Conformal Invariance and Shape-Dependent Conductance of Graphene Samples

For a sample of an arbitrary shape, the dependence of its conductance on the longitudinal and Hall conductivity is identical to that of a rectangle. We use analytic results for a conducting rectangle, combined with the semicircle model for transport coefficients, to study properties of the monolayer and bilayer graphene. A conductance plateau centered at the neutrality point, predicted for square geometry, is in agreement with recent experiments. For rectangular geometry, the conductance exhibits maxima at the densities of compressible quantum Hall states for wide samples, and minima for narrow samples. The positions and relative sizes of these features are different in the monolayer and bilayer cases, indicating that the conductance can be used as a tool for sample diagnostic.Comment: 9 pages, 6 figure

Wavelength-scale stationary-wave integrated Fourier-transform spectrometry

Spectrometry is a general physical-analysis approach for investigating light-matter interactions. However, the complex designs of existing spectrometers render them resistant to simplification and miniaturization, both of which are vital for applications in micro- and nanotechnology and which are now undergoing intensive research. Stationary-wave integrated Fourier-transform spectrometry (SWIFTS)-an approach based on direct intensity detection of a standing wave resulting from either reflection (as in the principle of colour photography by Gabriel Lippmann) or counterpropagative interference phenomenon-is expected to be able to overcome this drawback. Here, we present a SWIFTS-based spectrometer relying on an original optical near-field detection method in which optical nanoprobes are used to sample directly the evanescent standing wave in the waveguide. Combined with integrated optics, we report a way of reducing the volume of the spectrometer to a few hundreds of cubic wavelengths. This is the first attempt, using SWIFTS, to produce a very small integrated one-dimensional spectrometer suitable for applications where microspectrometers are essential

The roles of motivation and ability in controlling the consequences of stereotype suppression

Two experiments investigated the conditions under which previously suppressed stereotypes are applied in impression formation. In Experiment 1, the extent to which a previously suppressed racial stereotype influenced subsequent impressions depended on the race of the target who was subsequently encountered. Whereas impressions of race-unspecified targets were assimilated to the stereotype following its suppression, no such effects were observed when the target belonged to the racial group whose stereotype had been initially suppressed. These results demonstrate that when perceivers are motivated to avoid stereo-typing individuals, the influence of a stereotype that has been previously activated through suppression is minimized. Experiment 2 demonstrated that these processing goals effectively reduce the impact of suppression-activated stereotypes only when perceivers have sufficient capacity to enact the goals. These results suggest that both sufficient motivation and capacity are necessary to prevent heightened stereotyping following stereotype suppression

Development of an AmpliSeq (TM) Panel for Next-Generation Sequencing of a Set of Genetic Predictors of Persisting Pain

Background: Many gene variants modulate the individual perception of pain and possibly also its persistence. The limited selection of single functional variants is increasingly being replaced by analyses of the full coding and regulatory sequences of pain-relevant genes accessible by means of next generation sequencing (NGS). Methods: An NGS panel was created for a set of 77 human genes selected following different lines of evidence supporting their role in persisting pain. To address the role of these candidate genes, we established a sequencing assay based on a custom AmpliSeq (TM) panel to assess the exomic sequences in 72 subjects of Caucasian ethnicity. To identify the systems biology of the genes, the biological functions associated with these genes were assessed by means of a computational over-representation analysis. Results: Sequencing generated a median of 2.85 . 10(6) reads per run with a mean depth close to 200 reads, mean read length of 205 called bases and an average chip loading of 71%. A total of 3,185 genetic variants were called. A computational functional genomics analysis indicated that the proposed NGS gene panel covers biological processes identified previously as characterizing the functional genomics of persisting pain. Conclusion: Results of the NGS assay suggested that the produced nucleotide sequences are comparable to those earned with the classical Sanger sequencing technique. The assay is applicable for small to large-scale experimental setups to target the accessing of information about any nucleotide within the addressed genes in a study cohort.Peer reviewe

Evolving high-speed, easy-to-understand network intrusion detection rules with genetic programming

Proceeding of: EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG, Tübingen, Germany, April 15-17, 2009An ever-present problem in intrusion detection technology is how to construct the patterns of (good, bad or anomalous) behaviour upon which an engine have to make decisions regarding the nature of the activity observed in a system. This has traditionally been one of the central areas of research in the field, and most of the solutions proposed so far have relied in one way or another upon some form of data mining–with the exception, of course, of human-constructed patterns. In this paper, we explore the use of Genetic Programming (GP) for such a purpose. Our approach is not new in some aspects, as GP has already been partially explored in the past. Here we show that GP can offer at least two advantages over other classical mechanisms: it can produce very lightweight detection rules (something of extreme importance for high-speed networks or resource-constrained applications) and the simplicity of the patterns generated allows to easily understand the semantics of the underlying attack.Publicad

Quantum Hall transitions: An exact theory based on conformal restriction

We revisit the problem of the plateau transition in the integer quantum Hall effect. Here we develop an analytical approach for this transition, based on the theory of conformal restriction. This is a mathematical theory that was recently developed within the context of the Schramm-Loewner evolution which describes the stochastic geometry of fractal curves and other stochastic geometrical fractal objects in 2D space. Observables elucidating the connection with the plateau transition include the so-called point-contact conductances (PCCs) between points on the boundary of the sample, described within the language of the Chalker-Coddington network model. We show that the disorder-averaged PCCs are characterized by classical probabilities for certain geometric objects in the plane (pictures), occurring with positive statistical weights, that satisfy the crucial restriction property with respect to changes in the shape of the sample with absorbing boundaries. Upon combining this restriction property with the expected conformal invariance at the transition point, we employ the mathematical theory of conformal restriction measures to relate the disorder-averaged PCCs to correlation functions of primary operators in a conformal field theory (of central charge $c=0$). We show how this can be used to calculate these functions in a number of geometries with various boundary conditions. Since our results employ only the conformal restriction property, they are equally applicable to a number of other critical disordered electronic systems in 2D. For most of these systems, we also predict exact values of critical exponents related to the spatial behavior of various disorder-averaged PCCs.Comment: Published versio

Effectiveness evaluation of data mining based IDS

Proceeding of: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 14-15, 2006.Data mining has been widely applied to the problem of Intrusion Detection in computer networks. However, the misconception of the underlying problem has led to out of context results. This paper shows that factors such as the probability of intrusion and the costs of responding to detected intrusions must be taken into account in order to compare the effectiveness of machine learning algorithms over the intrusion detection domain. Furthermore, we show the advantages of combining different detection techniques. Results regarding the well known 1999 KDD dataset are shown.Publicad