How to use magnetic field information for coronal loop identification?

The structure of the solar corona is dominated by the magnetic field because the magnetic pressure is about four orders of magnitude higher than the plasma pressure. Due to the high conductivity the emitting coronal plasma (visible e.g. in SOHO/EIT) outlines the magnetic field lines. The gradient of the emitting plasma structures is significantly lower parallel to the magnetic field lines than in the perpendicular direction. Consequently information regarding the coronal magnetic field can be used for the interpretation of coronal plasma structures. We extrapolate the coronal magnetic field from photospheric magnetic field measurements into the corona. The extrapolation method depends on assumptions regarding coronal currents, e.g. potential fields (current free) or force-free fields (current parallel to magnetic field). As a next step we project the reconstructed 3D magnetic field lines on an EIT-image and compare with the emitting plasma structures. Coronal loops are identified as closed magnetic field lines with a high emissivity in EIT and a small gradient of the emissivity along the magnetic field.Comment: 14 pages, 3 figure

Temporal context and conditional associative learning

<p>Abstract</p> <p>Background</p> <p>We investigated how temporal context affects the learning of arbitrary visuo-motor associations. Human observers viewed highly distinguishable, fractal objects and learned to choose for each object the one motor response (of four) that was rewarded. Some objects were consistently preceded by specific other objects, while other objects lacked this task-irrelevant but predictive context.</p> <p>Results</p> <p>The results of five experiments showed that predictive context consistently and significantly accelerated associative learning. A simple model of reinforcement learning, in which three successive objects informed response selection, reproduced our behavioral results.</p> <p>Conclusions</p> <p>Our results imply that not just the representation of a current event, but also the representations of past events, are reinforced during conditional associative learning. In addition, these findings are broadly consistent with the prediction of attractor network models of associative learning and their prophecy of a persistent representation of past objects.</p

Junkie love : romance and addiction on the big screen

This article investigates the filmic construction of two disparate but intertwining cultural practices: those engaging in the life-affirming rituals of romantic love and those performing the potentially self-destructive rituals of hard drug consumption. Discussing a number of key feature films from the (mini) genre “junkie love”, it aims to show what happens when elements of mainstream romantic drama merge with the horror conventions of the heroin addiction film. Drawing amongst others on Murray Smith’s theory of “levels of [spectator] engagement” and Greg Smith’s concept of the “emotion system”, the article concludes that junkie love films, using tropes of the romantic tragedy in the tradition of Romeo and Juliet, present a more complex and nuanced approach to drug addicts than the predominantly condemnatory media coverage—one that arguably invites the spectator’s understanding and compassion