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, $R_{d}(t)$, and of the rim height, $h_{m}(t)$. After a transient time depending on the initial thickness, $h_{m}(t)$ grows like $\sqrt{t}$ while $R_{d}(t)$ increases like $\exp{(\sqrt{t})}$. 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