Forensic Data Mining: Finding Intrusion Patterns in Evidentiary Data

In The extensive growth of computing networks and tools and tricks for intruding into and attacking networks has underscored the importance of intrusion detection in network security. Yet, contemporary intrusion detection systems (IDS) are limiting in that they typically employ a misuse detection strategy, with searches for patterns of program or user behavior that match known intrusion scenarios, or signatures. Accordingly, there is a need for more robust and adaptive methods for designing and updating intrusion detection systems. One promising approach is the use of data mining methods for discovering intrusion patterns. Discovered patterns and profiles can be translated into classifiers for detecting deviations from normal usage patterns. Among promising mining methods are association rules, link analysis, and rule-induction algorithms. Our particular contribution is a unique approach to combining association rules with link analysis and a rule-induction algorithm to augment intrusion detection systems

Transport properties of dense dissipitive hard-sphere fluids for arbitrary energy loss models

The revised Enskog approximation for a fluid of hard spheres which lose energy upon collision is discussed for the case that the energy is lost from the normal component of the velocity at collision but is otherwise arbitrary. Granular fluids with a velocity-dependent coefficient of restitution are an important special case covered by this model. A normal solution to the Enskog equation is developed using the Chapman-Enskog expansion. The lowest order solution describes the general homogeneous cooling state and a generating function formalism is introduced for the determination of the distribution function. The first order solution, evaluated in the lowest Sonine approximation, provides estimates for the transport coefficients for the Navier-Stokes hydrodynamic description. All calculations are performed in an arbitrary number of dimensions.Comment: 27 pages + 1 figur

Combining Crop Models and Remote Sensing for Yield Prediction: Concepts, Applications and Challenges for Heterogeneous Smallholder Environments

JRC, CCAFS jointly sponsored the workshop on June 13-14, 2012, at the JRC in Ispra, Italy, to identify avenues for exploiting remote sensing information to improving crop forecasting in smallholder farming environments. The workshop’s objectives were: 1) To advance the state-of-knowledge of data assimilation for crop yield forecasting; 2) To address challenges and needs for successful applications of data assimilation in forecasting crop yields in heterogeneous, smallholder environments; and, 3) To enhance collaboration and exchange of knowledge among data assimilation and crop forecasting groups. The workshop succeeded in bringing together scientists from around the world. This has enabled discussions on research and results and has greatly enhanced collaboration and exchange of knowledge, especially about data assimilation and crop forecasting

The White Dwarf Cooling Sequence of NGC6397

We present the results of a deep Hubble Space Telescope (HST) exposure of the nearby globular cluster NGC6397, focussing attention on the cluster's white dwarf cooling sequence. This sequence is shown to extend over 5 magnitudes in depth, with an apparent cutoff at magnitude F814W=27.6. We demonstrate, using both artificial star tests and the detectability of background galaxies at fainter magnitudes, that the cutoff is real and represents the truncation of the white dwarf luminosity function in this cluster. We perform a detailed comparison between cooling models and the observed distribution of white dwarfs in colour and magnitude, taking into account uncertainties in distance, extinction, white dwarf mass, progenitor lifetimes, binarity and cooling model uncertainties. After marginalising over these variables, we obtain values for the cluster distance modulus and age of \mu_0 = 12.02 \pm 0.06 and T_c = 11.47 \pm 0.47Gyr (95% confidence limits). Our inferred distance and white dwarf initial-final mass relations are in good agreement with other independent determinations, and the cluster age is consistent with, but more precise than, prior determinations made using the main sequence turnoff method. In particular, within the context of the currently accepted \Lambda CDM cosmological model, this age places the formation of NGC6397 at a redshift z=3, at a time when the cosmological star formation rate was approaching its peak.Comment: 56 pages, 30 figure

Deep subcutaneous application of poly-L-lactic acid as a filler for facial lipoatrophy in HIV-infected patients

Introduction: Facial lipoatrophy is a crucial problem of HIV-infected patients undergoing highly active antiretroviral therapy (HAART). Poly-L-lactic acid (PLA), provided as New-Fill(R)/Sculptra(TM), is known as one possible treatment option. In 2004 PLA was approved by the FDA as Sculptra(TM) for the treatment of lipoatrophy of the face in HIV-infected patients. While the first trials demonstrated relevant efficacy, this was to some extent linked to unwanted effects. As the depth of injection was considered relevant in this context, the application modalities of the preparation were changed. The preparation was to be injected more deeply into subcutaneous tissue, after increased dilution. Material and Methods: To test this approach we performed a pilot study following the new recommendations in 14 patients. Results: While the efficacy turned out to be about the same, tolerability was markedly improved. The increase in facial dermal thickness was particularly obvious in those patients who had suffered from lipoatrophy for a comparatively small period of time. Conclusion: With the new recommendations to dilute PLA powder and to inject it into the deeper subcutaneous tissue nodule formation is a minor problem. However, good treatment results can only be achieved if lipoatrophy is not too intense; treatment intervals should be about 2 - 3 weeks. Copyright (C) 2005 S. Karger AG, Basel

