43,592 research outputs found
Fitness landscape of the cellular automata majority problem: View from the Olympus
In this paper we study cellular automata (CAs) that perform the computational
Majority task. This task is a good example of what the phenomenon of emergence
in complex systems is. We take an interest in the reasons that make this
particular fitness landscape a difficult one. The first goal is to study the
landscape as such, and thus it is ideally independent from the actual
heuristics used to search the space. However, a second goal is to understand
the features a good search technique for this particular problem space should
possess. We statistically quantify in various ways the degree of difficulty of
searching this landscape. Due to neutrality, investigations based on sampling
techniques on the whole landscape are difficult to conduct. So, we go exploring
the landscape from the top. Although it has been proved that no CA can perform
the task perfectly, several efficient CAs for this task have been found.
Exploiting similarities between these CAs and symmetries in the landscape, we
define the Olympus landscape which is regarded as the ''heavenly home'' of the
best local optima known (blok). Then we measure several properties of this
subspace. Although it is easier to find relevant CAs in this subspace than in
the overall landscape, there are structural reasons that prevent a searcher
from finding overfitted CAs in the Olympus. Finally, we study dynamics and
performance of genetic algorithms on the Olympus in order to confirm our
analysis and to find efficient CAs for the Majority problem with low
computational cost
From Molecular Cores to Planet-forming Disks: An SIRTF Legacy Program
Crucial steps in the formation of stars and planets can be studied only at mid‐ to far‐infrared wavelengths, where the Space Infrared Telescope (SIRTF) provides an unprecedented improvement in sensitivity. We will use all three SIRTF instruments (Infrared Array Camera [IRAC], Multiband Imaging Photometer for SIRTF [MIPS], and Infrared Spectrograph [IRS]) to observe sources that span the evolutionary sequence from molecular cores to protoplanetary disks, encompassing a wide range of cloud masses, stellar masses, and star‐forming environments. In addition to targeting about 150 known compact cores, we will survey with IRAC and MIPS (3.6–70 μm) the entire areas of five of the nearest large molecular clouds for new candidate protostars and substellar objects as faint as 0.001 solar luminosities. We will also observe with IRAC and MIPS about 190 systems likely to be in the early stages of planetary system formation (ages up to about 10 Myr), probing the evolution of the circumstellar dust, the raw material for planetary cores. Candidate planet‐forming disks as small as 0.1 lunar masses will be detectable. Spectroscopy with IRS of new objects found in the surveys and of a select group of known objects will add vital information on the changing chemical and physical conditions in the disks and envelopes. The resulting data products will include catalogs of thousands of previously unknown sources, multiwavelength maps of about 20 deg^2 of molecular clouds, photometry of about 190 known young stars, spectra of at least 170 sources, ancillary data from ground‐based telescopes, and new tools for analysis and modeling. These products will constitute the foundations for many follow‐up studies with ground‐based telescopes, as well as with SIRTF itself and other space missions such as SIM, JWST, Herschel, and TPF/Darwin
Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach
Many algorithms in workflow scheduling and resource provisioning rely on the
performance estimation of tasks to produce a scheduling plan. A profiler that
is capable of modeling the execution of tasks and predicting their runtime
accurately, therefore, becomes an essential part of any Workflow Management
System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS)
platforms that use clouds for deploying scientific workflows, task runtime
prediction becomes more challenging because it requires the processing of a
significant amount of data in a near real-time scenario while dealing with the
performance variability of cloud resources. Hence, relying on methods such as
profiling tasks' execution data using basic statistical description (e.g.,
mean, standard deviation) or batch offline regression techniques to estimate
the runtime may not be suitable for such environments. In this paper, we
propose an online incremental learning approach to predict the runtime of tasks
in scientific workflows in clouds. To improve the performance of the
predictions, we harness fine-grained resources monitoring data in the form of
time-series records of CPU utilization, memory usage, and I/O activities that
are reflecting the unique characteristics of a task's execution. We compare our
solution to a state-of-the-art approach that exploits the resources monitoring
data based on regression machine learning technique. From our experiments, the
proposed strategy improves the performance, in terms of the error, up to
29.89%, compared to the state-of-the-art solutions.Comment: Accepted for presentation at main conference track of 11th IEEE/ACM
International Conference on Utility and Cloud Computin
The Herschel view of the on-going star formation in the Vela-C molecular cloud
As part of the Herschel guaranteed time key program 'HOBYS', we present the
photometric survey of the star forming region Vela-C, one of the nearest sites
of low-to-high-mass star formation in the Galactic plane. Vela-C has been
observed with PACS and SPIRE in parallel mode between 70 um and 500 um over an
area of about 3 square degrees. A photometric catalogue has been extracted from
the detections in each band, using a threshold of 5 sigma over the local
background. Out of this catalogue we have selected a robust sub-sample of 268
sources, of which 75% are cloud clumps and 25% are cores. Their Spectral Energy
Distributions (SEDs) have been fitted with a modified black body function. We
classify 48 sources as protostellar and 218 as starless. For two further
sources, we do not provide a secure classification, but suggest they are Class
0 protostars.
From SED fitting we have derived key physical parameters. Protostellar
sources are in general warmer and more compact than starless sources. Both
these evidences can be ascribed to the presence of an internal source(s) of
moderate heating, which also causes a temperature gradient and hence a more
peaked intensity distribution. Moreover, the reduced dimensions of protostellar
sources may indicate that they will not fragment further. A virial analysis of
the starless sources gives an upper limit of 90% for the sources
gravitationally bound and therefore prestellar. We fit a power law N(logM) prop
M^-1.1 to the linear portion of the mass distribution of prestellar sources.
This is in between that typical of CO clumps and those of cores in nearby
star-forming regions. We interpret this as a result of the inhomogeneity of our
sample, which is composed of comparable fractions of clumps and cores.Comment: 9 pages, 7 figures, accepted by A&
Young starless cores embedded in the magnetically dominated Pipe Nebula
The Pipe Nebula is a massive, nearby dark molecular cloud with a low
star-formation efficiency which makes it a good laboratory to study the very
early stages of the star formation process. The Pipe Nebula is largely
filamentary, and appears to be threaded by a uniform magnetic field at scales
of few parsecs, perpendicular to its main axis. The field is only locally
perturbed in a few regions, such as the only active cluster forming core B59.
The aim of this study is to investigate primordial conditions in low-mass
pre-stellar cores and how they relate to the local magnetic field in the cloud.
We used the IRAM 30-m telescope to carry out a continuum and molecular survey
at 3 and 1 mm of early- and late-time molecules toward four selected starless
cores inside the Pipe Nebula. We found that the dust continuum emission maps
trace better the densest regions than previous 2MASS extinction maps, while
2MASS extinction maps trace better the diffuse gas. The properties of the cores
derived from dust emission show average radii of ~0.09 pc, densities of
~1.3x10^5 cm^-3, and core masses of ~2.5 M_sun. Our results confirm that the
Pipe Nebula starless cores studied are in a very early evolutionary stage, and
present a very young chemistry with different properties that allow us to
propose an evolutionary sequence. All of the cores present early-time molecular
emission, with CS detections toward all the sample. Two of them, Cores 40 and
109, present strong late-time molecular emission. There seems to be a
correlation between the chemical evolutionary stage of the cores and the local
magnetic properties that suggests that the evolution of the cores is ruled by a
local competition between the magnetic energy and other mechanisms, such as
turbulence.Comment: Accepted for publication in ApJ. 15 pages, 5 figures, 9 table
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