Globally sparse PLS regression

Volume 56 ; Print ISBN : 978-1-4614-8282-6Partial least squares (PLS) regression combines dimensionality reduction and prediction using a latent variable model. It provides better predictive ability than principle component analysis by taking into account both the independent and re- sponse variables in the dimension reduction procedure. However, PLS suffers from over-fitting problems for few samples but many variables. We formulate a new criterion for sparse PLS by adding a structured sparsity constraint to the global SIMPLS optimization. The constraint is a sparsity-inducing norm, which is useful for selecting the important variables shared among all the components. The optimization is solved by an augmented Lagrangian method to obtain the PLS components and to perform variable selection simultaneously. We propose a novel greedy algorithm to overcome the computation difficulties. Experiments demonstrate that our approach to PLS regression attains better performance with fewer selected predictor

Using dissolved H₂O in rhyolitic glasses to estimate palaeo-ice thickness during a subglacial eruption at Bláhnúkur(Torfajökull, Iceland)

The last decade has seen the refinement of a technique for reconstructing palaeo-ice thicknesses based on using the retained H2O and CO2 content in glassy eruptive deposits to infer quenching pressures and therefore ice thicknesses. The method is here applied to Bláhnúkur, a subglacially erupted rhyolitic edifice in Iceland. A decrease in water content from ~0.7 wt.% at the base to ~0.3 wt.% at the top of the edifice suggests that the ice was 400 m thick at the time of the eruption. As Bláhnúkur rises 350 m above the surrounding terrain, this implies that the eruption occurred entirely within ice, which corroborates evidence obtained from earlier lithofacies studies. This paper presents the largest data set (40 samples) so far obtained for the retained volatile contents of deposits from a subglacial eruption. An important consequence is that it enables subtle but significant variations in water content to become evident. In particular, there are anomalous samples which are either water-rich (up to 1 wt.%) or water-poor (~0.2 wt.%), with the former being interpreted as forming intrusively within hyaloclastite and the latter representing batches of magma that were volatile-poor prior to eruption. The large data set also provides further insights into the strengths and weaknesses of using volatiles to infer palaeo-ice thicknesses and highlights many of the uncertainties involved. By using examples from Bláhnúkur, the quantitative use of this technique is evaluated. However, the relative pressure conditions which have shed light on Bláhnúkur’s eruption mechanisms and syn-eruptive glacier response show that, despite uncertainties in absolute values, the volatile approach can provide useful insight into the mechanisms of subglacial rhyolitic eruptions, which have never been observed

Ice nucleation properties of volcanic ash from Eyjafjallajökull

The ice nucleation ability of volcanic ash particles collected close to the Icelandic volcano Eyjafjallajökull during its eruptions in April and May 2010 is investigated experimentally, in the immersion and deposition modes, and applied to atmospheric conditions by comparison with airborne measurements and microphysical model calculations. The number of ash particles which are active as ice nuclei (IN) is strongly temperature dependent, with a very small minority being active in the immersion mode at temperatures of 250–263 K. Average ash particles show only a moderate effect on ice nucleation, by inducing freezing at temperatures between 236 K and 240 K (i.e. approximately 3–4 K higher than temperatures required for homogeneous ice nucleation, measured with the same instrument). By scaling the results to aircraft and lidar measurements of the conditions in the ash plume days down wind of the eruption, and by applying a simple microphysical model, it was found that the IN active in the immersion mode in the range 250–263 K generally occurred in atmospheric number densities at the lower end of those required to have an impact on ice cloud formation. However, 3–4 K above the homogeneous freezing point, immersion mode IN number densities a few days down wind of the eruption were sufficiently high to have a moderate influence on ice cloud formation. The efficiency of IN in the deposition mode was found to be poor except at very cold conditions (<238 K), when they reach an efficiency similar to that of mineral dust with the onset of freezing at 10 % supersaturation with respect to ice, and with the frozen fraction nearing its maximum value at a supersaturation 20 %. In summary, these investigations suggest volcanic ash particles to have only moderate effects on atmospheric ice formation

Environmental pressure from the 2014–15 eruption of Bárðarbunga volcano, Iceland

The effusive six months long 2014-2015 Bárðarbunga eruption (31 August-27 February) was the largest in Iceland for more than 200 years, producing 1.6 ± 0.3 km3 of lava. The total SO2 emission was 11 ± 5 Mt, more than the amount emitted from Europe in 2011. The ground level concentration of SO2 exceeded the 350 μg m−3 hourly average health limit over much of Iceland for days to weeks. Anomalously high SO2 concentrations were also measured at several locations in Europe in September. The lowest pH of fresh snowmelt at the eruption site was 3.3, and 3.2 in precipitation 105 km away from the source. Elevated dissolved H2SO4, HCl, HF, and metal concentrations were measured in snow and precipitation. Environmental pressures from the eruption and impacts on populated areas were reduced by its remoteness, timing, and the weather. The anticipated primary environmental pressure is on the surface waters, soils, and vegetation of Iceland

Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems

Background: Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits.Results: A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework.Conclusions: sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets

Ice-confined construction of a large basaltic volcano—Austurfjöll massif, Askja, Iceland

Austurfjöll is the largest basaltic glaciovolcanic massif at Askja volcano (Central Iceland), and through detailed studies of its volcanological and geochemical characteristics, we provide a detailed account of the sequence and structure of the ice-confined construction of a large Icelandic basaltic volcano. In particular, Austurfjöll represents a geometry of vents, and resulting glaciovolcanic morphology, not previously documented in ice-confined basaltic volcanoes. Austurfjöll was constructed during two major phases of basaltic volcanism, via seven eruptive episodes through disperse fissure-dominated eruptions. The earliest episode involved a rare and poorly exposed example of subaerial activity, and this was succeeded by six episodes involving the eruption of ice-confined pillow lavas and numerous overlapping fissure eruptions of phreatomagmatic tephra. Evidence of local subaerial lavas and tephras indicates the local growth of eruptive centers above englacial lake levels, and subsequent flooding, but no prolonged subaerial activity. Localized ice-contact facies, paleowater levels, and diamictons indicate the position and thickness of the ice was variable during the construction of Austurfjöll, and eruptive activity likely occurred in multiple and variable level meltwater lakes during the last glacial period. Lithofacies evidence including gradational transitions from effusive to explosive deposits, superposition of fragmental facies above coherent facies, and drainage channels suggest that changes in eruptive style were driven largely by external factors such as drainage and the increasing elevation of the massif. This study emphasizes the unique character of Austurfjöll, being composed of large pillow lava sheets, numerous (> 40) overlapping glaciovolcanic tindars, and only localized emergent deposits, as a product of its prolonged ice-confined eruptive history, contrasts with previous descriptions of tuyas and tindars

Hyperspectral Sensing Techniques Applied to Bio-masses Characterization: The Olive Husk Case

Olive husk (OH) quality, in respect of constituting particles characteristics (olive stones and pulp residues as result after pressing), represents an important issue. OH particles size class distribution and composition play, in fact, an important role for OH utilization as: organic amendment, bio-mass, food ingredient, plastic filler, abrasive, raw material in the cosmetic sector, dietary animal supplementation, etc. . OH is characterised by a strong variability according to olive characteristics and olive oil production process. Actually it does not exist any strategy able to quantify OH chemical-physical attributes versus its correct utilisation adopting simple, efficient and low costs analytical tools. Furthermore the possibility to perform its continuous monitoring, without any samples collection and analysis at laboratory scale, could strongly enhance OH utilization, with a great economic and environmental benefits. In this paper an analytical approach, based on HyperSpectral Imaging (HSI) is presented. HSI allows to perform, also on-line, a full quantification of OH characteristics in order to qualify this product for its further re-use, with particular reference as bio-mass. HSI was applied to different samples of OH, characterized by different moisture, different residual pulp content and different size class distributions. Results are presented and critically evaluated. © 2011 IFIP International Federation for Information Processing

MeMoVolc report on classification and dynamics of volcanic explosive eruptions

Classifications of volcanic eruptions were first introduced in the early twentieth century mostly based on qualitative observations of eruptive activity, and over time, they have gradually been developed to incorporate more quantitative descriptions of the eruptive products from both deposits and observations of active volcanoes. Progress in physical volcanology, and increased capability in monitoring, measuring and modelling of explosive eruptions, has highlighted shortcomings in the way we classify eruptions and triggered a debate around the need for eruption classification and the advantages and disadvantages of existing classification schemes. Here, we (i) review and assess existing classification schemes, focussing on subaerial eruptions; (ii) summarize the fundamental processes that drive and parameters that characterize explosive volcanism; (iii) identify and prioritize the main research that will improve the understanding, characterization and classification of volcanic eruptions and (iv) provide a roadmap for producing a rational and comprehensive classification scheme. In particular, classification schemes need to be objective-driven and simple enough to permit scientific exchange and promote transfer of knowledge beyond the scientific community. Schemes should be comprehensive and encompass a variety of products, eruptive styles and processes, including for example, lava flows, pyroclastic density currents, gas emissions and cinder cone or caldera formation. Open questions, processes and parameters that need to be addressed and better characterized in order to develop more comprehensive classification schemes and to advance our understanding of volcanic eruptions include conduit processes and dynamics, abrupt transitions in eruption regime, unsteadiness, eruption energy and energy balance