Chevkinite-Group Minerals from Granulite-Facies Metamorphic Rocks and Associated Pegmatites of East Antarctica and South India

Electron microprobe data are presented for chevkinite-group minerals from granulite-facies rocks and associated pegmatities of the Napier Complex and Mawson Station charnockite in East Antarctica and from the Eastern Ghats, South India. Their compositions conform to the general formula for this group, viz. A(4)BC(2)D(2)Si(4)O(22) where, in the analysed specimens A = (rare-earth elements (REE), Ca, Y, Th), B = Fe(2+) Mg, C = (Al, Mg, Ti, Fe(2+), Fe(3+), Zr) and D = Ti and plot within the perrierite field oftlic total Fe (as FeO) (wt.%) vs. CaO (wt.%) discriminator diagram of Macdonald and Belkin (2002). In contrast to most chevkinite-group minerals, the A site shows unusual enrichment in the MREE and HREE relative to the LREE and Ca. In one sample from the Napier Complex, Y is the dominant cation among the total REE + Y in the A site, the first reported case of Y-dominance in the chevkinite group. The minerals include the most Al-rich yet reported in the chevkinite group (\u3c= 9.15 wt.% Al(2)O(3)), sufficient to fill the C site in two samples. Conversely, the amount of Ti in these samples does not fill the D site. and, thus, some of the Al could be making up the deficiency at D, a situation not previously reported in the chevkinite group. Fe abudances are low, requiring Mg to occupy up to 45% of the B site. The chevkinite-group minerals analysed originated from three distinct parageneses: (1) pegmatites containing hornblende and orthopyroxene or garnet; (2) orthopyroxene-bearing gneiss and granulite; (3) highly aluminous paragneisses in which the associated minerals are relatively magnesian or aluminous. Chevkinite-group minerals from the first two parageneses have relatively high FeO content and low MgO and Al(2)O(3) contents; their compositions plot in the field for mafic and intermediate igneous rocks. In contrast, chevkinite-group minerals from the third paragenesis are notably more aluminous and have greater Mg/Fe ratios

Compositional variation and zoning of epidote supergroup minerals in the Campi Flegrei geothermal field, Naples, Italy

Authigenic epidote supergroups are an abundant accessory mineral in the calcium–aluminum silicate and thermometamorphic hydrothermal zones of the Campi Flegrei (Phlegraean Fields) geothermal field located west of Naples, Italy. Geothermal exploration for high-enthalpy fluid produced drill core and cuttings to ∼ 3 km depth in the Mofete (MF1, MF2, MF5) and San Vito (SV1, SV3) wells, where measured down-hole temperatures of epidote-bearing samples range from 270–350 ∘C and from 285–390 ∘C for the Mofete and San Vito areas, respectively. Two epidote group (epidote, clinozoisite), some rare earth element (REE)-bearing, and two allanite group (allanite-(Ce), ferriallanite-(Ce)) minerals were identified by electron microprobe. The allanite group is light rare earth element (LREE, La–Gd) enriched, Ce dominant, with REE + Y that varies from 30.59 wt %–14.32 wt %. Complex compositional variation such as oscillatory, sector, and complex (mixed) zoning is a ubiquitous feature observed in the epidote group, which occurs as veins, in vugs, as various size masses, and as isolated single crystals. Compositional zoning is caused by variable Fe ↔ Al3+ substitution and XFe [(Fe3+) / (Fe3++ Al)] ranges from 0.06–0.33 (Fe3+=0.185–0.967 apfu). XFe tends to decrease with increasing temperature in the Mofete wells, but its distribution is more complex in the San Vito wells, which records recent fault displacement. The variety and complexity of the epidote supergroup zoning suggest rapid fluid composition and/or intensive parameter fluctuations in the local hydrothermal system.</p

Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \cite{LARS}), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: 1) the local geometry of samples is well preserved for low dimensional data representation, 2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, 3) the projection matrix of MEN improves the parsimony in computation, 4) the elastic net penalty reduces the over-fitting problem, and 5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.Comment: 33 pages, 12 figure

Uncertainty quantification in graph-based classification of high dimensional data

Classification of high dimensional data finds wide-ranging applications. In many of these applications equipping the resulting classification with a measure of uncertainty may be as important as the classification itself. In this paper we introduce, develop algorithms for, and investigate the properties of, a variety of Bayesian models for the task of binary classification; via the posterior distribution on the classification labels, these methods automatically give measures of uncertainty. The methods are all based around the graph formulation of semi-supervised learning. We provide a unified framework which brings together a variety of methods which have been introduced in different communities within the mathematical sciences. We study probit classification in the graph-based setting, generalize the level-set method for Bayesian inverse problems to the classification setting, and generalize the Ginzburg-Landau optimization-based classifier to a Bayesian setting; we also show that the probit and level set approaches are natural relaxations of the harmonic function approach introduced in [Zhu et al 2003]. We introduce efficient numerical methods, suited to large data-sets, for both MCMC-based sampling as well as gradient-based MAP estimation. Through numerical experiments we study classification accuracy and uncertainty quantification for our models; these experiments showcase a suite of datasets commonly used to evaluate graph-based semi-supervised learning algorithms.Comment: 33 pages, 14 figure

