Evaluation of Deep Learning on an Abstract Image Classification Dataset

Convolutional Neural Networks have become state of the art methods for image classification over the last couple of years. By now they perform better than human subjects on many of the image classification datasets. Most of these datasets are based on the notion of concrete classes (i.e. images are classified by the type of object in the image). In this paper we present a novel image classification dataset, using abstract classes, which should be easy to solve for humans, but variations of it are challenging for CNNs. The classification performance of popular CNN architectures is evaluated on this dataset and variations of the dataset that might be interesting for further research are identified.Comment: Copyright IEEE. To be published in the proceedings of MBCC at ICCV201

Economic Polarization Through Trade: Trade Liberalization and Regional Growth in Mexico

economic growth, regional disparities, trade, integration, polarization, Mexico

Learning Abstract Classes using Deep Learning

Humans are generally good at learning abstract concepts about objects and scenes (e.g.\ spatial orientation, relative sizes, etc.). Over the last years convolutional neural networks have achieved almost human performance in recognizing concrete classes (i.e.\ specific object categories). This paper tests the performance of a current CNN (GoogLeNet) on the task of differentiating between abstract classes which are trivially differentiable for humans. We trained and tested the CNN on the two abstract classes of horizontal and vertical orientation and determined how well the network is able to transfer the learned classes to other, previously unseen objects.Comment: To be published in the proceedings of the International Conference on Bio-inspired Information and Communications Technologies 201

Sensitivity to the Higgs sector of SUSY-seesaw models via LFV tau decays

Here we study and compare the sensitivity to the Higgs sector of the SUSY-seesaw models via the LFV tau decays: tau-> 3 mu, tau->K^{+}K^{-}, tau->mu eta and tau-> mu f_{0}. We emphasize that, at present, the two later channels are the most efficient ones to test indirectly the Higgs particles.Comment: 4 pages, 3 figures, conference SUSY09 Boston (M.Herrero

The tidally disturbed luminous compact blue galaxy Mkn 1087 and its surroundings

We present new broad-band optical and near-infrared CCD imaging together with deep optical intermediate-resolution spectroscopy of Mkn 1087 and its surrounding objects. We analyze the morphology and colors of the stellar populations of the brightest objects, some of them star-formation areas, as well as the kinematics, physical conditions and chemical composition of the ionized gas associated with them. Mkn 1087 does not host an Active Galactic Nucleus, but it could be a Luminous Compact Blue Galaxy. Although it was classified as a suspected Wolf-Rayet galaxy, we do not detect the spectral features of these sort of massive stars. Mkn 1087 shows morphological and kinematical features that can be explained assuming that it is in interaction with two nearby galaxies: the bright KPG 103a and a dwarf ($M_B\sim-18$) star-forming companion. We argue that this dwarf companion is not a tidal object but an external galaxy because of its low metallicity [12+log(O/H) = 8.24] with respect to the one derived for Mkn 1087 [12+log(O/H) = 8.57] and its kinematics. Some of the non-stellar objects surrounding Mkn 1087 are connected by bridges of matter with the main body, host star-formation events and show similar abundances despite their different angular distances. These facts, together their kinematics, suggest that they are tidal dwarf galaxies formed from material stripped from Mkn 1087. A bright star-forming region at the south of Mkn 1087 (knot #7) does not show indications of being a tidal galaxy or the product of a merging process as suggested in previous works. We argue that Mkn 1087 and its surroundings should be considered a group of galaxies.Comment: Accepted by A&A, 21 pages, 13 figures, 8 table