433 research outputs found
The GAN that warped: semantic attribute editing with unpaired data
Deep neural networks have recently been used to edit images with great success, in particular for faces. However, they are often limited to only being able to work at a restricted range of resolutions. Many methods are so flexible that face edits can often result in an unwanted loss of identity. This work proposes to learn how to perform semantic image edits through the application of smooth warp fields. Previous approaches that attempted to use warping for semantic edits required paired data, i.e. example images of the same subject with different semantic attributes. In contrast, we employ recent advances in Generative Adversarial Networks that allow our model to be trained with unpaired data. We demonstrate face editing at very high resolutions (4k images) with a single forward pass of a deep network at a lower resolution. We also show that our edits are substantially better at preserving the subject's identity. The robustness of our approach is demonstrated by showing plausible image editing results on the Cub200 birds dataset. To our knowledge this has not been previously accomplished, due the challenging nature of the dataset
A semi-analytical perspective on massive galaxies at
The most massive and luminous galaxies in the Universe serve as powerful
probes to study the formation of structure, the assembly of mass, and
cosmology. However, their detailed formation and evolution is still barely
understood. Here we extract a sample of massive mock galaxies from the
semi-analytical model of galaxy formation (SAM) GALACTICUS from the
MultiDark-Galaxies, by replicating the CMASS photometric selection from the
SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS). The comparison of the
GALACTICUS CMASS-mock with BOSS-CMASS data allows us to explore different
aspects of the massive galaxy population at , including the
galaxy-halo connection and the galaxy clustering. We find good agreement
between our modelled galaxies and observations regarding the galaxy-halo
connection, but our CMASS-mock over-estimates the clustering amplitude of the
2-point correlation function, due to a smaller number density compared to BOSS,
a lack of blue objects, and a small intrinsic scatter in stellar mass at fixed
halo mass of dex. To alleviate this problem, we construct an alternative
mock catalogue mimicking the CMASS colour-magnitude distribution by randomly
down-sampling the SAM catalogue. This CMASS-mock reproduces the clustering of
CMASS galaxies within 1 and shows some environmental dependency of star
formation properties that could be connected to the quenching of star formation
and the assembly bias.Comment: 15 pages, 10 figures, 2 tables, submitted to MNRA
The ALHAMBRA survey : band luminosity function of quiescent and star-forming galaxies at by PDF analysis
Our goal is to study the evolution of the band luminosity function (LF)
since using ALHAMBRA data. We used the photometric redshift and the
band selection magnitude probability distribution functions (PDFs) of those
ALHAMBRA galaxies with mag to compute the posterior LF. We
statistically studied quiescent and star-forming galaxies using the template
information encoded in the PDFs. The LF covariance matrix in
redshift-magnitude-galaxy type space was computed, including the cosmic
variance. That was estimated from the intrinsic dispersion of the LF
measurements in the 48 ALHAMBRA sub-fields. The uncertainty due to the
photometric redshift prior is also included in our analysis. We modelled the LF
with a redshift-dependent Schechter function affected by the same selection
effects than the data. The measured ALHAMBRA LF at and the
evolving Schechter parameters both for quiescent and star-forming galaxies
agree with previous results in the literature. The estimated redshift evolution
of is and , and of is
and . The measured faint-end slopes are and . We find a significant
population of faint quiescent galaxies, modelled by a second Schechter function
with slope . We find a factor decrease in the
luminosity density of star-forming galaxies, and a factor
increase in the of quiescent ones since , confirming the continuous
build-up of the quiescent population with cosmic time. The contribution of the
faint quiescent population to increases from 3% at to 6% at .
