Magnetoresistance in Disordered Graphene: The Role of Pseudospin and Dimensionality Effects Unraveled

We report a theoretical low-field magnetotransport study unveiling the effect of pseudospin in realistic models of weakly disordered graphene-based materials. Using an efficient Kubo computational method, and simulating the effect of charges trapped in the oxide, different magnetoconductance fingerprints are numerically obtained in system sizes as large as 0.3 micronmeter squared, containing tens of millions of carbon atoms. In two-dimensional graphene, a strong valley mixing is found to irreparably yield a positive magnetoconductance (weak localization), whereas crossovers from positive to a negative magnetoconductance (weak antilocalization) are obtained by reducing disorder strength down to the ballistic limit. In sharp contrast, graphene nanoribbons with lateral size as large as 10nm show no sign of weak antilocalization, even for very small disorder strength. Our results rationalize the emergence of a complex phase diagram of magnetoconductance fingerprints, shedding some new light on the microscopical origin of pseudospin effects.Comment: 8 pages, 5 figure

Otolith Microchemical Fingerprints of Age-0 Red Snapper, Lutjanus campechanus, from the Northern Gulf of Mexico

Red snapper, Lutjanus campechanus, in the northern Gulf of Mexico (Gulf) are believed to constitute a single stock. However, tagging and genetics studies suggest there is little mixing between populations of red snapper in the northern Gulf, and little is known about mixing rates of adult fish. The long-term goal of our work is to determine if age-0 red snapper from different nursery areas have unique microchemical fingerprints in their sagittal otoliths, and if so, can the microchemical fingerprints at the core of adult otoliths be used to determine retrospectively nursery area of origin. Ultimately, we hope to use the microchemical fingerprints at the core of adult snapper otoliths to estimate adults\u27 mixing rates and movement patterns. In this study, the objective was to determine if age-0 red snapper collected from different northern Gulf nursery areas in summer and fall 1995 did contain unique microchemical fingerprints. Sagittal otoliths of age-0 red snapper collected off the coasts of Alabama/Mississippi, Louisiana, and Texas were analyzed using inductively coupled plasma atomic emission spectrometry (ICP-AES). Twelve elements in the sagittae of age-0 snapper were analyzed with ICP-AES. Of these, eight were put into a stepwise discriminant function analysis with the best-fitted model including Mg, Se, As, Fe, and AI, entered in that order (MANOVA, P \u3c 0.001). Cross-validated classification accuracies were 92% for Texas fish, 91% for Louisiana fish, and 92% for Alabama/Mississippi fish. Therefore, it appears that otolith microchemistry can be used to infer nursery area of age-0 red snapper. Future work will focus on (1) establishing the temporal stability of age-0 red snapper otolith microchemical fingerprints and (2) inclusion of analyses of age-structured samples from adult red snapper otolith cores to estimate their nursery area of origin and mixing rates

Establishing Lagrangian connections between observations within air masses crossing the Atlantic during the International Consortium for Atmospheric Research on Transport and Transformation experiment

The ITCT-Lagrangian-2K4 (Intercontinental Transport and Chemical Transformation) experiment was conceived with an aim to quantify the effects of photochemistry and mixing on the transformation of air masses in the free troposphere away from emissions. To this end, attempts were made to intercept and sample air masses several times during their journey across the North Atlantic using four aircraft based in New Hampshire (USA), Faial (Azores) and Creil (France). This article begins by describing forecasts from two Lagrangian models that were used to direct the aircraft into target air masses. A novel technique then identifies Lagrangian matches between flight segments. Two independent searches are conducted: for Lagrangian model matches and for pairs of whole air samples with matching hydrocarbon fingerprints. The information is filtered further by searching for matching hydrocarbon samples that are linked by matching trajectories. The quality of these "coincident matches'' is assessed using temperature, humidity and tracer observations. The technique pulls out five clear Lagrangian cases covering a variety of situations and these are examined in detail. The matching trajectories and hydrocarbon fingerprints are shown, and the downwind minus upwind differences in tracers are discussed

High-temporal resolution fluvial sediment source fingerprinting with uncertainty: a Bayesian approach

