Structural Analysis of Network Traffic Flows

Network traffic arises from the superposition of Origin-Destination (OD) flows. Hence, a thorough understanding of OD flows is essential for modeling network traffic, and for addressing a wide variety of problems including traffic engineering, traffic matrix estimation, capacity planning, forecasting and anomaly detection. However, to date, OD flows have not been closely studied, and there is very little known about their properties. We present the first analysis of complete sets of OD flow timeseries, taken from two different backbone networks (Abilene and Sprint-Europe). Using Principal Component Analysis (PCA), we find that the set of OD flows has small intrinsic dimension. In fact, even in a network with over a hundred OD flows, these flows can be accurately modeled in time using a small number (10 or less) of independent components or dimensions. We also show how to use PCA to systematically decompose the structure of OD flow timeseries into three main constituents: common periodic trends, short-lived bursts, and noise. We provide insight into how the various constituents contribute to the overall structure of OD flows and explore the extent to which this decomposition varies over time.

Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.

Far-field analysis of axially symmetric three-dimensional directional cloaks.

Axisymmetric radiating and scattering structures whose rotational invariance is broken by non-axisymmetric excitations present an important class of problems in electromagnetics. For such problems, a cylindrical wave decomposition formalism can be used to efficiently obtain numerical solutions to the full-wave frequency-domain problem. Often, the far-field, or Fraunhofer region is of particular interest in scattering cross-section and radiation pattern calculations; yet, it is usually impractical to compute full-wave solutions for this region. Here, we propose a generalization of the Stratton-Chu far-field integral adapted for 2.5D formalism. The integration over a closed, axially symmetric surface is analytically reduced to a line integral on a meridional plane. We benchmark this computational technique by comparing it with analytical Mie solutions for a plasmonic nanoparticle, and apply it to the design of a three-dimensional polarization-insensitive cloak.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. RESULTS: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. CONCLUSIONS: Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Decomposition of in situ particulate absorption spectra

A global dataset of in situ particulate absorption spectra has been decomposed into component functions representing absorption by phytoplankton pigments and non-algal particles. The magnitudes of component Gaussian functions, used to represent absorption by individual or groups of pigments, are well correlated with pigment concentrations determined using High Performance Liquid Chromatography. We are able to predict the presence of chlorophylls a,ba,b, and cc, as well as two different groups of summed carotenoid pigments with percent errors between 30% and 57%. Existing methods of analysis of particulate absorption spectra measured in situ provide for only chlorophyll aa; the method presented here, using high spectral resolution particulate absorption, shows the ability to obtain the concentrations of additional pigments, allowing for more detailed studies of phytoplankton ecology than currently possible with in-situ spectroscopy.

Quantitative analysis of the reactivity of formate species seen by DRIFTS over a Au/Ce(La)O2 water–gas shift catalyst: First unambiguous evidence of the minority role of formates as reaction intermediates

The reactivity of the species formed at the surface of a Au/Ce(La)O2 catalyst during the water������¢���¯���¿���½���¯���¿���½gas shift (WGS) reaction were investigated by operando diffuse reflectance Fourier transform spectroscopy (DRIFTS) at the chemical steady state during isotopic transient kinetic analyses (SSITKA). The exchanges of the reaction product CO2 and of formate and carbonate surface species were followed during an isotopic exchange of the reactant CO using a DRIFTS cell as a single reactor. The DRIFTS cell was a modified commercial cell that yielded identical reaction rates to that measured over a quartz plug-flow reactor. The DRIFTS signal was used to quantify the relative oncentrations of the surface species and CO2. The analysis of the formate exchange curves between 428 and 493 K showed that at least two levels of reactivity were present. ������¢���¯���¿���½���¯���¿���½Slow formates������¢���¯���¿���½���¯���¿���½ displayed an exchange rate constant 10- to 20-fold slower than that of the reaction product CO2. ������¢���¯���¿���½���¯���¿���½Fast formates������¢���¯���¿���½���¯���¿���½ were exchanged on a time scale similar to that of CO2. Multiple nonreactive readsorption of CO2 took place, accounting for the kinetics of the exchange of CO2(g) and making it impossible to determine the number of active sites through the SSITKA technique. The concentration (in mol g������¢���¯���¿���½���¯���¿���½1) of formates on the catalyst was determined through a calibration curve and allowed calculation of the specific rate of formate decomposition. The rate of CO2 formation was more than an order of magnitude higher than the rate of decomposition of formates (slow + fast species), indicating that all of the formates detected by DRIFTS could not be the main reaction intermediates in the production of CO2. This work stresses the importance of full quantitative analyses (measuring both rate constants and adsorbate concentrations) when investigating the role of adsorbates as potential reaction intermediates, and illustrates how even reactive species seen by DRIFTS may be unimportant in the overall reaction scheme.

