Stochastic Discriminative EM

Stochastic discriminative EM (sdEM) is an online-EM-type algorithm for discriminative training of probabilistic generative models belonging to the exponential family. In this work, we introduce and justify this algorithm as a stochastic natural gradient descent method, i.e. a method which accounts for the information geometry in the parameter space of the statistical model. We show how this learning algorithm can be used to train probabilistic generative models by minimizing different discriminative loss functions, such as the negative conditional log-likelihood and the Hinge loss. The resulting models trained by sdEM are always generative (i.e. they define a joint probability distribution) and, in consequence, allows to deal with missing data and latent variables in a principled way either when being learned or when making predictions. The performance of this method is illustrated by several text classification problems for which a multinomial naive Bayes and a latent Dirichlet allocation based classifier are learned using different discriminative loss functions.Comment: UAI 2014 paper + Supplementary Material. In Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI 2014), edited by Nevin L. Zhang and Jian Tian. AUAI Pres

Probabilistic Graphical Models on Multi-Core CPUs using Java 8

In this paper, we discuss software design issues related to the development of parallel computational intelligence algorithms on multi-core CPUs, using the new Java 8 functional programming features. In particular, we focus on probabilistic graphical models (PGMs) and present the parallelisation of a collection of algorithms that deal with inference and learning of PGMs from data. Namely, maximum likelihood estimation, importance sampling, and greedy search for solving combinatorial optimisation problems. Through these concrete examples, we tackle the problem of defining efficient data structures for PGMs and parallel processing of same-size batches of data sets using Java 8 features. We also provide straightforward techniques to code parallel algorithms that seamlessly exploit multi-core processors. The experimental analysis, carried out using our open source AMIDST (Analysis of MassIve Data STreams) Java toolbox, shows the merits of the proposed solutions.Comment: Pre-print version of the paper presented in the special issue on Computational Intelligence Software at IEEE Computational Intelligence Magazine journa

Near-infrared photometry of isolated spirals with and without an AGN. I: The Data

We present infrared imaging data in the J and K' bands obtained for 18 active spiral galaxies, together with 11 non active galaxies taken as a control sample. All of them were chosen to satisfy well defined isolation criteria so that the observed properties are not related to gravitational interaction. For each object we give: the image in the K' band, the sharp-divided image (obtained by dividing the observed image by a filtered one), the difference image (obtained by subtracting a model to the observed one), the color J-K' image, the ellipticity and position angle profiles, the surface brightness profiles in J and K', their fits by bulge+disk models and the color gradient. We have found that four (one) active (control) galaxies previously classified as non-barred turn out to have bars when observed in the near-infrared. One of these four galaxies (UGC 1395) also harbours a secondary bar. For 15 (9 active, 6 control) out of 24 (14 active, 10 control) of the optically classified barred galaxies (SB or SX) we find that a secondary bar (or a disk, a lense or an elongated ring) is present. The work presented here is part of a large program (DEGAS) aimed at finding whether there are differences between active and non active galaxies in the properties of their central regions that could be connected with the onset of nuclear activity.Comment: Accepted for publication in Astronomy & Astrophysics Supplement Serie

Second Order PAC-Bayesian Bounds for the Weighted Majority Vote

We present a novel analysis of the expected risk of weighted majority vote in multiclass classification. The analysis takes correlation of predictions by ensemble members into account and provides a bound that is amenable to efficient minimization, which yields improved weighting for the majority vote. We also provide a specialized version of our bound for binary classification, which allows to exploit additional unlabeled data for tighter risk estimation. In experiments, we apply the bound to improve weighting of trees in random forests and show that, in contrast to the commonly used first order bound, minimization of the new bound typically does not lead to degradation of the test error of the ensemble

Effects of a thermal inversion experiment on STEM students learning and application of damped harmonic motion

There are diverse teaching methodologies to promote both collaborative and individual work in undergraduate physics courses. However, few educational studies seek to understand how students learn and apply new knowledge through open-ended activities that require mathematical modeling and experimentation focused on environmental problems. In this work, we propose a novel home experiment to simulate the dynamics of a particulate under temperature inversion and model it as damped harmonic motion. Twenty six first year students enrolled in STEM majors answered six qualitative questions after designing and developing the experiment. These questions helped analyze the students epistemological beliefs about their learning process of physics topics and its applications. Results showed that this type of open-ended experiments could facilitate the students understanding of physics phenomena. In addition, this experiment showed that it could help physics professors to promote students epistemological development by giving their students the opportunity to search for different sources of knowledge and becoming self-learners instead of looking at the professor as the epistemological authority. At the end, students described this activity as a positive experience that helped them realize alternative ways to apply physics topics in different contexts of their environment.Comment: 23 pages, 5 figure

A HST study of the stellar populations in the cometary dwarf irregular galaxy NGC 2366

We present V and I photometry of the resolved stars in the cometary dwarf irregular galaxy NGC 2366, using Wide Field Planetary Camera 2 images obtained with the Hubble Space Telescope. The resulting color-magnitude diagram reaches down to I~26.0 mag. It reveals not only a young population of blue main-sequence stars (age <30 Myr) but also an intermediate-age population of blue and red supergiants (20 Myr<age<100 Myr), and an older evolved populations of asymptotic giant branch (AGB) stars (age >100 Myr) and red giant branch (RGB) stars (age >1 Gyr). The measured magnitude I=23.65+/-0.10 mag of the RGB tip results in a distance modulus m-M=27.67+/-0.10, which corresponds to a distance of 3.42+/-0.15 Mpc, in agreement with previous distance determinations. The youngest stars are associated with the bright complex of HII regions NGC 2363=Mrk 71 in the southwest extremity of the galaxy. As a consequence of the diffusion and relaxation processes of stellar ensembles, the older the stellar population is, the smoother and more extended is its spatial distribution. An underlying population of older stars is found throughout the body of NGC 2366. The most notable feature of this older population is the presence of numerous relatively bright AGB stars. The number ratio of AGB to RGB stars and the average absolute brightness of AGB stars in NGC 2366 are appreciably higher than in the BCD VII Zw 403, indicating a younger age of the AGB stars in NGC 2366. In addition to the present burst of age <100 Myr, there has been strong star formation activity in the past of NGC 2366, from ~100 Myr to <3 Gyr ago.Comment: 32 pages, 15 figures, accepted for publication in the Astrophysical Journa