Follow-up observations at high-angular resolution of bright submillimeter galaxies selected from deep extragalactic surveys have shown that the single-dish sources are comprised of a blend of several galaxies. Consequently, number counts derived from low-and high-angular-resolution observations are in tension. This demonstrates the importance of resolution effects at these wavelengths and the need for realistic simulations to explore them. We built a new 2 deg 2 simulation of the extragalactic sky from the far-infrared to the submillimeter. It is based on an updated version of the 2SFM (two star-formation modes) galaxy evolution model. Using global galaxy properties generated by this model, we used an abundance-matching technique to populate a dark-matter lightcone and thus simulate the clustering. We produced maps from this simulation and extracted the sources, and we show that the limited angular resolution of single-dish instruments has a strong impact on (sub)millimeter continuum observations. Taking into account these resolution effects, we are reproducing a large set of observables, as number counts and their evolution with redshift and cosmic infrared background power spectra. Our simulation consistently describes the number counts from single-dish telescopes and interferometers. In particular, at 350 and 500 µm, we find that the number counts measured by Herschel between 5 and 50 mJy are biased towards high values by a factor ∼2, and that the redshift distributions are biased towards low redshifts. We also show that the clustering has an important impact on the Herschel pixel histogram used to derive number counts from P(D) analysis. We find that the brightest galaxy in the beam of a 500 µm Herschel source contributes on average to only ∼60 % of the Herschel flux density, but that this number will rise to ∼95 % for future millimeter surveys on 30 meter-class telescopes (e.g., NIKA2 at IRAM). Finally, we show that the large number density of red Herschel sources found in observations but not in models might be an observational artifact caused by the combination of noise, resolution effects, and the steepness of color-and flux density distributions. Our simulation, called SIDES (Simulated Infrared Dusty Extragalactic Sky), is available at http://cesam.lam.fr/sides.

We use the energy-balance code MAGPHYS to determine stellar and dust masses, and dust corrected star-formation rates for over 200,000 GAMA galaxies, 170,000 G10-COSMOS galaxies and 200,000 3D-HST galaxies. Our values agree well with previously reported measurements and constitute a representative and homogeneous dataset spanning a broad range in stellar mass (10 8 -10 12 M ), dust mass (10 6 -10 9 M ), and star-formation rates (0.01-100M yr −1 ), and over a broad redshift range (0.0 < z < 5.0). We combine these data to measure the cosmic star-formation history (CSFH), the stellar-mass density (SMD), and the dust-mass density (DMD) over a 12 Gyr timeline. The data mostly agree with previous estimates, where they exist, and provide a quasi-homogeneous dataset using consistent mass and star-formation estimators with consistent underlying assumptions over the full time range. As a consequence our formal errors are significantly reduced when compared to the historic literature. Integrating our cosmic star-formation history we precisely reproduce the stellar-mass density with an ISM replenishment factor of 0.50 ± 0.07, consistent with our choice of Chabrier IMF plus some modest amount of stripped stellar mass. Exploring the cosmic dust density evolution, we find a gradual increase in dust density with lookback time. We build a simple phenomenological model from the CSFH to account for the dust mass evolution, and infer two key conclusions: (1) For every unit of stellar mass which is formed 0.0065-0.004 units of dust mass is also formed; (2) Over the history of the Universe approximately 90 to 95 per cent of all dust formed has been destroyed and/or ejected.

Abstract.To protect critical resources in today's networked environments, it is desirable to quantify the likelihood of potential multi-step attacks that combine multiple vulnerabilities. This now becomes feasible due to a model of causal relationships between vulnerabilities, namely, attack graph. This paper proposes an attack graph-based probabilistic metric for network security and studies its efficient computation. We first define the basic metric and provide an intuitive and meaningful interpretation to the metric. We then study the definition in more complex attack graphs with cycles and extend the definition accordingly. We show that computing the metric directly from its definition is not efficient in many cases and propose heuristics to improve the efficiency of such computation.

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