This contribution addresses two developing areas of sediment fingerprinting research. Specifically, how to improve the temporal resolution of source apportionment estimates whilst minimizing analytical costs and, secondly, how to consistently quantify all perceived uncertainties associated with the sediment mixing model procedure. This first matter is tackled by using direct X-ray fluorescence spectroscopy (XRFS) and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) analyses of suspended particulate matter (SPM) covered filter papers in conjunction with automatic water samplers. This method enables SPM geochemistry to be quickly, accurately, inexpensively and non-destructively monitored at high-temporal resolution throughout the progression of numerous precipitation events. We then employed a Bayesian mixing model procedure to provide full characterization of spatial geochemical variability, instrument precision and residual error to yield a realistic and coherent assessment of the uncertainties associated with source apportionment estimates. Applying these methods to SPM data from the River Wensum catchment, UK, we have been able to apportion, with uncertainty, sediment contributions from eroding arable topsoils, damaged road verges and combined subsurface channel bank and agricultural field drain sources at 60- and 120-minute resolution for the duration of five precipitation events. The results presented here demonstrate how combining Bayesian mixing models with the direct spectroscopic analysis of SPM-covered filter papers can produce high-temporal resolution source apportionment estimates that can assist with the appropriate targeting of sediment pollution mitigation measures at a catchment level

Transfer Learning for Device Fingerprinting with Application to Cognitive Radio Networks

Primary user emulation (PUE) attacks are an emerging threat to cognitive radio (CR) networks in which malicious users imitate the primary users (PUs) signals to limit the access of secondary users (SUs). Ascertaining the identity of the devices is a key technical challenge that must be overcome to thwart the threat of PUE attacks. Typically, detection of PUE attacks is done by inspecting the signals coming from all the devices in the system, and then using these signals to form unique fingerprints for each device. Current detection and fingerprinting approaches require certain conditions to hold in order to effectively detect attackers. Such conditions include the need for a sufficient amount of fingerprint data for users or the existence of both the attacker and the victim PU within the same time frame. These conditions are necessary because current methods lack the ability to learn the behavior of both SUs and PUs with time. In this paper, a novel transfer learning (TL) approach is proposed, in which abstract knowledge about PUs and SUs is transferred from past time frames to improve the detection process at future time frames. The proposed approach extracts a high level representation for the environment at every time frame. This high level information is accumulated to form an abstract knowledge database. The CR system then utilizes this database to accurately detect PUE attacks even if an insufficient amount of fingerprint data is available at the current time frame. The dynamic structure of the proposed approach uses the final detection decisions to update the abstract knowledge database for future runs. Simulation results show that the proposed method can improve the performance with an average of 3.5% for only 10% relevant information between the past knowledge and the current environment signals.Comment: 6 pages, 3 figures, in Proceedings of IEEE 26th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Hong Kong, P.R. China, Aug. 201

Hierarchical mixture models for assessing fingerprint individuality

The study of fingerprint individuality aims to determine to what extent a fingerprint uniquely identifies an individual. Recent court cases have highlighted the need for measures of fingerprint individuality when a person is identified based on fingerprint evidence. The main challenge in studies of fingerprint individuality is to adequately capture the variability of fingerprint features in a population. In this paper hierarchical mixture models are introduced to infer the extent of individualization. Hierarchical mixtures utilize complementary aspects of mixtures at different levels of the hierarchy. At the first (top) level, a mixture is used to represent homogeneous groups of fingerprints in the population, whereas at the second level, nested mixtures are used as flexible representations of distributions of features from each fingerprint. Inference for hierarchical mixtures is more challenging since the number of unknown mixture components arise in both the first and second levels of the hierarchy. A Bayesian approach based on reversible jump Markov chain Monte Carlo methodology is developed for the inference of all unknown parameters of hierarchical mixtures. The methodology is illustrated on fingerprint images from the NIST database and is used to make inference on fingerprint individuality estimates from this population.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS266 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Gyrokinetic analysis and simulation of pedestals, to identify the culprits for energy losses using fingerprints

