25,957 research outputs found
Remote Sensing Information Sciences Research Group, Santa Barbara Information Sciences Research Group, year 3
Research continues to focus on improving the type, quantity, and quality of information which can be derived from remotely sensed data. The focus is on remote sensing and application for the Earth Observing System (Eos) and Space Station, including associated polar and co-orbiting platforms. The remote sensing research activities are being expanded, integrated, and extended into the areas of global science, georeferenced information systems, machine assissted information extraction from image data, and artificial intelligence. The accomplishments in these areas are examined
The Neuroscience Information Framework: A Data and Knowledge Environment for Neuroscience
With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience’s Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov, http://neurogateway.org, and other sites as they come on line
CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements
Information about user preferences plays a key role in automated decision
making. In many domains it is desirable to assess such preferences in a
qualitative rather than quantitative way. In this paper, we propose a
qualitative graphical representation of preferences that reflects conditional
dependence and independence of preference statements under a ceteris paribus
(all else being equal) interpretation. Such a representation is often compact
and arguably quite natural in many circumstances. We provide a formal semantics
for this model, and describe how the structure of the network can be exploited
in several inference tasks, such as determining whether one outcome dominates
(is preferred to) another, ordering a set outcomes according to the preference
relation, and constructing the best outcome subject to available evidence
SkyDOT (Sky Database for Objects in the Time Domain): A Virtual Observatory for Variability Studies at LANL
The mining of Virtual Observatories (VOs) is becoming a powerful new method
for discovery in astronomy. Here we report on the development of SkyDOT (Sky
Database for Objects in the Time domain), a new Virtual Observatory, which is
dedicated to the study of sky variability. The site will confederate a number
of massive variability surveys and enable exploration of the time domain in
astronomy. We discuss the architecture of the database and the functionality of
the user interface. An important aspect of SkyDOT is that it is continuously
updated in near real time so that users can access new observations in a timely
manner. The site will also utilize high level machine learning tools that will
allow sophisticated mining of the archive. Another key feature is the real time
data stream provided by RAPTOR (RAPid Telescopes for Optical Response), a new
sky monitoring experiment under construction at Los Alamos National Laboratory
(LANL).Comment: to appear in SPIE proceedings vol. 4846, 11 pages, 5 figure
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Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
The GalMer database: Galaxy Mergers in the Virtual Observatory
We present the GalMer database, a library of galaxy merger simulations, made
available to users through tools compatible with the Virtual Observatory (VO)
standards adapted specially for this theoretical database. To investigate the
physics of galaxy formation through hierarchical merging, it is necessary to
simulate galaxy interactions varying a large number of parameters:
morphological types, mass ratios, orbital configurations, etc. On one side,
these simulations have to be run in a cosmological context, able to provide a
large number of galaxy pairs, with boundary conditions given by the large-scale
simulations, on the other side the resolution has to be high enough at galaxy
scales, to provide realistic physics. The GalMer database is a library of
thousands simulations of galaxy mergers at moderate spatial resolution and it
is a compromise between the diversity of initial conditions and the details of
underlying physics. We provide all coordinates and data of simulated particles
in FITS binary tables. The main advantages of the database are VO access
interfaces and value-added services which allow users to compare the results of
the simulations directly to observations: stellar population modelling, dust
extinction, spectra, images, visualisation using dedicated VO tools. The GalMer
value-added services can be used as virtual telescope producing broadband
images, 1D spectra, 3D spectral datacubes, thus making our database oriented
towards the usage by observers. We present several examples of the GalMer
database scientific usage obtained from the analysis of simulations and
modelling their stellar population properties, including: (1) studies of the
star formation efficiency in interactions; (2) creation of old counter-rotating
components; (3) reshaping metallicity profiles in elliptical galaxies; (4)
orbital to internal angular momentum transfer; (5) reproducing observed colour
bimodality of galaxies.Comment: 15 pages, 11 figures, 10 tables accepted to A&A. Visualisation of
GalMer simulations, access to snapshot files and value-added tools described
in the paper are available at http://galmer.obspm.fr
Remote sensing information sciences research group
Research conducted under this grant was used to extend and expand existing remote sensing activities at the University of California, Santa Barbara in the areas of georeferenced information systems, matching assisted information extraction from image data and large spatial data bases, artificial intelligence, and vegetation analysis and modeling. The research thrusts during the past year are summarized. The projects are discussed in some detail
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