Venus has no seasons, slow rotation and a very massive atmosphere, which is mainly carbon dioxide with clouds primarily of sulphuric acid droplets. Infrared observations by previous missions to Venus revealed a bright 'dipole' feature surrounded by a cold 'collar' at its north pole. The polar dipole is a 'double-eye' feature at the centre of a vast vortex that rotates around the pole, and is possibly associated with rapid downwelling. The polar cold collar is a wide, shallow river of cold air that circulates around the polar vortex. One outstanding question has been whether the global circulation was symmetric, such that a dipole feature existed at the south pole. Here we report observations of Venus' south-polar region, where we have seen clouds with morphology much like those around the north pole, but rotating somewhat faster than the northern dipole. The vortex may extend down to the lower cloud layers that lie at about 50 km height and perhaps deeper. The spectroscopic properties of the clouds around the south pole are compatible with a sulphuric acid composition.

Abstract. Empirical auto-tuning and machine learning techniques have been showing high potential to improve execution time, power consumption, code size, reliability and other important metrics of various applications for more than two decades. However, they are still far from widespread production use due to lack of native support for auto-tuning in an ever changing and complex software and hardware stack, large and multi-dimensional optimization spaces, excessively long exploration times, and lack of unified mechanisms for preserving and sharing of optimization knowledge and research material.We present a possible collaborative approach to solve above problems using Collective Mind knowledge management system. In contrast with previous cTuning framework, this modular infrastructure allows to preserve and share through the Internet the whole auto-tuning setups with all related artifacts and their software and hardware dependencies besides just performance data. It also allows to gradually structure, systematize and describe all available research material including tools, benchmarks, data sets, search strategies and machine learning models. Researchers can take advantage of shared components and data with extensible meta-description to quickly and collaboratively validate and improve existing auto-tuning and benchmarking techniques or prototype new ones. The community can now gradually learn and improve complex behavior of all existing computer systems while exposing behavior anomalies or model mispredictions to an interdisciplinary community in a reproducible way for further analysis. We present several practical, collaborative and model-driven auto-tuning scenarios. We also decided to release all material at c-mind.org/repo to set up an example for a collaborative and reproducible research as well as our new publication model in computer engineering where experimental results are continuously shared and validated by the community.

[1] In this paper a set of frequency domain (500 Hz to 1 MHz) experimental data on electrical and magnetic parameters of magnetite/silica mixtures as Martian soil simulants is presented. Complex permittivity was obtained by measuring the magnitude and phase of the electrical impedance of a capacitive cell filled with the tested material. Complex magnetic permeability was obtained by measuring the impedance of a toroid fully embedded in the mixtures. Both the permittivity and magnetic permeability exhibited an appreciable increase with the magnetite volume fraction and a complex behavior with grain size and origin. Furthermore, to assess the possibility of extending the data acquired at 1 MHz to higher frequencies (tens to hundreds of MHz), some comparisons were made between these data and Time Domain Reflectometry measurements. Finally, the attenuation versus magnetite volume fraction and frequency (in a band consistent with the MARSIS and SHARAD sounders) was computed for various types of magnetite/silica mixtures. The results indicate that in a dry soil which contains a significant fraction of magnetic material both the wave velocity and the attenuation are a combination of different factors, including the dimension, shape, and origin of the magnetic material.

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