Robust L1-norm Singular-Value Decomposition and Estimation

Singular-Value Decomposition (SVD) is a ubiquitous data analysis method in engineering, science, and statistics. Singular-value estimation, in particular, is of critical importance in an array of engineering applications, such as channel estimation in communication systems, EMG signal analysis, and image compression, to name just a few. Conventional SVD of a data matrix coincides with standard Principal-Component Analysis (PCA). The L2-norm (sum of squared values) formulation of PCA promotes peripheral data points and, thus, makes PCA sensitive against outliers. Naturally, SVD inherits this outlier sensitivity. In this work, we present a novel robust method for SVD based on a L1-norm (sum of absolute values) formulation, namely L1-norm compact Singular-Value Decomposition (L1-cSVD). We then propose a closed-form algorithm to solve this problem and find the robust singular values with cost $\mathcal{O}(N^3K^2)$. Accordingly, the proposed method demonstrates sturdy resistance against outliers, especially for singular values estimation, and can facilitate more reliable data analysis and processing in a wide range of engineering applications

Controlling colloidal phase transitions with critical Casimir forces

The critical Casimir effect provides a thermodynamic analogue of the well-known quantum mechanical Casimir effect. It acts between two surfaces immersed in a critical binary liquid mixture, and results from the confinement of concentration fluctuations of the solvent. Unlike the quantum mechanical effect, the magnitude and range of this attraction can be adjusted with temperature via the solvent correlation length, thus offering new opportunities for the assembly of nano and micron-scale structures. Here, we demonstrate the active assembly control of equilibrium phases using critical Casimir forces. We guide colloidal particles into analogues of molecular liquid and solid phases via exquisite control over their interactions. By measuring the critical Casimir particle pair potential directly from density fluctuations in the colloidal gas, we obtain insight into liquefaction at small scales: We apply the Van der Waals model of molecular liquefaction and show that the colloidal gas-liquid condensation is accurately described by the Van der Waals theory, even on the scale of a few particles. These results open up new possibilities in the active assembly control of micro and nanostructures

b anti-b Higgs production at the LHC: Yukawa corrections and the leading Landau singularity

At tree-level Higgs production in association with a b-quark pair proceeds through the small Yukawa bottom coupling in the Standard Model. Even in the limit where this coupling vanishes, electroweak one-loop effects, through the top-Higgs Yukawa coupling in particular, can still trigger this reaction. This contribution is small for Higgs masses around 120GeV but it quickly picks up for higher Higgs masses especially because the one-loop amplitude develops a leading Landau singularity and new thresholds open up. These effects can be viewed as the production of a pair of top quarks which rescatter to give rise to Higgs production through WW fusion. We study the leading Landau singularity in detail. Since this singularity is not integrable when the one-loop amplitude is squared, we regulate the cross section by taking into account the width of the internal top and W particles. This requires that we extend the usual box one-loop function to the case of imaginary masses. We show how this can be implemented analytically in our case. We study in some detail the cross section at the LHC as a function of the Higgs mass and show how some distributions can be drastically affected compared to the tree-level result.Comment: 48 pages, 20 figures. Phys.Rev.D accepted version. Conclusions unchanged, minor changes and references adde

Neural network-based meta-modelling approach for estimating spatial distribution of air pollutant levels

Continuous measurements of the air pollutant concentrations at monitoring stations serve as a reliable basis for air quality regulations. Their availability is however limited only at locations of interest. In most situations, the spatial distribution beyond these locations still remains uncertain as it is highly influenced by other factors such as emission sources, meteorological effects, dispersion and topographical conditions. To overcome this issue, a larger number of monitoring stations could be installed, but it would involve a high investment cost. An alternative solution is via the use of a deterministic air quality model (DAQM), which is mostly adopted by regulatory authorities for prediction in the temporal and spatial domain as well as for policy scenario development. Nevertheless, the results obtained from a model are subject to some uncertainties and it requires, in general, a significant computation time. In this work, a meta-modelling approach based on neural network evaluation is proposed to improve the estimated spatial distribution of the pollutant concentrations. From a dispersion model, it is suggested that the spatially-distributed pollutant levels (i.e. ozone, in this study) across a region under consideration is a function of the grid coordinates, topographical information, solar radiation and the pollutant's precursor emission. Initially, for training the model, the input-output relationship is extracted from a photochemical dispersion model called The Air Pollution Model and Chemical Transport Model (TAPM-CTM), and some of those input-output data are correlated with the ambient measurements collected at monitoring stations. Here, improved radial basis function networks, incorporating a proposed technique for selection of the network centres, will be developed and trained by using the data obtained and the forward selection approach. The methodology is then applied to estimate the ozone concentrations in the Sydney basin, Australia. Once executed, apart from the advantage of inexpensive computation, it provides more reliable results of the estimation and offers better predictions of ozone concentrations than those obtained by using the TAPM-CTM model only, when compared to the measurement data collected at monitoring stations. © 2013 Elsevier B.V. All rights reserved

Toward sustainable energy usage in the power generation and construction sectors - a case study of Australia

© 2015 Elsevier B.V. To be sustainable in energy usage in the future, there are two aspects that need to be considered: the energy supply or generation and the consumption side, including the closely linked construction and building industries which consume a large amount of energy. Essential requirements for energy efficiency are to produce less greenhouse gas emissions and to rely more on renewable energy sources for future sustainability. Policies for mitigation of the environment impact are having effects on both the supply and demand. While the former requires more alternate sources in smart grids and improved technologies for carbon capture and storage, the latter involves the reduction of energy wastes and greenhouse gas (GHG) emissions as prerequisites to green certification within the construction and building sector. Thus, access to sustainable, affordable, and secure energy is one of the major global strategic priorities to maintain and improve public health, sustain economic growth, and mitigate the effects of climate change. Toward this goal, many countries, including Australia, are investing in clean, efficient, reliable energy systems for a prosperous and environmentally sustainable future. Hence, exploring various options to ensure energy security by diversification of energy sources is an important step in meeting the future requirements and delivering clean energy to different industry sectors. This paper discusses options to manage the use of energy sources in the power generation and construction industries. Options for mitigation of environmental impact and for achievement of sustainable energy usage, such as building design with BIM, are discussed

Magnetic properties of Gd_xY_{1-x}Fe_2Zn_{20}: dilute, large, S\textbf {S} moments in a nearly ferromagnetic Fermi liquid

Single crystals of the dilute, rare earth bearing, pseudo-ternary series, Gd_xY_{1-x}Fe_2Zn_{20} were grown out of Zn-rich solution. Measurements of magnetization, resistivity and heat capacity on Gd_xY_{1-x}Fe_2Zn_{20} samples reveal ferromagnetic order of Gd^{3+} local moments across virtually the whole series ($x \geq 0.02$). The magnetic properties of this series, including the ferromagnetic ordering, the reduced saturated moments at base temperature, the deviation of the susceptibilities from Curie-Weiss law and the anomalies in the resistivity, are understood within the frame work of dilute, $\textbf {S}$ moments (Gd^{3+}) embedded in a nearly ferromagnetic Fermi liquid (YFe_2Zn_{20}). The s-d model is employed to further explain the variation of $T_{\mathrm{C}}$ with x as well as the temperature dependences of of the susceptibilities