382,572 research outputs found
The birth of the strong components
Random directed graphs undergo a phase transition around the point
, and the width of the transition window has been known since the
works of Luczak and Seierstad. They have established that as
when , the asymptotic probability that the strongly
connected components of a random directed graph are only cycles and single
vertices decreases from 1 to 0 as goes from to .
By using techniques from analytic combinatorics, we establish the exact
limiting value of this probability as a function of and provide more
properties of the structure of a random digraph around, below and above its
transition point. We obtain the limiting probability that a random digraph is
acyclic and the probability that it has one strongly connected complex
component with a given difference between the number of edges and vertices
(called excess). Our result can be extended to the case of several complex
components with given excesses as well in the whole range of sparse digraphs.
Our study is based on a general symbolic method which can deal with a great
variety of possible digraph families, and a version of the saddle-point method
which can be systematically applied to the complex contour integrals appearing
from the symbolic method. While the technically easiest model is the model of
random multidigraphs, in which multiple edges are allowed, and where edge
multiplicities are sampled independently according to a Poisson distribution
with a fixed parameter , we also show how to systematically approach the
family of simple digraphs, where multiple edges are forbidden, and where
2-cycles are either allowed or not.
Our theoretical predictions are supported by numerical simulations, and we
provide tables of numerical values for the integrals of Airy functions that
appear in this study.Comment: 62 pages, 12 figures, 6 tables. Supplementary computer algebra
computations available at https://gitlab.com/vit.north/strong-components-au
Semiparametric Regression for Periodic Longitudinal Hormone Data from Multiple Menstrual Cycles
We consider Semiparametric regression for periodic longitudinal data. Parametric fixed effects are used to model the covariate effects and a periodic nonparametric smooth function is used to model the time effect. The within–subject correlation is modeled using subject-specific random effects and a random stochastic process with a periodic variance function. We use maximum penalized likelihood to estimate the regression coefficients and the periodic nonparametric time function, whose estimator is shown to be a periodic cubic smoothing spline. We use restricted maximum likelihood to simultaneously estimate the smoothing parameter and the variance components. We show that all model parameters can be easily obtained by fitting a linear mixed model. A common problem in the analysis of longitudinal data is to compare the time profiles of two groups, e.g., between treatment and placebo. We develop a scaled chi-squared test for the equality of two nonparametric time functions. The proposed model and the test are illustrated by analyzing hormone data collected during two consecutive menstrual cycles and their performance is evaluated through simulations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65472/1/j.0006-341X.2000.00031.x.pd
Probabilistic evaluation of uncertainties and risks in aerospace components
This paper summarizes a methodology developed at NASA Lewis Research Center which computationally simulates the structural, material, and load uncertainties associated with Space Shuttle Main Engine (SSME) components. The methodology was applied to evaluate the scatter in static, buckling, dynamic, fatigue, and damage behavior of the SSME turbo pump blade. Also calculated are the probability densities of typical critical blade responses, such as effective stress, natural frequency, damage initiation, most probable damage path, etc. Risk assessments were performed for different failure modes, and the effect of material degradation on the fatigue and damage behaviors of a blade were calculated using a multi-factor interaction equation. Failure probabilities for different fatigue cycles were computed and the uncertainties associated with damage initiation and damage propagation due to different load cycle were quantified. Evaluations on the effects of mistuned blades on a rotor were made; uncertainties in the excitation frequency were found to significantly amplify the blade responses of a mistuned rotor. The effects of the number of blades on a rotor were studied. The autocorrelation function of displacements and the probability density function of the first passage time for deterministic and random barriers for structures subjected to random processes also were computed. A brief discussion was included on the future direction of probabilistic structural analysis
Random curves on surfaces induced from the Laplacian determinant
We define natural probability measures on cycle-rooted spanning forests
(CRSFs) on graphs embedded on a surface with a Riemannian metric. These
measures arise from the Laplacian determinant and depend on the choice of a
unitary connection on the tangent bundle to the surface.
We show that, for a sequence of graphs conformally approximating the
surface, the measures on CRSFs of converge and give a limiting
probability measure on finite multicurves (finite collections of pairwise
disjoint simple closed curves) on the surface, independent of the approximating
sequence.
Wilson's algorithm for generating spanning trees on a graph generalizes to a
cycle-popping algorithm for generating CRSFs for a general family of weights on
the cycles. We use this to sample the above measures. The sampling algorithm,
which relates these measures to the loop-erased random walk, is also used to
prove tightness of the sequence of measures, a key step in the proof of their
convergence.
We set the framework for the study of these probability measures and their
scaling limits and state some of their properties
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