The developed methodology will be applied to future multi-filter surveys such
as J-PAS.Comment: Accepted for publication in Astronomy and Astrophysics. 25 pages, 20
figures, 7 table
Caracterización del aerosol sahariano en Gran Canaria
Ponencia presentada en: 1er Encuentro sobre Meteorología y Atmósfera de Canarias, celebrado en el Puerto de la Cruz, los días 12,13 y 14 de noviembre de 2003. El encuentro estuvo organizado por el Centro Meteorológico Territorial en Canarias Occidental, con la colaboración del Observatorio Atmosférico de Izaña y del Grupo de Física de la Atmósfera de la Facultad de Física (Universidad de La Laguna
The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: modelling the clustering and halo occupation distribution of BOSS CMASS galaxies in the Final Data Release
Citation: Rodriguez-Torres, S. A., Chuang, C. H., Prada, F., Guo, H., Klypin, A., Behroozi, P., . . . Thomas, D. (2016). The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: modelling the clustering and halo occupation distribution of BOSS CMASS galaxies in the Final Data Release. Monthly Notices of the Royal Astronomical Society, 460(2), 1173-1187. doi:10.1093/mnras/stw1014We present a study of the clustering and halo occupation distribution of Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxies in the redshift range 0.43 cold dark matter Planck cosmology. We compare the observational data with the simulated ones on a light cone constructed from 20 subsequent outputs of the simulation. Observational effects such as incompleteness, geometry, veto masks and fibre collisions are included in the model, which reproduces within 1 sigma errors the observed monopole of the two-point correlation function at all relevant scales: from the smallest scales, 0.5 h(-1) Mpc, up to scales beyond the baryon acoustic oscillation feature. This model also agrees remarkably well with the BOSS galaxy power spectrum (up to k similar to 1 h Mpc(-1)), and the three-point correlation function. The quadrupole of the correlation function presents some tensions with observations. We discuss possible causes that can explain this disagreement, including target selection effects. Overall, the standard HAM model describes remarkably well the clustering statistics of the CMASS sample. We compare the stellar-to-halo mass relation for the CMASS sample measured using weak lensing in the Canada-France-Hawaii Telescope Stripe 82 Survey with the prediction of our clustering model, and find a good agreement within 1 sigma. The BigMD-BOSS light cone including properties of BOSS galaxies and halo properties is made publicly available
X-ray selected AGN in groups at redshifts z~1
We explore the role of the group environment in the evolution of AGN at the
redshift interval 0.7<z<1.4, by combining deep Chandra observations with
extensive optical spectroscopy from the All-wavelength Extended Groth strip
International Survey (AEGIS). The sample consists of 3902 optical sources and
71 X-ray AGN. Compared to the overall optically selected galaxy population,
X-ray AGN are more frequently found in groups at the 99% confidence level. This
is partly because AGN are hosted by red luminous galaxies, which are known to
reside, on average, in dense environments. Relative to these sources, the
excess of X-ray AGN in groups is significant at the 91% level only. Restricting
the sample to 0.7<z<0.9 and M_B<-20mag in order to control systematics we find
that X-ray AGN represent (4.7\pm1.6) and (4.5\pm1.0)% of the optical galaxy
population in groups and in the field respectively. These numbers are
consistent with the AGN fraction in low redshift clusters, groups and the
field. The results above, although affected by small number statistics, suggest
that X-ray AGN are spread over a range of environments, from groups to the
field, once the properties of their hosts (e.g. colour, luminosity) are
accounted for. There is also tentative evidence, significant at the 98% level,
that the field produces more X-ray luminous AGN compared to groups, extending
similar results at low redshift to z~1. This trend may be because of either
cold gas availability or the nature of the interactions occurring in the denser
