201,410 research outputs found
Enabling adaptive scientific workflows via trigger detection
Next generation architectures necessitate a shift away from traditional
workflows in which the simulation state is saved at prescribed frequencies for
post-processing analysis. While the need to shift to in~situ workflows has been
acknowledged for some time, much of the current research is focused on static
workflows, where the analysis that would have been done as a post-process is
performed concurrently with the simulation at user-prescribed frequencies.
Recently, research efforts are striving to enable adaptive workflows, in which
the frequency, composition, and execution of computational and data
manipulation steps dynamically depend on the state of the simulation. Adapting
the workflow to the state of simulation in such a data-driven fashion puts
extremely strict efficiency requirements on the analysis capabilities that are
used to identify the transitions in the workflow. In this paper we build upon
earlier work on trigger detection using sublinear techniques to drive adaptive
workflows. Here we propose a methodology to detect the time when sudden heat
release occurs in simulations of turbulent combustion. Our proposed method
provides an alternative metric that can be used along with our former metric to
increase the robustness of trigger detection. We show the effectiveness of our
metric empirically for predicting heat release for two use cases.Comment: arXiv admin note: substantial text overlap with arXiv:1506.0825
Algorithms and Adaptivity Gaps for Stochastic k-TSP
Given a metric and a , the classic
\textsf{k-TSP} problem is to find a tour originating at the
of minimum length that visits at least nodes in . In this work,
motivated by applications where the input to an optimization problem is
uncertain, we study two stochastic versions of \textsf{k-TSP}.
In Stoch-Reward -TSP, originally defined by Ene-Nagarajan-Saket [ENS17],
each vertex in the given metric contains a stochastic reward .
The goal is to adaptively find a tour of minimum expected length that collects
at least reward ; here "adaptively" means our next decision may depend on
previous outcomes. Ene et al. give an -approximation adaptive
algorithm for this problem, and left open if there is an -approximation
algorithm. We totally resolve their open question and even give an
-approximation \emph{non-adaptive} algorithm for this problem.
We also introduce and obtain similar results for the Stoch-Cost -TSP
problem. In this problem each vertex has a stochastic cost , and the
goal is to visit and select at least vertices to minimize the expected
\emph{sum} of tour length and cost of selected vertices. This problem
generalizes the Price of Information framework [Singla18] from deterministic
probing costs to metric probing costs.
Our techniques are based on two crucial ideas: "repetitions" and "critical
scaling". We show using Freedman's and Jogdeo-Samuels' inequalities that for
our problems, if we truncate the random variables at an ideal threshold and
repeat, then their expected values form a good surrogate. Unfortunately, this
ideal threshold is adaptive as it depends on how far we are from achieving our
target , so we truncate at various different scales and identify a
"critical" scale.Comment: ITCS 202
Perceptual musical similarity metric learning with graph neural networks
Sound retrieval for assisted music composition depends on evaluating similarity between musical instrument sounds, which is partly influenced by playing techniques. Previous methods utilizing Euclidean nearest neighbours over acoustic features show some limitations in retrieving sounds sharing equivalent timbral properties, but potentially generated using a different instrument, playing technique, pitch or dynamic. In this paper, we present a metric learning system designed to approximate human similarity judgments between extended musical playing techniques using graph neural networks. Such structure is a natural candidate for solving similarity retrieval tasks, yet have seen little application in modelling perceptual music similarity. We optimize a Graph Convolutional Network (GCN) over acoustic features via a proxy metric learning loss to learn embeddings that reflect perceptual similarities. Specifically, we construct the graph's adjacency matrix from the acoustic data manifold with an example-wise adaptive k-nearest neighbourhood graph: Adaptive Neighbourhood Graph Neural Network (AN-GNN). Our approach achieves 96.4% retrieval accuracy compared to 38.5% with a Euclidean metric and 86.0% with a multilayer perceptron (MLP), while effectively considering retrievals from distinct playing techniques to the query example
Quality Adaptive Least Squares Trained Filters for Video Compression Artifacts Removal Using a No-reference Block Visibility Metric
Compression artifacts removal is a challenging problem because videos can be compressed at different qualities. In this paper, a least squares approach that is self-adaptive to the visual quality of the input sequence is proposed. For compression artifacts, the visual quality of an image is measured by a no-reference block visibility metric. According to the blockiness visibility of an input image, an appropriate set of filter coefficients that are trained beforehand is selected for optimally removing coding artifacts and reconstructing object details. The performance of the proposed algorithm is evaluated on a variety of sequences compressed at different qualities in comparison to several other deblocking techniques. The proposed method outperforms the others significantly both objectively and subjectively
Image Reconstruction with Analytical Point Spread Functions
The image degradation produced by atmospheric turbulence and optical
aberrations is usually alleviated using post-facto image reconstruction
techniques, even when observing with adaptive optics systems. These techniques
rely on the development of the wavefront using Zernike functions and the
non-linear optimization of a certain metric. The resulting optimization
procedure is computationally heavy. Our aim is to alleviate this
computationally burden. To this aim, we generalize the recently developed
extended Zernike-Nijboer theory to carry out the analytical integration of the
Fresnel integral and present a natural basis set for the development of the
point spread function in case the wavefront is described using Zernike
functions. We present a linear expansion of the point spread function in terms
of analytic functions which, additionally, takes defocusing into account in a
natural way. This expansion is used to develop a very fast phase-diversity
reconstruction technique which is demonstrated through some applications. This
suggest that the linear expansion of the point spread function can be applied
to accelerate other reconstruction techniques in use presently and based on
blind deconvolution.Comment: 10 pages, 4 figures, accepted for publication in Astronomy &
Astrophysic
- …