TAP studies of ammonia decomposition over Ru and Ir catalysts

The Temporal Analysis of Products (TAP) technique has been used to investigate the mechanism involved in the catalytic decomposition of NH3 over a series of catalysts consisting of activated carbon supported Ru (promoted and non-promoted with Na) and over an activated carbon supported Ir. An extensive study of the role played by both the support and the promoter in the

Human figure segmentation using independent component analysis

In this paper, we present a Statistical Shape Model for Human Figure Segmentation in gait sequences. Point Distribution Models (PDM) generally use Principal Component analysis (PCA) to describe the main directions of variation in the training set. However, PCA assumes a number of restrictions on the data that do not always hold. In this work, we explore the potential of Independent Component Analysis (ICA) as an alternative shape decomposition to the PDM-based Human Figure Segmentation. The shape model obtained enables accurate estimation of human figures despite segmentation errors in the input silhouettes and has really good convergence qualities.

Automatic decomposition and efficient semi-structured meshing of complex solids

In this paper, a novel approach to automatically sub-divide a complex geometry and apply an efficient mesh is presented. Following the identification and removal of thin-sheet regions from an arbitrary solid using the thick/thin decomposition approach developed by Robinson et al. [1], the technique here employs shape metrics generated using local sizing measures to identify long-slender regions within the thick body. A series of algorithms automatically partition the thick region into a non-manifold assembly of long-slender and complex sub-regions. A structured anisotropic mesh is applied to the thin-sheet and long-slender bodies, and the remaining complex bodies are filled with unstructured isotropic tetrahedra. The resulting semi-structured mesh possesses significantly fewer degrees of freedom than the equivalent unstructured mesh, demonstrating the effectiveness of the approach. The accuracy of the efficient meshes generated for a complex geometry is verified via a study that compares the results of a modal analysis with the results of an equivalent analysis on a dense tetrahedral mesh.

Can we trust mass spectrometry for determination of arsenic peptides in plants:comparison of LC-ICP-MS and LC-ES-MS/ICP-MS with XANES/EXAFS in analysis of Thunbergia alata

The weakest step in the analytical procedure for speciation analysis is extraction from a biological material into an aqueous solution which undergoes HPLC separation and then simultaneous online detection by elemental and molecular mass spectrometry (ICP-MS/ES-MS). This paper describes a study to determine the speciation of arsenic and, in particular, the arsenite phytochelatin complexes in the root from an ornamental garden plant Thunbergia alata exposed to 1 mg As L(-1) as arsenate. The approach of formic acid extraction followed by HPLC-ES-MS/ICP-MS identified different As(III)-PC complexes in the extract of this plant and made their quantification via sulfur (m/z 32) and arsenic (m/z 75) possible. Although sulfur sensitivity could be significantly increased when xenon was used as collision gas in ICP-qMS, or when HR-ICP-MS was used in medium resolution, the As:S ratio gave misleading results in the identification of As(III)-PC complexes due to the relatively low resolution of the chromatography system in relation to the variety of As-peptides in plants. Hence only the parallel use of ES-MS/ICP-MS was able to prove the occurrence of such arsenite phytochelatin complexes. Between 55 and 64% of the arsenic was bound to the sulfur of peptides mainly as As(III)(PC(2))(2), As(III)(PC(3)) and As(III)(PC(4)). XANES (X-ray absorption near-edge spectroscopy) measurement, using the freshly exposed plant root directly, confirmed that most of the arsenic is trivalent and binds to S of peptides (53% As-S) while 38% occurred as arsenite and only 9% unchanged as arsenate. EXAFS data confirmed that As-S and As-O bonds occur in the plants. This study confirms, for the first time, that As-peptides can be extracted by formic acid and chromatographically separated on a reversed-phase column without significant decomposition or de-novo synthesis during the extraction step.

