575,580 research outputs found
Scaled tree fractals do not strictly self-assemble
In this paper, we show that any scaled-up version of any discrete
self-similar {\it tree} fractal does not strictly self-assemble, at any
temperature, in Winfree's abstract Tile Assembly Model.Comment: 13 pages, 3 figures, Appeared in the Proceedings of UCNC-2014, pp
27-39; Unconventional Computation and Natural Computation - 13th
International Conference, UCNC 2014, London, ON, Canada, July 14-18, 2014,
Springer Lecture Notes in Computer Science ISBN 978-3-319-08122-
Evolving MultiAlgebras unify all usual sequential computation models
It is well-known that Abstract State Machines (ASMs) can simulate
"step-by-step" any type of machines (Turing machines, RAMs, etc.). We aim to
overcome two facts: 1) simulation is not identification, 2) the ASMs simulating
machines of some type do not constitute a natural class among all ASMs. We
modify Gurevich's notion of ASM to that of EMA ("Evolving MultiAlgebra") by
replacing the program (which is a syntactic object) by a semantic object: a
functional which has to be very simply definable over the static part of the
ASM. We prove that very natural classes of EMAs correspond via "literal
identifications" to slight extensions of the usual machine models and also to
grammar models. Though we modify these models, we keep their computation
approach: only some contingencies are modified. Thus, EMAs appear as the
mathematical model unifying all kinds of sequential computation paradigms.Comment: 12 pages, Symposium on Theoretical Aspects of Computer Scienc
Computation in Physical Systems: A Normative Mapping Account
The relationship between abstract formal procedures and the activities of actual physical systems has proved to be surprisingly subtle and controversial, and there are a number of competing accounts of when a physical system can be properly said to implement a mathematical formalism and hence perform a computation. I defend an account wherein computational descriptions of physical systems are high-level normative interpretations motivated by our pragmatic concerns. Furthermore, the criteria of utility and success vary according to our diverse purposes and pragmatic goals. Hence there is no independent or uniform fact to the matter, and I advance the ‘anti-realist’ conclusion that computational descriptions of physical systems are not founded upon deep ontological distinctions, but rather upon interest-relative human conventions. Hence physical computation is a ‘conventional’ rather than a ‘natural’ kind
ASMs and Operational Algorithmic Completeness of Lambda Calculus
We show that lambda calculus is a computation model which can step by step
simulate any sequential deterministic algorithm for any computable function
over integers or words or any datatype. More formally, given an algorithm above
a family of computable functions (taken as primitive tools, i.e., kind of
oracle functions for the algorithm), for every constant K big enough, each
computation step of the algorithm can be simulated by exactly K successive
reductions in a natural extension of lambda calculus with constants for
functions in the above considered family. The proof is based on a fixed point
technique in lambda calculus and on Gurevich sequential Thesis which allows to
identify sequential deterministic algorithms with Abstract State Machines. This
extends to algorithms for partial computable functions in such a way that
finite computations ending with exceptions are associated to finite reductions
leading to terms with a particular very simple feature.Comment: 37 page
Simulation of topological field theories by quantum computers
Quantum computers will work by evolving a high tensor power of a small (e.g.
two) dimensional Hilbert space by local gates, which can be implemented by
applying a local Hamiltonian H for a time t. In contrast to this quantum
engineering, the most abstract reaches of theoretical physics has spawned
topological models having a finite dimensional internal state space with no
natural tensor product structure and in which the evolution of the state is
discrete, H = 0. These are called topological quantum filed theories (TQFTs).
These exotic physical systems are proved to be efficiently simulated on a
quantum computer. The conclusion is two-fold: 1. TQFTs cannot be used to define
a model of computation stronger than the usual quantum model BQP. 2. TQFTs
provide a radically different way of looking at quantum computation. The rich
mathematical structure of TQFTs might suggest a new quantum algorithm
Inductive-data-type Systems
In a previous work ("Abstract Data Type Systems", TCS 173(2), 1997), the last
two authors presented a combined language made of a (strongly normalizing)
algebraic rewrite system and a typed lambda-calculus enriched by
pattern-matching definitions following a certain format, called the "General
Schema", which generalizes the usual recursor definitions for natural numbers
and similar "basic inductive types". This combined language was shown to be
strongly normalizing. The purpose of this paper is to reformulate and extend
the General Schema in order to make it easily extensible, to capture a more
general class of inductive types, called "strictly positive", and to ease the
strong normalization proof of the resulting system. This result provides a
computation model for the combination of an algebraic specification language
based on abstract data types and of a strongly typed functional language with
strictly positive inductive types.Comment: Theoretical Computer Science (2002
Quantum Genetics, Quantum Automata and Quantum Computation
The concepts of quantum automata and quantum computation are studied in the context of quantum genetics and genetic networks with nonlinear dynamics. In a previous publication (Baianu,1971a) the formal concept of quantum automaton was introduced and its possible implications for genetic and metabolic activities in living cells and organisms were considered. This was followed by a report on quantum and abstract, symbolic computation based on the theory of categories, functors and natural transformations (Baianu,1971b). The notions of topological semigroup, quantum automaton,or quantum computer, were then suggested with a view to their potential applications to the analogous simulation of biological systems, and especially genetic activities and nonlinear dynamics in genetic networks. Further, detailed studies of nonlinear dynamics in genetic networks were carried out in categories of n-valued, Lukasiewicz Logic Algebras that showed significant dissimilarities (Baianu, 1977) from Bolean models of human neural networks (McCullough and Pitts,1945). Molecular models in terms of categories, functors and natural transformations were then formulated for uni-molecular chemical transformations, multi-molecular chemical and biochemical transformations (Baianu, 1983,2004a). Previous applications of computer modeling, classical automata theory, and relational biology to molecular biology, oncogenesis and medicine were extensively reviewed and several important conclusions were reached regarding both the potential and limitations of the computation-assisted modeling of biological systems, and especially complex organisms such as Homo sapiens sapiens(Baianu,1987). Novel approaches to solving the realization problems of Relational Biology models in Complex System Biology are introduced in terms of natural transformations between functors of such molecular categories. Several applications of such natural transformations of functors were then presented to protein biosynthesis, embryogenesis and nuclear transplant experiments. Other possible realizations in Molecular Biology and Relational Biology of Organisms are here suggested in terms of quantum automata models of Quantum Genetics and Interactomics. Future developments of this novel approach are likely to also include: Fuzzy Relations in Biology and Epigenomics, Relational Biology modeling of Complex Immunological and Hormonal regulatory systems, n-categories and Topoi of Lukasiewicz Logic Algebras and Intuitionistic Logic (Heyting) Algebras for modeling nonlinear dynamics and cognitive processes in complex neural networks that are present in the human brain, as well as stochastic modeling of genetic networks in Lukasiewicz Logic Algebras
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