group environment (i.e. prolonged tidal encounters).Comment: To appear in MNRA
Mathematical properties of weighted impact factors based on measures of prestige of the citing journals
The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-015-1741-0An abstract construction for general weighted impact factors is introduced. We
show that the classical weighted impact factors are particular cases of our model, but it can
also be used for defining new impact measuring tools for other sources of information as
repositories of datasets providing the mathematical support for a new family of altmet-
rics. Our aim is to show the main mathematical properties of this class of impact measuring
tools, that hold as consequences of their mathematical structure and does not depend on the
definition of any given index nowadays in use. In order to show the power of our approach
in a well-known setting, we apply our construction to analyze the stability of the ordering
induced in a list of journals by the 2-year impact factor (IF2). We study the change of this
ordering when the criterium to define it is given by the numerical value of a new weighted
impact factor, in which IF2 is used for defining the weights. We prove that, if we assume
that the weight associated to a citing journal increases with its IF2, then the ordering given
in the list by the new weighted impact factor coincides with the order defined by the IF2. We give a quantitative bound for the errors committed. We also show two examples of
weighted impact factors defined by weights associated to the prestige of the citing journal
for the fields of MATHEMATICS and MEDICINE, GENERAL AND INTERNAL,
checking if they satisfy the increasing behavior mentioned above.Ferrer Sapena, A.; Sánchez Pérez, EA.; González, LM.; Peset Mancebo, MF.; Aleixandre Benavent, R. (2015). Mathematical properties of weighted impact factors based on measures of prestige of the citing journals. Scientometrics. 105(3):2089-2108. https://doi.org/10.1007/s11192-015-1741-0S208921081053Ahlgren, P., & Waltman, L. (2014). The correlation between citation-based and expert-based assessments of publication channels: SNIP and SJR vs. Norwegian quality assessments. Journal of Informetrics, 8, 985–996.Aleixandre Benavent, R., Valderrama Zurián, J. C., & González Alcaide, G. (2007). Scientific journals impact factor: Limitations and alternative indicators. El Profesional de la Información, 16(1), 4–11.Altmann, K. G., & Gorman, G. E. (1998). The usefulness of impact factor in serial selection: A rank and mean analysis using ecology journals. Library Acquisitions-Practise and Theory, 22, 147–159.Arnold, D. N., & Fowler, K. K. (2011). Nefarious numbers. Notices of the American Mathematical Society, 58(3), 434–437.Beliakov, G., & James, S. (2012). Using linear programming for weights identification of generalized bonferroni means in R. In: Proceedings of MDAI 2012 modeling decisions for artificial intelligence. Lecture Notes in Computer Science, Vol. 7647, pp. 35–44.Beliakov, G., & James, S. (2011). Citation-based journal ranks: The use of fuzzy measures. Fuzzy Sets and Systems, 167, 101–119.Buela-Casal, G. (2003). Evaluating quality of articles and scientific journals. Proposal of weighted impact factor and a quality index. Psicothema, 15(1), 23–25.Dorta-Gonzalez, P., & Dorta-Gonzalez, M. I. (2013). Comparing journals from different fields of science and social science through a JCR subject categories normalized impact factor. Scientometrics, 95(2), 645–672.Dorta-Gonzalez, P., Dorta-Gonzalez, M. I., Santos-Penate, D. R., & Suarez-Vega, R. (2014). Journal topic citation potential and between-field comparisons: The topic normalized impact factor. Journal of Informetrics, 8(2), 406–418.Egghe, L., & Rousseau, R. (2002). A general frame-work for relative impact indicators. Canadian Journal of Information and Library Science, 27(1), 29–48.Gagolewski, M., & Mesiar, R. (2014). Monotone measures and universal integrals in a uniform framework for the scientific impact assessment problem. Information Sciences, 263, 166–174.Garfield, E. (2006). The history and meaning of the journal impact factor. JAMA, 295(1), 90–93.Habibzadeh, F., & Yadollahie, M. (2008). Journal weighted impact factor: A proposal. Journal of