42,421 research outputs found
Analysis of Farthest Point Sampling for Approximating Geodesics in a Graph
A standard way to approximate the distance between any two vertices and
on a mesh is to compute, in the associated graph, a shortest path from
to that goes through one of sources, which are well-chosen vertices.
Precomputing the distance between each of the sources to all vertices of
the graph yields an efficient computation of approximate distances between any
two vertices. One standard method for choosing sources, which has been used
extensively and successfully for isometry-invariant surface processing, is the
so-called Farthest Point Sampling (FPS), which starts with a random vertex as
the first source, and iteratively selects the farthest vertex from the already
selected sources.
In this paper, we analyze the stretch factor of
approximate geodesics computed using FPS, which is the maximum, over all pairs
of distinct vertices, of their approximated distance over their geodesic
distance in the graph. We show that can be bounded in terms
of the minimal value of the stretch factor obtained using an
optimal placement of sources as , where is the ratio of the lengths of
the longest and the shortest edges of the graph. This provides some evidence
explaining why farthest point sampling has been used successfully for
isometry-invariant shape processing. Furthermore, we show that it is
NP-complete to find sources that minimize the stretch factor.Comment: 13 pages, 4 figure
De-aliasing Undersampled Volume Images for Visualization
We present and illustrate a new technique, Image Correlation Supersampling (ICS), for resampling volume data that are undersampled in one dimension. The resulting data satisfies the sampling theorem, and, therefore, many visualization algorithms that assume the theorem is satisfied can be applied to the data. Without the supersampling the visualization algorithms create artifacts due to aliasing. The assumptions made in developing the algorithm are often satisfied by data that is undersampled temporally. Through this supersampling we can completely characterize phenomena with measurements at a coarser temporal sampling rate than would otherwise be necessary. This can save acquisition time and storage space, permit the study of faster phenomena, and allow their study without introducing aliasing artifacts. The resampling technique relies on a priori knowledge of the measured phenomenon, and applies, in particular, to scalar concentration measurements of fluid flow. Because of the characteristics of fluid flow, an image deformation that takes each slice image to the next can be used to calculate intermediate slice images at arbitrarily fine spacing. We determine the deformation with an automatic, multi-resolution algorithm
Quantum Commuting Circuits and Complexity of Ising Partition Functions
Instantaneous quantum polynomial-time (IQP) computation is a class of quantum
computation consisting only of commuting two-qubit gates and is not universal
in the sense of standard quantum computation. Nevertheless, it has been shown
that if there is a classical algorithm that can simulate IQP efficiently, the
polynomial hierarchy (PH) collapses at the third level, which is highly
implausible. However, the origin of the classical intractability is still less
understood. Here we establish a relationship between IQP and computational
complexity of the partition functions of Ising models. We apply the established
relationship in two opposite directions. One direction is to find subclasses of
IQP that are classically efficiently simulatable in the strong sense, by using
exact solvability of certain types of Ising models. Another direction is
applying quantum computational complexity of IQP to investigate (im)possibility
of efficient classical approximations of Ising models with imaginary coupling
constants. Specifically, we show that there is no fully polynomial randomized
approximation scheme (FPRAS) for Ising models with almost all imaginary
coupling constants even on a planar graph of a bounded degree, unless the PH
collapses at the third level. Furthermore, we also show a multiplicative
approximation of such a class of Ising partition functions is at least as hard
as a multiplicative approximation for the output distribution of an arbitrary
quantum circuit.Comment: 36 pages, 5 figure
Estimation of fractal dimension for a class of Non-Gaussian stationary processes and fields
We present the asymptotic distribution theory for a class of increment-based
estimators of the fractal dimension of a random field of the form g{X(t)},
where g:R\to R is an unknown smooth function and X(t) is a real-valued
stationary Gaussian field on R^d, d=1 or 2, whose covariance function obeys a
power law at the origin. The relevant theoretical framework here is ``fixed
domain'' (or ``infill'') asymptotics. Surprisingly, the limit theory in this
non-Gaussian case is somewhat richer than in the Gaussian case (the latter is
recovered when g is affine), in part because estimators of the type considered
may have an asymptotic variance which is random in the limit. Broadly, when g
is smooth and nonaffine, three types of limit distributions can arise, types
(i), (ii) and (iii), say. Each type can be represented as a random integral.
More specifically, type (i) can be represented as the integral of a certain
random function with respect to Lebesgue measure; type (ii) can be represented
as the integral of a second random functio
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