Functional central limit theorems for vicious walkers

We consider the diffusion scaling limit of the vicious walker model that is a system of nonintersecting random walks. We prove a functional central limit theorem for the model and derive two types of nonintersecting Brownian motions, in which the nonintersecting condition is imposed in a finite time interval $(0,T]$ for the first type and in an infinite time interval $(0,\infty)$ for the second type, respectively. The limit process of the first type is a temporally inhomogeneous diffusion, and that of the second type is a temporally homogeneous diffusion that is identified with a Dyson's model of Brownian motions studied in the random matrix theory. We show that these two types of processes are related to each other by a multi-dimensional generalization of Imhof's relation, whose original form relates the Brownian meander and the three-dimensional Bessel process. We also study the vicious walkers with wall restriction and prove a functional central limit theorem in the diffusion scaling limit.Comment: AMS-LaTeX, 20 pages, 2 figures, v6: minor corrections made for publicatio

Laminin isoform expression in breast tumors

Certain laminins of vascular basement membranes have been identified in human breast tumors and brain gliomas that share the same β1 chain. These laminins are new carcinoma angiogenic markers and might represent potential targets for antiangiogenic therapy

A graph-based integration of multimodal brain imaging data for the detection of early mild cognitive impairment (E-MCI)

Alzheimer's disease (AD) is the most common cause of dementia in older adults. By the time an individual has been diagnosed with AD, it may be too late for potential disease modifying therapy to strongly influence outcome. Therefore, it is critical to develop better diagnostic tools that can recognize AD at early symptomatic and especially pre-symptomatic stages. Mild cognitive impairment (MCI), introduced to describe a prodromal stage of AD, is presently classified into early and late stages (E-MCI, L-MCI) based on severity. Using a graph-based semi-supervised learning (SSL) method to integrate multimodal brain imaging data and select valid imaging-based predictors for optimizing prediction accuracy, we developed a model to differentiate E-MCI from healthy controls (HC) for early detection of AD. Multimodal brain imaging scans (MRI and PET) of 174 E-MCI and 98 HC participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were used in this analysis. Mean targeted region-of-interest (ROI) values extracted from structural MRI (voxel-based morphometry (VBM) and FreeSurfer V5) and PET (FDG and Florbetapir) scans were used as features. Our results show that the graph-based SSL classifiers outperformed support vector machines for this task and the best performance was obtained with 66.8% cross-validated AUC (area under the ROC curve) when FDG and FreeSurfer datasets were integrated. Valid imaging-based phenotypes selected from our approach included ROI values extracted from temporal lobe, hippocampus, and amygdala. Employing a graph-based SSL approach with multimodal brain imaging data appears to have substantial potential for detecting E-MCI for early detection of prodromal AD warranting further investigation

Forces Sauces and Eggs for Soldiers: food, nostalgia, and the rehabilitation of the British military

This article identifies, and considers the political implications of, the association of the contemporary British military and British soldiers with nostalgia. This aspect of the discursive project of rehabilitating the British military post-Iraq has not hitherto been theorized. The article analyses a set of exemplifying texts, four military charity food brands (Eggs for Soldiers, Forces Sauces, Red Lion Foods, and Rare Tea Company Battle of Britain Tea) to ask how nostalgic rehabilitation of the British military unfolds at the intersections of militarization, commemoration, and post-2008 “conscience capitalism”. I outline how military charity food brands are a form of “conscience capitalism” through which the perpetuation of militarized logics are produced as a notionally apolitical social “cause”, rendered intelligible within the terms of existing commoditized discourses of post-2008 vintage nostalgia. I then ask what understandings of British soldiers and the British military are constituted within the discourse of nostalgic rehabilitation, and secondly what forms of commemoration are entailed. I argue that a nostalgic generalization of soldiers and the military nullifies the potential unruliness of individual soldiers and obscures the specifics of recent, controversial, wars. Secondly nostalgic civil–military engagement entails a commemorative logic in which forms of quasi-military service are brought into the most banal spaces of everyday civilian life

Engineered far-fields of metal-metal terahertz quantum cascade lasers with integrated planar horn structures

The far-field emission profile of terahertz quantum cascade lasers (QCLs) in metal-metal waveguides is controlled in directionality and form through planar horn-type shape structures, whilst conserving a broad spectral response. The structures produce a gradual change in the high modal confinement of the waveguides and permit an improved far-field emission profile and resulting in a four-fold increase in the emitted output power. The two-dimensional far-field patterns are measured at 77 K and are agreement in with 3D modal simulations. The influence of parasitic high-order transverse modes is shown to be controlled by engineering the horn structure (ridge and horn widths), allowing only the fundamental mode to be coupled out

Rhythmic dynamics and synchronization via dimensionality reduction : application to human gait

Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high-dimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankle-knee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system