Automatic, fast and robust characterization of noise distributions for diffusion MRI

Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging methods, the number of receiver coils and imaging filters applied by the scanner, amongst other factors, dictate the resulting signal distribution. Accurate estimation beyond textbook Rician or noncentral chi distributions often requires information about the acquisition process (e.g. coils sensitivity maps or reconstruction coefficients), which is not usually available. We introduce a new method where a change of variable naturally gives rise to a particular form of the gamma distribution for background signals. The first moments and maximum likelihood estimators of this gamma distribution explicitly depend on the number of coils, making it possible to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the method automatic and robust to artifacts. Experiments on synthetic datasets show that the proposed method can reliably estimate both the degrees of freedom and the standard deviation. The worst case errors range from below 2% (spatially uniform noise) to approximately 10% (spatially variable noise). Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variances than compared methods.Comment: v2: added publisher DOI statement, fixed text typo in appendix A

Smashing WEP in A Passive Attack

In this paper, we report extremely fast and optimised active and passive attacks against the old IEEE 802.11 wireless communication protocol WEP. This was achieved through a huge amount of theoretical and experimental analysis (capturing WiFi packets), refinement and optimisation of all the former known attacks and methodologies against RC4 stream cipher in WEP mode. We support all our claims by providing an implementation of this attack as a publicly available patch on Aircrack-ng. Our new attacks improve its success probability drastically. We adapt our theoretical analysis in Eurocrypt 2011 to real-world scenarios and we perform a slight adjustment to match the empirical observations. Our active attack, based on ARP injection, requires 22 500 packets to gain success probability of 50% against a 104-bit WEP key, using Aircrack-ng in non-interactive mode. It runs in less than 5 seconds on an off-the-shelf PC. Using the same number of packets, Aicrack-ng yields around 3% success rate. Furthermore, we describe very fast passive only attacks by just eavesdropping TCP/IPv4 packets in a WiFi communication. Our passive attack requires 27 500 packets. This is much less than the number of packets Aircrack-ng requires in active mode (around 37 500), which is a huge improvement.We believe that our analysis brings on further insight to the security of RC4

Application of Markov models to area-average daily precipitation series and interannual variability in seasonal totals

This paper examines the success of various Markov-chain models of daily precipitation series in reproducing the characteristics of area-average rainfall in Britain. The first model considered is the standard twos-tate first-order Markov renewal process coupled to an amount model using the incomplete G-probability distribution. We find that variability of seasonal totals and autocorrelation of daily amounts are both too small in this model, compared with observations. These are serious deficiencies, often overlooked, and possibly related. We proceed to consider models involving Markov chains of higher (temporal) order and many states, both of which generalizations may increase autocorrelation. A second-order two-state model is no better than the first-order, but a first-order many-state model captures a high fraction of the seasonal variability, because use of many states improves the model's representation of spells of heavy precipitation, which appear to have a considerable influence on the seasonal variance. Better still is a second-order many-state model, a type which, to our knowledge, has not previously been investigated. We suggest that the best model would have a continuum of states, rather than a discrete set. Our conclusion is that a large proportion of seasonal variability may be explained in terms of the average daily structure, but there may be a residual component caused by processes operating on longer time-scales and possibly predictable with reference to these. Reproduction of long-period (e.g. monthly or seasonal) variance and of the structure of daily autocorrelation provide crucial tests of stochastic "weather generators", and we recommend that models which fail to simulate these statistics realistically be used only with great caution