Informetrics, 2(2), 164–172.Klement, E., Mesiar, R., & Pap, E. (2010). A universal integral as common frame for Choquet and Sugeno integral. IEEE Transaction on Fuzzy System, 18, 178–187.Leydesdorff, L., & Opthof, T. (2010). Scopus’s source normalized impact per paper (SNIP) versus a journal impact factor based on fractional counting of citations. Journal of the American Society for Information Science and Technology, 61, 2365–2369.Li, Y. R., Radicchi, F., Castellano, C., & Ruiz-Castillo, J. (2013). Quantitative evaluation of alternative field normalization procedures. Journal of Informetrics, 7(3), 746–755.Moed, H. F. (2010). Measuring contextual citation impact of scientific journals. Journal of Informetrics, 4, 265–277.NISO. (2014). Alternative metrics initiative phase 1. White paper. http://www.niso.org/apps/group-public/download.php/13809/Altmetrics-project-phase1-white-paperOwlia, P., Vasei, M., Goliaei, B., & Nassiri, I. (2011). Normalized impact factor (NIF): An adjusted method for calculating the citation rate of biomedical journals. Journal of Biomedical Informatics, 44(2), 216–220.Pinski, G., & Narin, F. (1976). Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics. Information Processing and Management, 12, 297–312.Pinto, A. C., & Andrade, J. B. (1999). Impact factor of scientific journals: What is the meaning of this parameter? Quimica Nova, 22, 448–453.Raghunathan, M. S., & Srinivas, V. (2001). Significance of impact factor with regard to mathematics journals. Current Science, 80(5), 605.Ruiz Castillo, J., & Waltman, L. (2015). Field-normalized citation impact indicators using algorithmically constructed classification systems of science. Journal of Informetrics, 9, 102–117.Saha, S., Saint, S., & Christakis, D. A. (2003). Impact factor: A valid measure of journal quality? Journal of the Medical Library Association, 91, 42–46.Torra, V., & Narukawa, Y. (2008). The h-index and the number of citations: Two fuzzy integrals. IEEE Transaction on Fuzzy System, 16, 795–797.Torres-Salinas, D., & Jimenez-Contreras, E. (2010). Introduction and comparative study of the new scientific journals citation indicators in journal citation reports and scopus. El Profesional de la Información, 19, 201–207.Waltman, L., & van Eck, N. J. (2008). Some comments on the journal weighted impact factor proposed by Habibzadeh and Yadollahie. Journal of Informetrics, 2(4), 369–372.Waltman, L., van Eck, N. J., van Leeuwen, T. N., & Visser, M. S. (2013). Some modifications to the SNIP journal impact indicator. Journal of Informetrics, 7, 272–285.Zitt, M. (2011). Behind citing-side normalization of citations: some properties of the journal impact factor. Scientometrics, 89, 329–344.Zitt, M., & Small, H. (2008). Modifying the journal impact factor by fractional citation weighting: The audience factor. Journal of the American Society for Information Science and Technology, 59, 1856–1860.Zyczkowski, K. (2010). Citation graph, weighted impact factors and performance indices. Scientometrics, 85(1), 301–315
Selección de tipos de tiempo en Canarias. Un ejemplo: las invasiones de aire sahariano
Ponencia presentada en: 1er Encuentro sobre Meteorología y Atmósfera de Canarias, celebrado en el Puerto de la Cruz, los días 12,13 y 14 de noviembre de 2003. El encuentro estuvo organizado por el Centro Meteorológico Territorial en Canarias Occidental, con la colaboración del Observatorio Atmosférico de Izaña y del Grupo de Física de la Atmósfera de la Facultad de Física (Universidad de La Laguna)Se presenta una propuesta de clasificación de tipos de tiempo por medio de técnicas de estadística
inferencia/ combinadas con los análisis sinópticos tradicionales, con el fin de agrupar y seleccionar
fechas de rasgos similares. Se muestra un ejemplo con uno de los tipos de tiempo más representativos
del clima de Canarias: las invasiones de aire sahariano. Su aplicación permitirá estudiar, de manera
relativamente simple, las tendencias en series temporales largas de estas situaciones desde que se
cuenta con datos en las estaciones meteorológicas de las islas. Se contribuye así al estudio del clima
del archipiélago y del cambio climático en esta región del planeta
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