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Context. The ongoing Gaia-ESO Public Spectroscopic Survey is using FLAMES at the VLT to obtain high-quality medium-resolution Giraffe spectra for about 10 5 stars and high-resolution UVES spectra for about 5000 stars. With UVES, the Survey has already observed 1447 FGK-type stars. Aims. These UVES spectra are analyzed in parallel by several state-of-the-art methodologies. Our aim is to present how these analyses were implemented, to discuss their results, and to describe how a final recommended parameter scale is defined. We also discuss the precision (method-tomethod dispersion) and accuracy (biases with respect to the reference values) of the final parameters. These results are part of the Gaia-ESO second internal release and will be part of its first public release of advanced data products. Methods. The final parameter scale is tied to the scale defined by the Gaia benchmark stars, a set of stars with fundamental atmospheric parameters. In addition, a set of open and globular clusters is used to evaluate the physical soundness of the results. Each of the implemented methodologies is judged against the benchmark stars to define weights in three different regions of the parameter space. The final recommended results are the weighted medians of those from the individual methods. Results. The recommended results successfully reproduce the atmospheric parameters of the benchmark stars and the expected T eff -log g relation of the calibrating clusters. Atmospheric parameters and abundances have been determined for 1301 FGK-type stars observed with UVES. The median of the method-to-method dispersion of the atmospheric parameters is 55 K for T eff , 0.13 dex for log g and 0.07 dex for [Fe/H]. Systematic biases are estimated to be between 50−100 K for T eff , 0.10−0.25 dex for log g and 0.05−0.10 dex for [Fe/H]. Abundances for 24 elements were derived: C, N, O, Na, Mg, Al, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Y, Zr, Mo, Ba, Nd, and Eu. The typical method-to-method dispersion of the abundances varies between 0.10 and 0.20 dex. Conclusions. The Gaia-ESO sample of high-resolution spectra of FGK-type stars will be among the largest of its kind analyzed in a homogeneous way. The extensive list of elemental abundances derived in these stars will enable significant advances in the areas of stellar evolution and Milky Way formation and evolution.

We present a renewed look at M31's Giant Stellar Stream along with the nearby structures Stream C and Stream D, exploiting a new algorithm capable of fitting to the red giant branch (RGB) of a structure in both colour and magnitude space. Using this algorithm, we are able to generate probability distributions in distance, metallicity and RGB width for a series of subfields spanning these structures. Specifically, we confirm a distance gradient of approximately 20 kpc per degree along a 6 degree extension of the Giant Stellar Stream, with the farthest subfields from M31 lying ∼ 120 kpc more distant than the inner-most subfields. Further, we find a metallicity that steadily increases from −0.7 +0.1 −0.1 dex to −0.2 +0.2 −0.1 dex along the inner half of the stream before steadily dropping to a value of −1.0 +0.2 −0.2 dex at the farthest reaches of our coverage. The RGB width is found to increase rapidly from 0.4 +0.1 −0.1 dex to 1.1 +0.2 −0.1 dex in the inner portion of the stream before plateauing and decreasing marginally in the outer subfields of the stream. In addition, we estimate Stream C to lie at a distance between 794 and 862 kpc and Stream D between 758 kpc and 868 kpc. We estimate the median metallicity of Stream C to lie in the range −0.7 to −1.6 dex and a metallicity of −1.1 +0.3 −0.2 dex for Stream D. RGB widths for the two structures are estimated to lie in the range 0.4 to 1.2 dex and 0.3 to 0.7 dex respectively. In total, measurements are obtained for 19 subfields along the Giant Stellar Stream, 4 along Stream C, 5 along Stream D and 3 general M31 spheroid fields for comparison. We thus provide a higher resolution coverage of the structures in these parameters than has previously been available in the literature.

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Radiation induced soft errors in combinational logic is expected to become as important as directly induced errors on state elements. Consequently, it has become important to develop techniques to quickly and accurately predict soft error rates (SER) in logic circuits. In this paper, we propose a new approach, which can be applied to designs that use cell libraries characterized for soft error analysis and utilizes analytical equations to model the propagation of a voltage pulse to the input of a state element. The average error of the SER estimates using our approach compared to the estimates obtained using circuit level simulations is 6.5% while providing an average speed up of 15000. We have demonstrated the scalability of our approach using designs from the ISCAS-85 benchmarks.

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Discrete droplet digital microfluidics-based biochips face problems similar to that in other VLSI CAD systems, but with new constraints and interrelations. We focus on one such problem of resource constrained scheduling for digital microfluidic biochips. Since the problem is NP-complete, finding the optimal solution is a very time expensive task. We propose a hybrid priority scheduling algorithm solution directly applicable to digital microfluidics with the potential to yield near optimal schedules in the general case in a very short time. Furthermore we propose the use of configurable detectors that allow for even more improved system performance.