Poisson factorization for peer-based anomaly detection

Anomaly detection systems are a promising tool to identify compromised user credentials and malicious insiders in enterprise networks. Most existing approaches for modelling user behaviour rely on either independent observations for each user or on pre-defined user peer groups. A method is proposed based on recommender system algorithms to learn overlapping user peer groups and to use this learned structure to detect anomalous activity. Results analysing the authentication and process-running activities of thousands of users show that the proposed method can detect compromised user accounts during a red team exercise

Using the seismology of non-magnetic chemically peculiar stars as a probe of dynamical processes in stellar interiors

Chemical composition is a good tracer of hydrodynamical processes that occur in stars as they often lead to mixing and particle transport. By comparing abundances predicted by models and those observed in stars we can infer some constraints on those mixing processes. As pulsations in stars are often very sensitive to chemical composition, we can use asteroseismology to probe the internal chemical composition of stars where no direct observations are possible. In this paper I focus on main sequence stars Am, lambda bootis, and HgMn stars and discuss what we can learn of mixing processes in those stars from seismology.Comment: 10 pages,6 figures. accepted in Journal of astrophysics and astronomy. proceedings of aries conferemce on asteroseismology. december 200

The temperature dependence of the isothermal bulk modulus at 1 bar pressure

It is well established that the product of the volume coefficient of thermal expansion and the bulk modulus is nearly constant at temperatures higher than the Debye temperature. Using this approximation allows predicting the values of the bulk modulus. The derived analytical solution for the temperature dependence of the isothermal bulk modulus has been applied to ten substances. The good correlations to the experiments indicate that the expression may be useful for substances for which bulk modulus data are lacking

Spontaneous thermal runaway as an ultimate failure mechanism of materials

The first theoretical estimate of the shear strength of a perfect crystal was given by Frenkel [Z. Phys. 37, 572 (1926)]. He assumed that as slip occurred, two rigid atomic rows in the crystal would move over each other along a slip plane. Based on this simple model, Frenkel derived the ultimate shear strength to be about one tenth of the shear modulus. Here we present a theoretical study showing that catastrophic material failure may occur below Frenkel's ultimate limit as a result of thermal runaway. We demonstrate that the condition for thermal runaway to occur is controlled by only two dimensionless variables and, based on the thermal runaway failure mechanism, we calculate the maximum shear strength $\sigma_c$ of viscoelastic materials. Moreover, during the thermal runaway process, the magnitude of strain and temperature progressively localize in space producing a narrow region of highly deformed material, i.e. a shear band. We then demonstrate the relevance of this new concept for material failure known to occur at scales ranging from nanometers to kilometers.Comment: 4 pages, 3 figures. Eq. (6) and Fig. 2a corrected; added references; improved quality of figure

Zipf's law in Multifragmentation

We discuss the meaning of Zipf's law in nuclear multifragmentation. We remark that Zipf's law is a consequence of a power law fragment size distribution with exponent $\tau \simeq 2$. We also recall why the presence of such distribution is not a reliable signal of a liquid-gas phase transition

Mixing and Accretion in lambda Bootis Stars

Strong evidence for deep mixing has been uncovered for slowly rotating F, and A stars of the main sequence. As the accretion/diffusion model for the formation of lboo stars is heavily dependent on mixing in superficial regions, such deep mixing may have important repercussions on our understanding of these stars. It is shown that deep mixing at a level similar to that of FmAm stars increases the amount of matter that needs to be accreted by the stars with respect with the standard models by some three orders of magnitude. It is also shown that significantly larger accretion rates have to be maintained, as high as $10^{-11}$~M_\sun yr^{-1}, to prevent meridional circulation from canceling the effect of accretion. The existence of old ($\approx 1$~Gyr) is not a likely outcome of the present models for accretion/diffusion with or without deep mixing. It is argued that lboo stars are potentially very good diagnostics of mixing mechanisms in moderately fast rotators.Comment: To appear in Astrophysical Journal Letters. 4 pages, 2 fgure

Scaling Analysis and Evolution Equation of the North Atlantic Oscillation Index Fluctuations

The North Atlantic Oscillation (NAO) monthly index is studied from 1825 till 2002 in order to identify the scaling ranges of its fluctuations upon different delay times and to find out whether or not it can be regarded as a Markov process. A Hurst rescaled range analysis and a detrended fluctuation analysis both indicate the existence of weakly persistent long range time correlations for the whole scaling range and time span hereby studied. Such correlations are similar to Brownian fluctuations. The Fokker-Planck equation is derived and Kramers-Moyal coefficients estimated from the data. They are interpreted in terms of a drift and a diffusion coefficient as in fluid mechanics. All partial distribution functions of the NAO monthly index fluctuations have a form close to a Gaussian, for all time lags, in agreement with the findings of the scaling analyses. This indicates the lack of predictive power of the present NAO monthly index. Yet there are some deviations for large (and thus rare) events. Whence suggestions for other measurements are made if some improved predictability of the weather/climate in the North Atlantic is of interest. The subsequent Langevin equation of the NAO signal fluctuations is explicitly written in terms of the diffusion and drift parameters, and a characteristic time scale for these is given in appendix.Comment: 6 figures, 54 refs., 16 pages; submitted to Int. J. Mod. Phys. C: Comput. Phy

Snow metamorphism: a fractal approach

Snow is a porous disordered medium consisting of air and three water phases: ice, vapour and liquid. The ice phase consists of an assemblage of grains, ice matrix, initially arranged over a random load bearing skeleton. The quantitative relationship between density and morphological characteristics of different snow microstructures is still an open issue. In this work, a three-dimensional fractal description of density corresponding to different snow microstructure is put forward. First, snow density is simulated in terms of a generalized Menger sponge model. Then, a fully three-dimensional compact stochastic fractal model is adopted. The latter approach yields a quantitative map of the randomness of the snow texture, which is described as a three-dimensional fractional Brownian field with the Hurst exponent H varying as continuous parameter. The Hurst exponent is found to be strongly dependent on snow morphology and density. The approach might be applied to all those cases where the morphological evolution of snow cover or ice sheets should be conveniently described at a quantitative level

Changepoint detection on a graph of time series

When analysing multiple time series that may be subject to changepoints, it is sometimes possible to specify a priori, by means of a graph G, which pairs of time series are likely to be impacted by simultaneous changepoints. This article proposes a novel Bayesian changepoint model for multiple time series that borrows strength across clusters of connected time series in G to detect weak signals for synchronous changepoints. The graphical changepoint model is further extended to allow dependence between nearby but not necessarily synchronous changepoints across neighbour time series in G. A novel reversible jump MCMC algorithm making use of auxiliary variables is proposed to sample from the graphical changepoint model. The merit of the proposed model is demonstrated via a changepoint analysis of real network authentication data from Los Alamos National Laboratory (LANL), with some success at detecting weak signals for network intrusions across users that are linked by network connectivity, whilst limiting the number of false alerts.Comment: 31 pages, 13 figure