Hubble Space Telescope Near-Ultraviolet Spectroscopy of Bright CEMP-s Stars

We present an elemental-abundance analysis, in the near-ultraviolet (NUV) spectral range, for the bright carbon-enhanced metal-poor (CEMP) stars HD196944 (V = 8.40, [Fe/H] = -2.41) and HD201626 (V = 8.16, [Fe/H] = -1.51), based on data acquired with the Space Telescope Imaging Spectrograph (STIS) on the Hubble Space Telescope. Both of these stars belong to the sub-class CEMP-s, and exhibit clear over-abundances of heavy elements associated with production by the slow neutron-capture process. HD196944 has been well-studied in the optical region, but we are able to add abundance results for six species (Ge, Nb, Mo, Lu, Pt, and Au) that are only accessible in the NUV. In addition, we provide the first determination of its orbital period, P=1325 days. HD201626 has only a limited number of abundance results based on previous optical work -- here we add five new species from the NUV, including Pb. We compare these results with models of binary-system evolution and s-process element production in stars on the asymptotic giant branch, aiming to explain their origin and evolution. Our best-fitting models for HD 196944 (M1,i = 0.9Mo, M2,i = 0.86Mo, for [Fe/H]=-2.2), and HD 201626 (M1,i = 0.9Mo , M2,i = 0.76Mo , for [Fe/H]=-2.2; M1,i = 1.6Mo , M2,i = 0.59Mo, for [Fe/H]=-1.5) are consistent with the current accepted scenario for the formation of CEMP-s stars.Comment: 25 pages, 13 figures; accepted for publication in Ap

Stellar-Mass Black Holes in the Solar Neighborhood

We search for nearby, isolated, accreting, ``stellar-mass'' (3 to $100M_\odot$) black holes. Models suggest a synchrotron spectrum in visible wavelengths and some emission in X-ray wavelengths. Of 3.7 million objects in the Sloan Digital Sky Survey Early Data Release, about 150,000 objects have colors and properties consistent with such a spectrum, and 87 of these objects are X-ray sources from the ROSAT All Sky Survey. Thirty-two of these have been confirmed not to be black-holes using optical spectra. We give the positions and colors of these 55 black-hole candidates, and quantitatively rank them on their likelihood to be black holes. We discuss uncertainties the expected number of sources, and the contribution of blackholes to local dark matter.Comment: Replaced with version accepted by ApJ. 40 pages, 8 figure

Modeling Semantic Encoding in a Common Neural Representational Space

Encoding models for mapping voxelwise semantic tuning are typically estimated separately for each individual, limiting their generalizability. In the current report, we develop a method for estimating semantic encoding models that generalize across individuals. Functional MRI was used to measure brain responses while participants freely viewed a naturalistic audiovisual movie. Word embeddings capturing agent-, action-, object-, and scene-related semantic content were assigned to each imaging volume based on an annotation of the film. We constructed both conventional within-subject semantic encoding models and between-subject models where the model was trained on a subset of participants and validated on a left-out participant. Between-subject models were trained using cortical surface-based anatomical normalization or surface-based whole-cortex hyperalignment. We used hyperalignment to project group data into an individual’s unique anatomical space via a common representational space, thus leveraging a larger volume of data for out-of-sample prediction while preserving the individual’s fine-grained functional–anatomical idiosyncrasies. Our findings demonstrate that anatomical normalization degrades the spatial specificity of between-subject encoding models relative to within-subject models. Hyperalignment, on the other hand, recovers the spatial specificity of semantic tuning lost during anatomical normalization, and yields model performance exceeding that of within-subject models

Modeling Semantic Encoding in a Common Neural Representational Space

Encoding models for mapping voxelwise semantic tuning are typically estimated separately for each individual, limiting their generalizability. In the current report, we develop a method for estimating semantic encoding models that generalize across individuals. Functional MRI was used to measure brain responses while participants freely viewed a naturalistic audiovisual movie. Word embeddings capturing agent-, action-, object-, and scene-related semantic content were assigned to each imaging volume based on an annotation of the film. We constructed both conventional within-subject semantic encoding models and between-subject models where the model was trained on a subset of participants and validated on a left-out participant. Between-subject models were trained using cortical surface-based anatomical normalization or surface-based whole-cortex hyperalignment. We used hyperalignment to project group data into an individual’s unique anatomical space via a common representational space, thus leveraging a larger volume of data for out-of-sample prediction while preserving the individual’s fine-grained functional–anatomical idiosyncrasies. Our findings demonstrate that anatomical normalization degrades the spatial specificity of between-subject encoding models relative to within-subject models. Hyperalignment, on the other hand, recovers the spatial specificity of semantic tuning lost during anatomical normalization, and yields model performance exceeding that of within-subject models

The Cluster Mass Function from Early SDSS Data: Cosmological Implications

The mass function of clusters of galaxies is determined from 400 deg^2 of early commissioning imaging data of the Sloan Digital Sky Survey; ~300 clusters in the redshift range z = 0.1 - 0.2 are used. Clusters are selected using two independent selection methods: a Matched Filter and a red-sequence color magnitude technique. The two methods yield consistent results. The cluster mass function is compared with large-scale cosmological simulations. We find a best-fit cluster normalization relation of sigma_8*omega_m^0.6 = 0.33 +- 0.03 (for 0.1 ~< omega_m ~< 0.4), or equivalently sigma_8 = (0.16/omega_m)^0.6. The amplitude of this relation is significantly lower than the previous canonical value, implying that either omega_m is lower than previously expected (omega_m = 0.16 if sigma_8 = 1) or sigma_8 is lower than expected (sigma_8 = 0.7 if omega_m = 0.3). The best-fit mass function parameters are omega_m = 0.19 (+0.08,-0.07) and sigma_8 = 0.9 (+0.3,-0.2). High values of omega_m (>= 0.4) and low sigma_8 (=~ 2 sigma.Comment: AASTeX, 25 pages, including 7 figures, accepted for publication in ApJ, vol.585, March 200