8,311 research outputs found
Acquiring Correct Knowledge for Natural Language Generation
Natural language generation (NLG) systems are computer software systems that
produce texts in English and other human languages, often from non-linguistic
input data. NLG systems, like most AI systems, need substantial amounts of
knowledge. However, our experience in two NLG projects suggests that it is
difficult to acquire correct knowledge for NLG systems; indeed, every knowledge
acquisition (KA) technique we tried had significant problems. In general terms,
these problems were due to the complexity, novelty, and poorly understood
nature of the tasks our systems attempted, and were worsened by the fact that
people write so differently. This meant in particular that corpus-based KA
approaches suffered because it was impossible to assemble a sizable corpus of
high-quality consistent manually written texts in our domains; and structured
expert-oriented KA techniques suffered because experts disagreed and because we
could not get enough information about special and unusual cases to build
robust systems. We believe that such problems are likely to affect many other
NLG systems as well. In the long term, we hope that new KA techniques may
emerge to help NLG system builders. In the shorter term, we believe that
understanding how individual KA techniques can fail, and using a mixture of
different KA techniques with different strengths and weaknesses, can help
developers acquire NLG knowledge that is mostly correct
Bayesian Inference under Cluster Sampling with Probability Proportional to Size
Cluster sampling is common in survey practice, and the corresponding
inference has been predominantly design-based. We develop a Bayesian framework
for cluster sampling and account for the design effect in the outcome modeling.
We consider a two-stage cluster sampling design where the clusters are first
selected with probability proportional to cluster size, and then units are
randomly sampled inside selected clusters. Challenges arise when the sizes of
nonsampled cluster are unknown. We propose nonparametric and parametric
Bayesian approaches for predicting the unknown cluster sizes, with this
inference performed simultaneously with the model for survey outcome.
Simulation studies show that the integrated Bayesian approach outperforms
classical methods with efficiency gains. We use Stan for computing and apply
the proposal to the Fragile Families and Child Wellbeing study as an
illustration of complex survey inference in health surveys
A Method for Determining Optimum Re-entry Trajectories
Determining optimum atmospheric reentry trajectories using Pontryagin maximum principl
Dewetting of thin polymer films near the glass transition
Dewetting of ultra-thin polymer films near the glass transition exhibits
unexpected front morphologies [G. Reiter, Phys. Rev. Lett., 87, 186101 (2001)].
We present here the first theoretical attempt to understand these features,
focusing on the shear-thinning behaviour of these films. We analyse the profile
of the dewetting film, and characterize the time evolution of the dry region
radius, , and of the rim height, . After a transient time
depending on the initial thickness, grows like while
increases like . Different regimes of growth are
expected, depending on the initial film thickness and experimental time range.Comment: 4 pages, 5 figures Revised version, published in Physical Review
Letters: F. Saulnier, E. Raphael and P.-G. de Gennes, Phys. Rev. Lett. 88,
196101 (2002
Spitzer/IRAC Observations of AGB stars
We present here the first observation of galactic AGB stars with the InfraRed
Array Camera (IRAC) onboard the Spitzer Space Telescope. Our sample consists of
48 AGB stars of different chemical signature, mass loss rate and variability
class. For each star we have measured IRAC photometry and colors. Preliminary
results shows that IRAC colors are sensitive to spectroscopic features
associated to molecules and dust in the AGB wind. Period is only loosely
correlated to the brightness of the stars in the IRAC bands. We do find,
however, a tight period-color relation for sources classified as semiregular
variables. This may be interpreted as the lack of warm dust in the wind of the
sources in this class, as opposed to Mira variables that show higher infrared
excess in all IRAC bands.Comment: 8 pages, to be published in proceedings "IX Torino Workshop on
Evolution and Nucleosynthesis in AGB Stars", 22-26 October 2007, Perugia,
Ital
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