22,218 research outputs found
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
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
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