The assembly of massive galaxies from NIR observations of the Hubble Deep Field South

We use a deep K(AB)<25 galaxy sample in the Hubble Deep Field South to trace the evolution of the cosmological stellar mass density from z~ 0.5 to z~3. We find clear evidence for a decrease of the average stellar mass density at high redshift, 2<z<3.2, that is 15^{+25}_{-5}% of the local value, two times higher than what observed in the Hubble Deep Field North. To take into account for the selection effects, we define a homogeneous subsample of galaxies with 10^{10}M_\odot \leq M_* \leq 10^{11}M_\odot: in this sample, the mass density at z>2 is 20^{+20}_{-5} % of the local value. In the mass--limited subsample at z>2, the fraction of passively fading galaxies is at most 25%, although they can contribute up to about 40% of the stellar mass density. On the other hand, star--forming galaxies at z>2 form stars with an average specific rate at least ~4 x10^{-10} yr$^{-1}$, 3 times higher than the z<~1 value. This implies that UV bright star--forming galaxies are substancial contributors to the rise of the stellar mass density with cosmic time. Although these results are globally consistent with $\Lambda$--CDM scenarios, the present rendition of semi analytic models fails to match the stellar mass density produced by more massive galaxies present at z>2.Comment: Accepted for publication on ApJLetter

Predicting Exploitation of Disclosed Software Vulnerabilities Using Open-source Data

Each year, thousands of software vulnerabilities are discovered and reported to the public. Unpatched known vulnerabilities are a significant security risk. It is imperative that software vendors quickly provide patches once vulnerabilities are known and users quickly install those patches as soon as they are available. However, most vulnerabilities are never actually exploited. Since writing, testing, and installing software patches can involve considerable resources, it would be desirable to prioritize the remediation of vulnerabilities that are likely to be exploited. Several published research studies have reported moderate success in applying machine learning techniques to the task of predicting whether a vulnerability will be exploited. These approaches typically use features derived from vulnerability databases (such as the summary text describing the vulnerability) or social media posts that mention the vulnerability by name. However, these prior studies share multiple methodological shortcomings that inflate predictive power of these approaches. We replicate key portions of the prior work, compare their approaches, and show how selection of training and test data critically affect the estimated performance of predictive models. The results of this study point to important methodological considerations that should be taken into account so that results reflect real-world utility

The evolution of the galaxy luminosity function in the rest frame blue band up to z=3.5

We present an estimate of the cosmological evolution of the field galaxy luminosity function (LF) in the rest frame 4400 Angstrom B -band up to redshift z=3.5. To this purpose, we use a composite sample of 1541 I--selected galaxies selected down to I_(AB)=27.2 and 138 galaxies selected down to K_(AB)=25 from ground-based and HST multicolor surveys, most notably the new deep JHK images in the Hubble Deep Field South (HDF-S) taken with the ISAAC instrument at the ESO-VLT telescope. About 21% of the sample has spectroscopic redshifts, and the remaining fraction well calibrated photometric redshifts. The resulting blue LF shows little density evolution at the faint end with respect to the local values, while at the bright end (M_B(AB)<-20) a brightening increasing with redshift is apparent with respect to the local LF. Hierarchical CDM models overpredict the number of faint galaxies by about a factor 3 at z=1. At the bright end the predicted LFs are in reasonable agreement only at low and intermediate redshifts (z=1), but fail to reproduce the pronounced brightening observed in the high redshift (z=2-3) LF. This brightening could mark the epoch where a major star formation activity is present in the galaxy evolution.Comment: 14 pages, 2 figures, Astrophysical Journal Letters, in pres

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya

Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization

Afterglow upper limits for four short duration, hard spectrum gamma-ray bursts

We present interplanetary network localization, spectral, and time history information for four short-duration, hard spectrum gamma-ray bursts, GRB000607, 001025B, 001204, and 010119. All of these events were followed up with sensitive radio and optical observations (the first and only such bursts to be followed up in the radio to date), but no detections were made, demonstrating that the short bursts do not have anomalously intense afterglows. We discuss the upper limits, and show that the lack of observable counterparts is consistent both with the hypothesis that the afterglow behavior of the short bursts is like that of the long duration bursts, many of which similarly have no detectable afterglows, as well as with the hypothesis that the short bursts have no detectable afterglows at all. Small number statistics do not allow a clear choice between these alternatives, but given the present detection rates of various missions, we show that progress can be expected in the near future.Comment: 19 pages, 4 figures; Revised version, accepted by the Astrophysical Journa