The formation of planetary disks and winds: an ultraviolet view

Planetary systems are angular momentum reservoirs generated during star formation. This accretion process produces very powerful engines able to drive the optical jets and the molecular outflows. A fraction of the engine energy is released into heating thus the temperature of the engine ranges from the 3000K of the inner disk material to the 10MK in the areas where magnetic reconnection occurs. There are important unsolved problems concerning the nature of the engine, its evolution and the impact of the engine in the chemical evolution of the inner disk. Of special relevance is the understanding of the shear layer between the stellar photosphere and the disk; this layer controls a significant fraction of the magnetic field building up and the subsequent dissipative processes ougth to be studied in the UV. This contribution focus on describing the connections between 1 Myr old suns and the Sun and the requirements for new UV instrumentation to address their evolution during this period. Two types of observations are shown to be needed: monitoring programmes and high resolution imaging down to, at least, milliarsecond scales.Comment: Accepted for publication in Astrophysics and Space Science 9 figure

Copper and Barium Abundances in the Ursa Major Moving Group

We present Cu and Ba abundances for 7 G-K dwarf stars, members of the solar-metallicity, 0.3 Gyr old Ursa Major Moving Group. All analyzed member stars show [Ba/Fe] excesses of +0.3-plus, associated with [Cu/Fe] deficiencies of up to -0.23 dex. The present results suggest that there is an anti-correlation between the abundances of Cu and the heavy elements produced by the main component of the neutron capture s-process. Other possible anomalies are Na and C deficiencies with respect to normal solar-metallicity stars. The new data do not confirm the recent claim that the group member HR6094 is a Ba dwarf star.Comment: 8 pages, 6 figures, accepted to MNRA

The influence of statistical properties of Fourier coefficients on random surfaces

Many examples of natural systems can be described by random Gaussian surfaces. Much can be learned by analyzing the Fourier expansion of the surfaces, from which it is possible to determine the corresponding Hurst exponent and consequently establish the presence of scale invariance. We show that this symmetry is not affected by the distribution of the modulus of the Fourier coefficients. Furthermore, we investigate the role of the Fourier phases of random surfaces. In particular, we show how the surface is affected by a non-uniform distribution of phases

Topologically Protected Zero Modes in Twisted Bilayer Graphene

We show that the twisted graphene bilayer can reveal unusual topological properties at low energies, as a consequence of a Dirac-point splitting. These features rely on a symmetry analysis of the electron hopping between the two layers of graphene and we derive a simplified effective low-energy Hamiltonian which captures the essential topological properties of twisted bilayer graphene. The corresponding Landau levels peculiarly reveal a degenerate zero-energy mode which cannot be lifted by strong magnetic fields.Comment: 5 pages, 3 figures; published versio

Automatic learning of gait signatures for people identification

This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouettes, in this work we explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). We carry out a thorough experimental evaluation of the proposed CNN architecture on the challenging TUM-GAID dataset. The experimental results indicate that using spatio-temporal cuboids of optical flow as input data for CNN allows to obtain state-of-the-art results on the gait task with an image resolution eight times lower than the previously reported results (i.e. 80x60 pixels).Comment: Proof of concept paper. Technical report on the use of ConvNets (CNN) for gait recognition. Data and code: http://www.uco.es/~in1majim/research/cnngaitof.htm