48,824 research outputs found
On the complexity of range searching among curves
Modern tracking technology has made the collection of large numbers of
densely sampled trajectories of moving objects widely available. We consider a
fundamental problem encountered when analysing such data: Given polygonal
curves in , preprocess into a data structure that answers
queries with a query curve and radius for the curves of that
have \Frechet distance at most to .
We initiate a comprehensive analysis of the space/query-time trade-off for
this data structuring problem. Our lower bounds imply that any data structure
in the pointer model model that achieves query time, where is
the output size, has to use roughly space in
the worst case, even if queries are mere points (for the discrete \Frechet
distance) or line segments (for the continuous \Frechet distance). More
importantly, we show that more complex queries and input curves lead to
additional logarithmic factors in the lower bound. Roughly speaking, the number
of logarithmic factors added is linear in the number of edges added to the
query and input curve complexity. This means that the space/query time
trade-off worsens by an exponential factor of input and query complexity. This
behaviour addresses an open question in the range searching literature: whether
it is possible to avoid the additional logarithmic factors in the space and
query time of a multilevel partition tree. We answer this question negatively.
On the positive side, we show we can build data structures for the \Frechet
distance by using semialgebraic range searching. Our solution for the discrete
\Frechet distance is in line with the lower bound, as the number of levels in
the data structure is , where denotes the maximal number of vertices
of a curve. For the continuous \Frechet distance, the number of levels
increases to
Speeding up neighborhood search in local Gaussian process prediction
Recent implementations of local approximate Gaussian process models have
pushed computational boundaries for non-linear, non-parametric prediction
problems, particularly when deployed as emulators for computer experiments.
Their flavor of spatially independent computation accommodates massive
parallelization, meaning that they can handle designs two or more orders of
magnitude larger than previously. However, accomplishing that feat can still
require massive supercomputing resources. Here we aim to ease that burden. We
study how predictive variance is reduced as local designs are built up for
prediction. We then observe how the exhaustive and discrete nature of an
important search subroutine involved in building such local designs may be
overly conservative. Rather, we suggest that searching the space radially,
i.e., continuously along rays emanating from the predictive location of
interest, is a far thriftier alternative. Our empirical work demonstrates that
ray-based search yields predictors with accuracy comparable to exhaustive
search, but in a fraction of the time - bringing a supercomputer implementation
back onto the desktop.Comment: 24 pages, 5 figures, 4 table
Review of the mathematical foundations of data fusion techniques in surface metrology
The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed
HyperVAE: A Minimum Description Length Variational Hyper-Encoding Network
We propose a framework called HyperVAE for encoding distributions of
distributions. When a target distribution is modeled by a VAE, its neural
network parameters \theta is drawn from a distribution p(\theta) which is
modeled by a hyper-level VAE. We propose a variational inference using Gaussian
mixture models to implicitly encode the parameters \theta into a low
dimensional Gaussian distribution. Given a target distribution, we predict the
posterior distribution of the latent code, then use a matrix-network decoder to
generate a posterior distribution q(\theta). HyperVAE can encode the parameters
\theta in full in contrast to common hyper-networks practices, which generate
only the scale and bias vectors as target-network parameters. Thus HyperVAE
preserves much more information about the model for each task in the latent
space. We discuss HyperVAE using the minimum description length (MDL) principle
and show that it helps HyperVAE to generalize. We evaluate HyperVAE in density
estimation tasks, outlier detection and discovery of novel design classes,
demonstrating its efficacy
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