3,408 research outputs found
Inferring an Indeterminate String from a Prefix Graph
An \itbf{indeterminate string} (or, more simply, just a \itbf{string}) \s{x}
= \s{x}[1..n] on an alphabet is a sequence of nonempty subsets of
. We say that \s{x}[i_1] and \s{x}[i_2] \itbf{match} (written
\s{x}[i_1] \match \s{x}[i_2]) if and only if \s{x}[i_1] \cap \s{x}[i_2] \ne
\emptyset. A \itbf{feasible array} is an array \s{y} = \s{y}[1..n] of
integers such that \s{y}[1] = n and for every , \s{y}[i] \in
0..n\- i\+ 1. A \itbf{prefix table} of a string \s{x} is an array \s{\pi} =
\s{\pi}[1..n] of integers such that, for every , \s{\pi}[i] = j
if and only if \s{x}[i..i\+ j\- 1] is the longest substring at position
of \s{x} that matches a prefix of \s{x}. It is known from \cite{CRSW13} that
every feasible array is a prefix table of some indetermintate string. A
\itbf{prefix graph} \mathcal{P} = \mathcal{P}_{\s{y}} is a labelled simple
graph whose structure is determined by a feasible array \s{y}. In this paper we
show, given a feasible array \s{y}, how to use \mathcal{P}_{\s{y}} to
construct a lexicographically least indeterminate string on a minimum alphabet
whose prefix table \s{\pi} = \s{y}.Comment: 13 pages, 1 figur
Computing Covers Using Prefix Tables
An \emph{indeterminate string} on an alphabet is a
sequence of nonempty subsets of ; is said to be \emph{regular} if
every subset is of size one. A proper substring of regular is said to
be a \emph{cover} of iff for every , an occurrence of in
includes . The \emph{cover array} of is
an integer array such that is the longest cover of .
Fifteen years ago a complex, though nevertheless linear-time, algorithm was
proposed to compute the cover array of regular based on prior computation
of the border array of . In this paper we first describe a linear-time
algorithm to compute the cover array of regular string based on the prefix
table of . We then extend this result to indeterminate strings.Comment: 14 pages, 1 figur
Naming the largest number: Exploring the boundary between mathematics and the philosophy of mathematics
What is the largest number accessible to the human imagination? The question
is neither entirely mathematical nor entirely philosophical. Mathematical
formulations of the problem fall into two classes: those that fail to fully
capture the spirit of the problem, and those that turn it back into a
philosophical problem
MODELLING EXPECTATIONS WITH GENEFER- AN ARTIFICIAL INTELLIGENCE APPROACH
Economic modelling of financial markets means to model highly complex systems in which expectations can be the dominant driving forces. Therefore it is necessary to focus on how agents form their expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. AgentsÆ bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule-Bases. E. g. if a single agent believes the exchange rate is determined by a set of possible inputs and is asked to put their relationship in words his answer will probably reveal a fuzzy nature like: "IF the inflation rate in the EURO-Zone is low and the GDP growth rate is larger than in the US THEN the EURO will rise against the USD". æLowÆ and ælargerÆ are fuzzy terms which give a gradual linguistic meaning to crisp intervalls in the respective universes of discourse. In order to learn a Fuzzy Fuzzy Rule base from examples we introduce Genetic Algorithms and Artificial Neural Networks as learning operators. These examples can either be empirical data or originate from an economic simulation model. The software GENEFER (GEnetic NEural Fuzzy ExplorER) has been developed for designing such a Fuzzy Rule Base. The design process is modular and comprises Input Identification, Fuzzification, Rule-Base Generating and Rule-Base Tuning. The two latter steps make use of genetic and neural learning algorithms for optimizing the Fuzzy Rule-Base.
On a symbolic representation of non-central Wishart random matrices with applications
By using a symbolic method, known in the literature as the classical umbral
calculus, the trace of a non-central Wishart random matrix is represented as
the convolution of the trace of its central component and of a formal variable
involving traces of its non-centrality matrix. Thanks to this representation,
the moments of this random matrix are proved to be a Sheffer polynomial
sequence, allowing us to recover several properties. The multivariate symbolic
method generalizes the employment of Sheffer representation and a closed form
formula for computing joint moments and cumulants (also normalized) is given.
By using this closed form formula and a combinatorial device, known in the
literature as necklace, an efficient algorithm for their computations is set
up. Applications are given to the computation of permanents as well as to the
characterization of inherited estimators of cumulants, which turn useful in
dealing with minors of non-central Wishart random matrices. An asymptotic
approximation of generalized moments involving free probability is proposed.Comment: Journal of Multivariate Analysis (2014
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