254,967 research outputs found
Artificial Intelligence Approach to the Determination of Physical Properties of Eclipsing Binaries. I. The EBAI Project
Achieving maximum scientific results from the overwhelming volume of
astronomical data to be acquired over the next few decades will demand novel,
fully automatic methods of data analysis. Artificial intelligence approaches
hold great promise in contributing to this goal. Here we apply neural network
learning technology to the specific domain of eclipsing binary (EB) stars, of
which only some hundreds have been rigorously analyzed, but whose numbers will
reach millions in a decade. Well-analyzed EBs are a prime source of
astrophysical information whose growth rate is at present limited by the need
for human interaction with each EB data-set, principally in determining a
starting solution for subsequent rigorous analysis. We describe the artificial
neural network (ANN) approach which is able to surmount this human bottleneck
and permit EB-based astrophysical information to keep pace with future data
rates. The ANN, following training on a sample of 33,235 model light curves,
outputs a set of approximate model parameters (T2/T1, (R1+R2)/a, e sin(omega),
e cos(omega), and sin i) for each input light curve data-set. The whole sample
is processed in just a few seconds on a single 2GHz CPU. The obtained
parameters can then be readily passed to sophisticated modeling engines. We
also describe a novel method polyfit for pre-processing observational light
curves before inputting their data to the ANN and present the results and
analysis of testing the approach on synthetic data and on real data including
fifty binaries from the Catalog and Atlas of Eclipsing Binaries (CALEB)
database and 2580 light curves from OGLE survey data. [abridged]Comment: 52 pages, accepted to Ap
Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database
In this paper we present a novel architecture for storing visual data.
Effective storing, browsing and searching collections of images is one of the
most important challenges of computer science. The design of architecture for
storing such data requires a set of tools and frameworks such as SQL database
management systems and service-oriented frameworks. The proposed solution is
based on a multi-layer architecture, which allows to replace any component
without recompilation of other components. The approach contains five
components, i.e. Model, Base Engine, Concrete Engine, CBIR service and
Presentation. They were based on two well-known design patterns: Dependency
Injection and Inverse of Control. For experimental purposes we implemented the
SURF local interest point detector as a feature extractor and -means
clustering as indexer. The presented architecture is intended for content-based
retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial
Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan
Parameterized Algorithmics for Computational Social Choice: Nine Research Challenges
Computational Social Choice is an interdisciplinary research area involving
Economics, Political Science, and Social Science on the one side, and
Mathematics and Computer Science (including Artificial Intelligence and
Multiagent Systems) on the other side. Typical computational problems studied
in this field include the vulnerability of voting procedures against attacks,
or preference aggregation in multi-agent systems. Parameterized Algorithmics is
a subfield of Theoretical Computer Science seeking to exploit meaningful
problem-specific parameters in order to identify tractable special cases of in
general computationally hard problems. In this paper, we propose nine of our
favorite research challenges concerning the parameterized complexity of
problems appearing in this context
Beneficial Artificial Intelligence Coordination by means of a Value Sensitive Design Approach
This paper argues that the Value Sensitive Design (VSD) methodology provides a principled approach to embedding common values in to AI systems both early and throughout the design process. To do so, it draws on an important case study: the evidence and final report of the UK Select Committee on Artificial Intelligence. This empirical investigation shows that the different and often disparate stakeholder groups that are implicated in AI design and use share some common values that can be used to further strengthen design coordination efforts. VSD is shown to be both able to distill these common values as well as provide a framework for stakeholder coordination
Variations on a Theme: A Bibliography on Approaches to Theorem Proving Inspired From Satchmo
This articles is a structured bibliography on theorem provers,
approaches to theorem proving, and theorem proving applications inspired
from Satchmo, the model generation theorem prover developed
in the mid 80es of the 20th century at ECRC, the European Computer-
Industry Research Centre. Note that the bibliography given in this article
is not exhaustive
Formulating Consciousness: A Comparative Analysis of Searleās and Dennettās Theory of Consciousness
This research will argue about which theory of mind between
Searleās and Dennettās can better explain human consciousness. Initially,
distinctions between dualism and materialism will be discussed ranging from
substance dualism, property dualism, physicalism, and functionalism. In this
part, the main issue that is tackled in various theories of mind is revealed. It
is the missing connection between input stimulus (neuronal reactions) and
behavioral disposition: consciousness. Then, the discussion will be more
specific on Searleās biological naturalism and Dennettās multiple drafts
model as the two attempted to answer the issue. The differences between
them will be highlighted and will be analyzed according to their relation to
their roots: dualism and materialism. The two theories will be examined on
how each answer the questions on consciousness
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