32,451 research outputs found
Spectral unmixing of Multispectral Lidar signals
In this paper, we present a Bayesian approach for spectral unmixing of
multispectral Lidar (MSL) data associated with surface reflection from targeted
surfaces composed of several known materials. The problem addressed is the
estimation of the positions and area distribution of each material. In the
Bayesian framework, appropriate prior distributions are assigned to the unknown
model parameters and a Markov chain Monte Carlo method is used to sample the
resulting posterior distribution. The performance of the proposed algorithm is
evaluated using synthetic MSL signals, for which single and multi-layered
models are derived. To evaluate the expected estimation performance associated
with MSL signal analysis, a Cramer-Rao lower bound associated with model
considered is also derived, and compared with the experimental data. Both the
theoretical lower bound and the experimental analysis will be of primary
assistance in future instrument design
Built to Last or Built Too Fast? Evaluating Prediction Models for Build Times
Automated builds are integral to the Continuous Integration (CI) software
development practice. In CI, developers are encouraged to integrate early and
often. However, long build times can be an issue when integrations are
frequent. This research focuses on finding a balance between integrating often
and keeping developers productive. We propose and analyze models that can
predict the build time of a job. Such models can help developers to better
manage their time and tasks. Also, project managers can explore different
factors to determine the best setup for a build job that will keep the build
wait time to an acceptable level. Software organizations transitioning to CI
practices can use the predictive models to anticipate build times before CI is
implemented. The research community can modify our predictive models to further
understand the factors and relationships affecting build times.Comment: 4 paged version published in the Proceedings of the IEEE/ACM 14th
International Conference on Mining Software Repositories (MSR) Pages 487-490.
MSR 201
Massively-Parallel Break Detection for Satellite Data
The field of remote sensing is nowadays faced with huge amounts of data.
While this offers a variety of exciting research opportunities, it also yields
significant challenges regarding both computation time and space requirements.
In practice, the sheer data volumes render existing approaches too slow for
processing and analyzing all the available data. This work aims at accelerating
BFAST, one of the state-of-the-art methods for break detection given satellite
image time series. In particular, we propose a massively-parallel
implementation for BFAST that can effectively make use of modern parallel
compute devices such as GPUs. Our experimental evaluation shows that the
proposed GPU implementation is up to four orders of magnitude faster than the
existing publicly available implementation and up to ten times faster than a
corresponding multi-threaded CPU execution. The dramatic decrease in running
time renders the analysis of significantly larger datasets possible in seconds
or minutes instead of hours or days. We demonstrate the practical benefits of
our implementations given both artificial and real datasets.Comment: 10 page
A System for Accessible Artificial Intelligence
While artificial intelligence (AI) has become widespread, many commercial AI
systems are not yet accessible to individual researchers nor the general public
due to the deep knowledge of the systems required to use them. We believe that
AI has matured to the point where it should be an accessible technology for
everyone. We present an ongoing project whose ultimate goal is to deliver an
open source, user-friendly AI system that is specialized for machine learning
analysis of complex data in the biomedical and health care domains. We discuss
how genetic programming can aid in this endeavor, and highlight specific
examples where genetic programming has automated machine learning analyses in
previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and
Practice 2017 worksho
Palgol: A High-Level DSL for Vertex-Centric Graph Processing with Remote Data Access
Pregel is a popular distributed computing model for dealing with large-scale
graphs. However, it can be tricky to implement graph algorithms correctly and
efficiently in Pregel's vertex-centric model, especially when the algorithm has
multiple computation stages, complicated data dependencies, or even
communication over dynamic internal data structures. Some domain-specific
languages (DSLs) have been proposed to provide more intuitive ways to implement
graph algorithms, but due to the lack of support for remote access --- reading
or writing attributes of other vertices through references --- they cannot
handle the above mentioned dynamic communication, causing a class of Pregel
algorithms with fast convergence impossible to implement.
To address this problem, we design and implement Palgol, a more declarative
and powerful DSL which supports remote access. In particular, programmers can
use a more declarative syntax called chain access to naturally specify dynamic
communication as if directly reading data on arbitrary remote vertices. By
analyzing the logic patterns of chain access, we provide a novel algorithm for
compiling Palgol programs to efficient Pregel code. We demonstrate the power of
Palgol by using it to implement several practical Pregel algorithms, and the
evaluation result shows that the efficiency of Palgol is comparable with that
of hand-written code.Comment: 12 pages, 10 figures, extended version of APLAS 2017 pape
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