Privacy is linking permission to purpose

The last years have seen a peak in privacy related research. The focus has been mostly on how to protect the individual from being tracked, with plenty of anonymizing solutions. We advocate another model that is closer to the “physical” world: we consider our privacy respected when our personal data is used for the purpose for which we gave it in the first place. Essentially, in any distributed authorization protocol, credentials should mention their purpose beside their powers. For this information to be meaningful we should link it to the functional requirements of the original application. We sketch how one can modify a requirement engineering methodology to incorporate security concerns so that we explicitly trace back the high-level goals for which a functionality has been delegated by a (human or software) agent to another one. Then one could be directly derive purpose-based trust management solutions from the requirements

Soft striped magnetic fluctuations competing with superconductivity in Fe1+xTe

Neutron spectroscopy is used to investigate the magnetic fluctuations in Fe1+xTe - a parent compound of chalcogenide superconductors. Incommensurate "stripelike" excitations soften with increased interstitial iron concentration. The energy crossover from incommensurate to stripy fluctuations defines an apparent hourglass dispersion. Application of sum rules of neutron scattering find that the integrated intensity is inconsistent with an S=1Fe2+ ground state and significantly less than S=2 predicted from weak crystal field arguments pointing towards the Fe2+ being in a superposition of orbital states. The results suggest that a highly anisotropic order competes with superconductivity in chalcogenide systems. © 2014 American Physical Society

Effects of sample design and landscape features on a measure of environmental heterogeneity

Environmental heterogeneity, an important influence on organisms and ecological processes, can be quantified by the variance of an environmental characteristic over all locations within a study extent. However on landscapes with autocorrelation and gradient patterns, estimating this variance from a sample of locations may lead to errors that cannot be corrected with statistical techniques. We analytically derived the relative expected sampling error of sample designs on landscapes with particular gradient pattern and autocorrelation features. We applied this closed-form approach to temperature observations from an existing study. The expected heterogeneity differed, both in magnitude and direction, amongst sample designs over the study site's likely range of autocorrelation and gradient features. We conducted a simulation study to understand the effects of (i) landscape variability and (ii) design variability on an average sampling error. On 10 000 simulated landscapes with varying gradient and autocorrelation features, we compared estimates of variance from a variety of structured and random sample designs. While gradient patterns and autocorrelation cause large errors for some designs, others yield near-zero average sampling error. Sample location spacing is a key factor in sample design performance. Random designs have larger range of possible sampling errors than structured designs due to the potential for sample arrangements that over- and under-sample certain areas of the landscape. When implementing a new sample design to quantify environmental heterogeneity via variance, we recommend using a simple structured design with appropriate sample spacing. For existing designs, we recommend calculating the relative expected sampling error via our analytical derivation