Galaxy Filament Detection using the Quality Candy Model- Intro

A joint project between INRIA and the French Riviera Observatory proposes to apply
a marked point process to detect a galaxy filament network. The method is based
on a model initially developed for road network extraction in remotely sensed images.

The Astro-Wise System: A Federated Information Accumulator for Astronomy- III

The core of the system exploits
three properties in database
environment. First, we apply the
principle of inheritance using
Object Oriented Programming
(Python), where all Astro-Wise
objects inherit key properties for
database access, such as persistency
of attributes. Second, the
linking (associations or references)
between instances of
objects in the database is completely
maintained, and for each

bit of information, it is possible to trace
those bits of information that were used
to obtain it. Third, each step, and the
inputs used for it, is kept within the
system. The database grows constantly
through the addition of new information
or improvements made to existing information.
All system components are distributed
over Europe, enabling research groups to
collaborate on shared projects.
Knowledge added by one group is
immediately accessible by others via a
Web portal, which includes data
viewing, quality labelling and computeservices
(see links). Currently,
researchers use the Astro-Wise system
with 10 Tbyte of astronomical images.
Hundreds of Tbytes of data will start
entering the system when the
OmegaCAM panoramic camera starts
operations in Chile. This camera is dedicated
to various large surveys using the
Astro-Wise system.
Astro-Wise coordinator OmegaCENNOVA
is collaborating with the LOFAR
consortium and CWI to explore usage of
the Astro-Wise system for LOFAR, the
next generation Low Frequency Array of
radio telescopes, which is being built in
the Netherlands and Germany. Astro-
Wise can also be applied to other fields
of science. The object-oriented use of
the database allows for classes of objects
dealing with arbitrary forms of digitized
observational data. Scans of cultural heritage,
DNA sequences, data from highenergy
particle collisions or financial
markets can be processed using similar
principles to the images of the sky.

The Astro-Wise System: A Federated Information Accumulator for Astronomy- II

This improvement is achieved by:
• emphasis on project management;
enforcing a global data acquisition and
processing model, while retaining
flexibility
• translating the data model to an object
model, with full registration of all
dependencies
• storing all I/O of the project in a single,
distributed database, containing all
metadata describing the bulk data (eg
images) and derived results in catalogue
form (eg lists of celestial
sources).
• connecting to the database a federated
file server that stores hundreds of
Terabytes of bulk data
• an own compute-GRID which sends
jobs (including clients) to single nodes
or parallel clusters, which then request
data from the distributed database.
The database with all metadata and catalogues
provides the infrastructure to
develop tools for a variety of purposes.
These include rapid trend analysis of
data, complex queries and fast hunting
for ‘needles in the haystack’ of Terabytesized
catalogues. Thus, the system provides
the user with fully integrated,
transparent access to all stages of the
data processing and thereby allows the
data to be reprocessed and the system to
be improved and expanded.
For a given project/instrument, the
system initially starts in a naive, ‘quick
look’ mode, which gradually improves
as various researchers add refined information
to the system under the supervision
of project leaders. Approved calibration
modifications automatically
become public, beyond the project
boundaries. A mechanism for quality
control is implemented which allows for
changes due to one of:
•true physical changes of parameter
values
•improvements in encoded
methods, or
•improved insight in either of
these.

The Astro-Wise System: A Federated Information Accumulator for Astronomy- I

Much of modern research involves the
accumulation of huge amounts of digitized
data. The analysis of this data by
distributed communities represents a significant
challenge to project management
and ICT implementation, and is
relevant to fields as diverse as biology,
physics, astronomy, economics and cultural
heritage projects. Furthermore, the
projects are often global efforts requiring
collaborators in many places to share,
validate and combine processed data and
derived results. It is therefore necessary
to develop more efficient data lineage,
mining and analysis systems to allow
researchers to search intelligently
through previously unmanageable volumes
of data.
The Astro-Wise consortium has developed
an information system to meet these
challenges for wide-field imaging in
astronomy. The Astro-Wise consortium is
a partnership between OmegaCENNOVA/
Kapteyn Institute (Groningen,
The Netherlands; coordinator),
Osservatorio Astronomico di
Capodimonte (Naples, Italy),
Terapix at IAP (Paris, France),
ESO, Universitäts-Sternwarte &
Max-Planck Institut für
Extraterrestrische Physik
(Munich, Germany).
Large data projects in highenergy
physics, space missions
and astronomy typically push
data through various platforms
in an irreversible way (eg a
TIER node setting). In such a situation,
the end user has little or
no influence on what happens
upstream. This ‘classical’
paradigm is characterized by
fixed ‘releases’ of homogeneous,
well-documented data
products. In contrast, the Astro-
Wise system allows the end user to trace
the data product, following all its dependencies
up to the raw observational data
and, if necessary, to re-derive the result
with better calibration data and/or
improved methods.

The Astro-Wise System: A Federated Information Accumulator for Astronomy- Intro

The progress of astronomy is about to hit a wall in terms of the processing, mining
and interpretation of huge datasets. The Astro-Wise consortium has designed and
implemented a fully scalable and distributed information system to overcome this
problem for wide-field imaging. The same principles can be applied to other sciences.

A New Approach for Advanced Life-Support Systems Control -III

The proposed MAS is organized in a layered
structure. In the first layer, agents will be in
charge of the planning and coordination of
necessary tasks and interaction with the crew,
requiring their intervention only when
mandatory. The use of Expert Systems in this
layer will allow actions to be planned in a systematic
and efficient manner. For example,
achieving an optimal harvest depends not
only on the maturity of the crop but also on
food requirements and storage capability.
In the second layer, agents will control
the execution of these tasks, interfacing
with the sensors and actuators of each
LSS subsystem and notifying relevant
process data, progress information or
unexpected events to the planner layer.
This layer will be heterogeneous in the
sense of implementation, but for each
process control an agent will be on top,
permitting a standard communication
with the rest of the system.
An especially critical problem to be
solved is the amount of supervisory
information that this system will generate.
Agents specializing in information
synthesis are needed to process this
information before communicating it to
the crew. Automated supervision of the
processes will be required, providing
only relevant information and requiring
crew attention only when mandatory.

A New Approach for Advanced Life-Support Systems Control- II

The goal of BLSS is very ambitious, as
the fact of using living beings generates
a problem in the determinism of the
system. These systems are highly nonlinear,
with a high level of uncertainty in
their behaviour, making it impossible to
perform a complete analytical modelling
of the processes. It is therefore necessary
to develop, in parallel with the biochemical
and physiological studies, new
approaches to system control (Figure 1).
Model-based control systems will not be
successful since they cannot deal with
incomplete or inaccurate information.
For instance, the maturity level of the
crop must be assessed indirectly using
several variables (atmosphere gas composition,
plant colour, biomass, time
from seeding etc). In addition, these variables
will depend on a great number of
factors, making predictability very poor.
New control-system architecture to cope
with these problems can be effectively
implemented using a Multi-Agent
System (MAS). This approach allows
the problem to be broken down into
small parts, each dealing with specific
tasks but in a coordinated manner, performing
as an organization with a
common objective and sharing a set of
rules. In addition, designing this system
as a multi-agent network will allow specific
control solutions to be applied to
each part as needed. The different types
of controllers will become encapsulated
in the agent structure and only relevant
information will be shared to enable
monitoring and global control. Another
benefit will be the reconfiguration capability,
in cases of, for instance, failure of
part of the system or the need to adapt
the system to new objectives.