synthesis principle (CEGIS) and allows verification engines to
communicate non-provability information to guide invariant synthesis. We show
precisely how the verification engine can compute such non-provability
information and how to build effective learning algorithms when invariants are
expressed as Boolean combinations of a fixed set of predicates. Moreover, we
evaluate our framework in two verification settings, one in which verification
engines need to handle quantified formulas and one in which verification
engines have to reason about heap properties expressed in an expressive but
undecidable separation logic. Our experiments show that our invariant synthesis
framework based on non-provability information can both effectively synthesize
inductive invariants and adequately strengthen contracts across a large suite
of programs
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