Spinal neurilemmomas and neurofibromas: central dot sign in postgadolinium MRI

The MR studies of three histologically proven spinal neurilemmomas and neurofibromas were reviewed retrospectively. There were two benign neurilemmomas (schwannomas) and one neurofibroma. The common characteristic of these cases was a central low intensity focus ("dot") seen on postcontrast T1-weighted imaging. The low intensity foci corresponded histologically to a congeries of changes including edema, microcysts, foam cells, hyalinization of blood vessels, old hemorrhage, and dystrophic calcification.     

A Monte-Carlo code for the detailed simulation of electron and light-ion tracks in condensed matter

In an effort to understand the basic mechanism of the action of charged particles in solid radiation dosimeters, we extend our Monte-Carlo code (MC4) to condensed media (liquids/solids) and present new track-structure calculations for electrons and protons. Modeling the energy dissipation process is based on a model dielectric function, which accounts in a semi-empirical and self-consistent way for condensed-phase effects which are computationally intractable. Importantly, these effects mostly influence track-structure characteristics at the nanometer scale, which is the focus of radiation action models. Since the event-by-event scheme for electron transport is impractical above several kilo-electron volts, a condensed-history random-walk scheme has been implemented to transport the energetic delta rays produced by energetic ions. Based on the above developments, new track-structure calculations are presented for two representative dosimetric materials, namely, liquid water and silicon. Results include radial dose distributions in cylindrical and spherical geometries, as well as, clustering distributions, which, among other things, are important in predicting irreparable damage in biological systems and prompt electric-fields in microelectronics.     

An empirical study on green practices of mobile phone users

Given the market’s saturation with smartphones and the increased power needs that these have as compared with older feature phones, the users’ green practices and behavior is emerging as an important research topic. The environmental aspects and general awareness issues are not addressed in this study; however, the limited battery life of smartphones is a decisive factor that shapes the users’ behavior, practices and preferences. As such, the users need to follow green practices and carefully assess the energy related characteristics (speed, screen size, weight, and price) that they value the most in a smartphone, to maximize their experience. Based on our previous work that gathered relevant data from 313 users, we extended the analysis in order to examine the user battery life and charging practices, buying habits, green practices and preferences. To that end, we used quantitative statistics and Fuzzy Decision Tree analysis to propose relevant Fuzzy Decision Rules that can classify the results and profile the users.     

The tanh transformation for the solution of singular integral equations

Using a tanh transformation a quadrature formula for the evaluation of singular integrals is obtained. The formula has the same step length h as the formula for regular integrals derived by F. Stenger. These quadrature formulae are valid for end point singularities of any order and their error exhibits an exponential decay when the number of integrations tends to infinity. Using these formulae the solution of singular integral equations does not depend on the order of the end point singularities. Furthermore the collocation points are given by a very simple equation and, in the case of constant coefficients, by a closed-form formula.     

Topics in Optical Communications - Service Differentiation and Traffic Engineering in IP over WDM Networks

The emergence of WDM-based optical transport has brought about unprecedented potential for ultra-high-speed IP core networks. It also holds great challenges, not least of which is to determine the optimal way of merging the optical domain with the IP domain. Beyond this issue, still a subject of research, the path to an efficient and resilient optical transmission IP core network is beset by numerous difficulties caused by shortcomings inherent to IP. Simply merging WDM and IP will not automatically produce a core network capable of serving the needs and adapting to the nature of diverse IP traffic. This article demonstrates the complexities of and proposes a possible solution for optimizing the next-generation core network, evolving the management plane IP/WDM integration model proposed in the 1ST WINMAN project. We concentrate on making the core network efficient for transporting differentiated service traffic, adaptive to changes in traffic patterns and resilient against possible failures, which would disrupt its operation. To these ends we introduce MPLS TE and DiffServ QoS functionality in our network model, thus ensuring maximum utilization of resources and automated guarantees of optimal quality for different types of transported traffic.     

Computational models can replicate the capacity of human recognition memory

The capacity of human recognition memory was investigated by Standing, who presented several groups of participants with different numbers of pictures (from 20 to 10 000), and subsequently tested their ability to distinguish between previously presented and novel pictures. The estimated number of pictures retained in recognition memory by different groups when plotted as a logarithmic function of the number of pictures presented formed a straight line, representing a power-law relationship. Here, we investigate if published models of familiarity discrimination can replicate Standing's results. We first consider a simplified assumption that visual stimuli are represented by uncorrelated patterns of firing of visual neurons providing input to the familiarity discrimination network. We show that for this case three models (Familiarity discrimination based on Energy (FamE), Anti-Hebbian and Info-max) can reproduce the observed power-law relationship when their synaptic weights are appropriately initialized. For more realistic assumptions on neural representation of stimuli, the FamE model is no longer able to reproduce the power-law relationship in simulations, while the Anti-Hebbian and Info-max can reproduce it. Nevertheless, the slopes of the power-law relationships produced by the models in all simulations differ from that observed by Standing. We discuss possible reasons for this difference, including separate contributions of familiarity and recollection processes, and describe experimentally testable predictions based on our analysis.     

Improving network anomaly detection via selective flow-based sampling

Sampling has become an essential component of scalable Internet traffic monitoring and anomaly detection. A new flow-based sampling technique that focuses on the selection of small flows, which are usually the source of malicious traffic, is introduced and analysed. The proposed approach provides a flexible framework for preferential flow sampling that can effectively balance the tradeoff between the volume of the processed information and the anomaly detection accuracy. The performance evaluation of the impact of selective flow-based sampling on the anomaly detection process is achieved through the adoption and application of a sequential non-parametric change-point anomaly detection method on realistic data that have been collected from a real operational university campus network. The corresponding numerical results demonstrate that the proposed approach achieves to improve anomaly detection effectiveness and at the same time reduces the number of selected flows.     

Network anomaly detection and classification via opportunistic sampling

Abstract In this article the emphasis is placed on the evaluation of the impact of intelligent flow sampling techniques on the detection and classification of network anomalies. Based on the observation that for specific-purpose applications such as anomaly detection a large fraction of information is contained in a small fraction of flows, we demonstrate that by using sampling techniques that opportunistically and preferentially sample traffic data, we achieve ?magnification? of the appearance of anomalies within the sampled data set and therefore improve their detection. Therefore, the inherently ?lossy? sampling process is transformed to an advantageous feature in the anomaly detection case, allowing the revealing of anomalies that would be otherwise untraceable, and thus becoming the vehicle for efficient anomaly detection and classification. The evaluation of the impact of intelligent sampling techniques on the anomaly detection process is based on the application of an entropy-based anomaly detection method on a packet trace with data that has been collected from a real operational university campus network.