Fusion performance in tokamaks hinges critically on the efficacy of the Edge Transport Barrier (ETB) at suppressing energy losses. The new concept of fingerprints is introduced to identify the instabilities that cause the transport losses in the ETB of many of today's experiments, from widely posited candidates. Analysis of the Gyrokinetic-Maxwell equations, and gyrokinetic simulations of experiments, find that each mode type produces characteristic ratios of transport in the various channels: density, heat and impurities. This, together with experimental observations of transport in some channel, or, of the relative size of the driving sources of channels, can identify or determine the dominant modes causing energy transport. In multiple ELMy H-mode cases that are examined, these fingerprints indicate that MHD-like modes are apparently not the dominant agent of energy transport; rather, this role is played by Micro-Tearing Modes (MTM) and Electron Temperature Gradient (ETG) modes, and in addition, possibly Ion Temperature Gradient (ITG)/Trapped Electron Modes (ITG/TEM) on JET. MHD-like modes may dominate the electron particle losses. Fluctuation frequency can also be an important means of identification, and is often closely related to the transport fingerprint. The analytical arguments unify and explain previously disparate experimental observations on multiple devices, including DIII-D, JET and ASDEX-U, and detailed simulations of two DIII-D ETBs also demonstrate and corroborate this

Experimental assessment of particle mixing fingerprints in the deposit-feeding bivalve Abra alba (Wood)

Particle mixing induced by the deposit-feeding bivalve Abra alba was assessed using a new experimental approach allowing for the tracking of individual particle displacements. This approach combines the adaptation of existing image acquisition techniques with new image analysis software that tracks the position of individual particles. This led to measurements of particle mixing fingerprints, namely the frequency distributions of particle waiting times, and of the characteristics (i.e. direction and length) of their jumps. The validity of this new approach was assessed by comparing the so-measured frequency distributions of jump characteristics with the current qualitative knowledge regarding particle mixing in the genus Abra. Frequency distributions were complex due to the coexistence of several types of particle displacements and cannot be fitted with the most commonly used procedures when using the Continuous Time Random Walk (CTRW) model. Our approach allowed for the spatial analysis of particle mixing, which showed: 1) longer waiting times; 2) more frequent vertical jumps; and 3) shorter jump lengths deep in the sediment column than close to the sediment-water interface. This resulted in lower DbX and DbY (vertical and horizontal particle mixing bioffusion coefficients) deep in the sediment column. Our results underline the needs for: 1) preliminary checks of the adequacy of selected distributions to the species/communities studied; and 2) an assessment of vertical changes in particle mixing fingerprints when using CTRW

On the Evidence for Axion-like Particles from Active Galactic Nuclei

Burrage, Davis, and Shaw recently suggested exploiting the correlations between high and low energy luminosities of astrophysical objects to probe possible mixing between photons and axion-like particles (ALP) in magnetic field regions. They also presented evidence for the existence of ALP's by analyzing the optical/UV and X-ray monochromatic luminosities of AGNs. We extend their work by using the monochromatic luminosities of 320 unobscured Active Galactic Nuclei from the Sloan Digital Sky Survey/Xmm-Newton Quasar Survey (Young et al., 2009), which allows the exploration of 18 different combinations of optical/UV and X-ray monochromatic luminosities. However, we do not find compelling evidence for the existence of ALPs. Moreover, it appears that the signal reported by Burrage et al. is more likely due to X-ray absorption rather than to photon-ALP oscillation.Comment: 16 pages, 12 figures. Updated to reflect the minor changes introduced in the published versio

Nano-scale composition of commercial white powders for development of latent fingerprints on adhesives

This is the post-print version of the article - Copyright @ 2010 Elsevier.Titanium dioxide based powders are regularly used in the development of latent fingerprints on dark surfaces. For analysis of prints on adhesive tapes, the titanium dioxide can be suspended in a surfactant and used in the form of a powder suspension. Commercially available products, whilst having nominally similar composition, show varying levels of effectiveness of print development, with some powders adhering to the background as well as the print. X-ray fluorescence (XRF), analytical transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and laser particle sizing of the fingerprint powders show TiO2 particles with a surrounding coating, tens of nanometres thick, consisting of Al and Si rich material, with traces of sodium and sulphur. Such aluminosilicates are commonly used as anti-caking agents and to aid adhesion or functionality of some fingerprint powders; however, the morphology, thickness, coverage and composition of the aluminosilicates are the primary differences between the white powder formulations and could be related to variation in the efficacy of print development.This work is part funded by the Home Office Scientific Development Branch, UK