Epitaxial Magnetic Oxide Nanocrystals Via Phase Decomposition of Bismuth Perovskite Precursors

The phase instability of bismuth perovskite (BiMO₃), where M is a ferromagnetic cation, is exploited to create self-assembled magnetic oxide nanocrystal arrays on oxide supports. Conditions during pulsed laser deposition are tuned so as to induce complete breakdown of the perovskite precursor into bismuth oxide (Bi₂ O₃ ) and metal oxide (M-O_x ) pockets. Subsequent cooling in vacuum volatizes the Bi₂ O₃ leaving behind an array of monodisperse nanocrystals. In situ reflective high energy electron diffraction beam is exploited to monitor the synthesis in real-time. Analysis of the patterns confi rms the phase separation and volatization process. Successful synthesis of M-O_x, where M = Mn, Fe, Co, and Cr, is shown using this template-free facile approach. Detailed magnetic characterization of nanocrystals is carried out to reveal the functionalities such as magnetic anisotropy as well as larger than bulk moments, as expected in these oxide nanostructures.

Stability Analysis and Implementation of a Decentralized Formation Control Strategy for Unmanned Vehicles

This paper presents a new methodology for solving the multi-vehicle formation control problem. It employs a unique extension-decomposition-aggregation scheme to transform the overall complex formation control problem into a group of subproblems, which work via boundary interactions or disturbances. Thus, it is proved that the overall formation system is exponentially stable in the sense of Lyapunov, if all the individual augmented subsystems (IASs) are stable. Linear matrix inequality-based H8 control methodology is employed to design the decentralized formation controllers to reject the impact of the formation changes being treated as boundary disturbances and guarantee the stability of all the IASs, consequently maintaining the stability of the overall formation system. Simulation studies are performed to verify the stability, performance, and effectiveness of the proposed strategy.

Decentralised formation control and stability analysis for cooperative multi-vehicle manoeuvre

Multi-vehicle cooperative formation control problem is an important and typical topic of research on multi-agent system. This paper presents a formation stability conjecture to conceive a new methodology for solving the decentralised multi-vehicle formation control problem. It employs the “extension-decomposition-aggregation” scheme to transform the complex multi-agent control problem into a group of sub-problems which is able to be solved conveniently. Based on this methodology, it is proved that if all the individual augmented subsystems can be stabilised by using any approach, the overall formation system is not only asymptotically but also exponentially stable in the sense of Lyapunov within a neighbourhood of the desired formation. Simulation study on 6-DOF aerial vehicles (Aerosonde UAVs) has been performed to verify the achieved formation stability result. The proposed multi-vehicle formation control strategy can be conveniently extended to other cooperative control problems of multi-agent systems.

Multi-Dimensional Deprivation in Ireland Among 9-Year-Olds in Ireland:An Analysis of the Growing Up in Ireland Survey’

In this paper we make use of the 9-year old wave of the Growing Up in Ireland study to analyse multidimensional deprivation in Ireland. The Alkire and Foster adjusted head count ratio approach (AHCR; 2007, 2011a, 2011b) applied here constitutes a significant improvement on union and intersection approaches and allows for the decomposition of multidimensional poverty in terms of dimensions and sub-groups. The approach involves a censoring of data such that deprivations count only for those above the specified multidimensional threshold leading to a stronger set of interrelationships between deprivation dimensions. Our analysis shows that the composition of the adjusted head ratio is influenced by a range of socio-economic factors. For less-favoured socio-economic groups dimensions relating to material deprivation are disproportionately represented while for the more advantaged groups, those relating to behavioral and emotional issues and social interaction play a greater role. Notwithstanding such variation in composition, our analysis showed that the AHCR varied systematically across categories of household type, and the social class, education and age group of the primary care giver. Furthermore, these variables combined in a cumulative manner. The most systematic variation was in relation to the head count of those above the multidimensional threshold rather than intensity, conditional on being above that cut-off point. Without seeking to arbitrate on the relative value of composite indices versus disaggregated profiles, our analysis demonstrates that there is much to be gained from adopting an approach with clearly understood axiomatic properties. Doing so allows one to evaluate the consequences of the measurement strategy employed for the understanding of levels of multidimensional deprivation, the nature of such deprivation profiles and socio-economic risk patterns. Ultimately it permits an informed assessment of the strengths and weaknesses of the particular choices made.