192,849 research outputs found
Simple structures axiomatized by almost sure theories
In this article we give a classification of the binary, simple,
-categorical structures with SU-rank 1 and trivial pregeometry. This is
done both by showing that they satisfy certain extension properties, but also
by noting that they may be approximated by the almost sure theory of some sets
of finite structures equipped with a probability measure. This study give
results about general almost sure theories, but also considers certain
attributes which, if they are almost surely true, generate almost sure theories
with very specific properties such as -stability or strong minimality.Comment: 27 page
Moment-Based Spectral Analysis of Random Graphs with Given Expected Degrees
In this paper, we analyze the limiting spectral distribution of the adjacency
matrix of a random graph ensemble, proposed by Chung and Lu, in which a given
expected degree sequence
is prescribed on the ensemble. Let if there is an edge
between the nodes and zero otherwise, and consider the normalized
random adjacency matrix of the graph ensemble: . The empirical spectral distribution
of denoted by is the empirical
measure putting a mass at each of the real eigenvalues of the
symmetric matrix . Under some technical conditions on the
expected degree sequence, we show that with probability one,
converges weakly to a deterministic
distribution . Furthermore, we fully characterize this
distribution by providing explicit expressions for the moments of
. We apply our results to well-known degree distributions,
such as power-law and exponential. The asymptotic expressions of the spectral
moments in each case provide significant insights about the bulk behavior of
the eigenvalue spectrum
From almost sure local regularity to almost sure Hausdorff dimension for Gaussian fields
Fine regularity of stochastic processes is usually measured in a local way by
local H\"older exponents and in a global way by fractal dimensions. Following a
previous work of Adler, we connect these two concepts for multiparameter
Gaussian random fields. More precisely, we prove that almost surely the
Hausdorff dimensions of the range and the graph in any ball are
bounded from above using the local H\"older exponent at . We define the
deterministic local sub-exponent of Gaussian processes, which allows to obtain
an almost sure lower bound for these dimensions. Moreover, the Hausdorff
dimensions of the sample path on an open interval are controlled almost surely
by the minimum of the local exponents.
Then, we apply these generic results to the cases of the multiparameter
fractional Brownian motion, the multifractional Brownian motion whose
regularity function is irregular and the generalized Weierstrass function,
whose Hausdorff dimensions were unknown so far.Comment: 28 page
When are Stochastic Transition Systems Tameable?
A decade ago, Abdulla, Ben Henda and Mayr introduced the elegant concept of
decisiveness for denumerable Markov chains [1]. Roughly speaking, decisiveness
allows one to lift most good properties from finite Markov chains to
denumerable ones, and therefore to adapt existing verification algorithms to
infinite-state models. Decisive Markov chains however do not encompass
stochastic real-time systems, and general stochastic transition systems (STSs
for short) are needed. In this article, we provide a framework to perform both
the qualitative and the quantitative analysis of STSs. First, we define various
notions of decisiveness (inherited from [1]), notions of fairness and of
attractors for STSs, and make explicit the relationships between them. Then, we
define a notion of abstraction, together with natural concepts of soundness and
completeness, and we give general transfer properties, which will be central to
several verification algorithms on STSs. We further design a generic
construction which will be useful for the analysis of {\omega}-regular
properties, when a finite attractor exists, either in the system (if it is
denumerable), or in a sound denumerable abstraction of the system. We next
provide algorithms for qualitative model-checking, and generic approximation
procedures for quantitative model-checking. Finally, we instantiate our
framework with stochastic timed automata (STA), generalized semi-Markov
processes (GSMPs) and stochastic time Petri nets (STPNs), three models
combining dense-time and probabilities. This allows us to derive decidability
and approximability results for the verification of these models. Some of these
results were known from the literature, but our generic approach permits to
view them in a unified framework, and to obtain them with less effort. We also
derive interesting new approximability results for STA, GSMPs and STPNs.Comment: 77 page
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