The Discovery of Soft X-ray Loud Broad Absorption Line Quasars

It is been known for more than a decade that BALQSOs (broad absorption line quasars) are highly attenuated in the X-ray regime compared to other quasars, especially in the soft band ($<$ 1 keV). Using X-ray selection techniques we have found "soft X-ray loud" BALQSOs that, by definition, have soft X-ray (0.3 keV) to UV ($3000 \AA$) flux density ratios that are higher than typical nonBAL radio quiet quasars. Our sample of 3 sources includes one LoBALQSO (low ionization BALQSO) which are generally considered to be the most highly attenuated in the X-rays. The three QSOs are the only known BALQSOs that have X-ray observations that are consistent with no intrinsic soft X-ray absorption. The existence of a large X-ray luminosity and the hard ionizing continuum that it presents to potential UV absorption gas is in conflict with the ionization states that are conducive to line driving forces within BAL winds (especially for the LoBALs).Comment: To appear in ApJ Letter

Horizontal-branch morphology and multiple stellar populations in the anomalous globular cluster M22

M22 is an anomalous globular cluster that hosts two groups of stars with different metallicity and s-element abundance. The star-to-star light-element variations in both groups, with the presence of individual Na-O and C-N anticorrelations, demonstrates that this Milky-Way satellite has experienced a complex star-formation history. We have analysed FLAMES/UVES spectra for seven stars covering a small color interval, on the reddest horizontal-branch (HB) portion of this cluster and investigated possible relations between the chemical composition of a star and its location along the HB. Our chemical abundance analysis takes into account effects introduced by deviations from the local-thermodynamic equilibrium (NLTE effects), that are significant for the measured spectral lines in the atmospheric parameters range spanned by our stars. We find that all the analysed stars are barium-poor and sodium-poor, thus supporting the idea that the position of a star along the HB is strictly related to the chemical composition, and that the HB-morphology is influenced by the presence of different stellar populations.Comment: 21 pages, 3 figures, accepted for publication in Ap

Explicit Bosonization of the Massive Thirring Model in 3+1 Dimensions

We bosonize the Massive Thirring Model in 3+1D for small coupling constant and arbitrary mass. The bosonized action is explicitly obtained both in terms of a Kalb-Ramond tensor field as well as in terms of a dual vector field. An exact bosonization formula for the current is derived. The small and large mass limits of the bosonized theory are examined in both the direct and dual forms. We finally obtain the exact bosonization of the free fermion with an arbitrary mass.Comment: Latex, 7 page

Quantized Skyrmion Fields in 2+1 Dimensions

A fully quantized field theory is developped for the skyrmion topological excitations of the O(3) symmetric CP$^1$-Nonlinear Sigma Model in 2+1D. The method allows for the obtainment of arbitrary correlation functions of quantum skyrmion fields. The two-point function is evaluated in three different situations: a) the pure theory; b) the case when it is coupled to fermions which are otherwise non-interacting and c) the case when an electromagnetic interaction among the fermions is introduced. The quantum skyrmion mass is explicitly obtained in each case from the large distance behavior of the two-point function and the skyrmion statistics is inferred from an analysis of the phase of this function. The ratio between the quantum and classical skyrmion masses is obtained, confirming the tendency, observed in semiclassical calculations, that quantum effects will decrease the skyrmion mass. A brief discussion of asymptotic skyrmion states, based on the short distance behavior of the two-point function, is also presented.Comment: Accepted for Physical Review

Losing Ground: An Ethnography Of Vulnerability And Climate Change In Shishmaref, Alaska

Thesis (Ph.D.) University of Alaska Fairbanks, 2012This dissertation presents an ethnography of vulnerability in Shishmaref, Alaska. The village of Shishmaref, population 563, faces imminent threat from increasing erosion and flooding events -- linked to climatic changes and ecological shift -- making the relocation of residents off of the island necessary in the foreseeable future. In spite of ongoing conversations with government agencies since 1974, an organized relocation has yet to occur in Shishmaref. While ecological shift and anthropogenic climate change are no doubt occurring in and around the island, the literature on vulnerability and disaster predicts that social systems contribute at least as much as ecological circumstances to disaster scenarios. This research tests this theory and asks the question: what exactly is causing vulnerability in Shishmaref, Alaska? The resulting dissertation is an exploration of the ecological, historical, social and cultural influences that contribute to vulnerability and risk in Shishmaref. Unlike common representations of climate change and disaster that present the natural environment as a sole driver of risk, this research finds complex systems of decision-making, ideologies of development, and cultural assumptions about social life contribute to why Shishmaref residents are exposed to erosion and flooding and why government intervention and planning remains difficult

Understanding and strain-engineering wrinkle networks in supported graphene through simulations

Wrinkle networks are ubiquitous buckle-induced delaminations in supported graphene, which locally modify the electronic structure and degrade device performance. Although the strong property-deformation coupling of graphene can be potentially harnessed by strain engineering, it has not been possible to precisely control the geometry of wrinkle networks. Through numerical simulations based on an atomistically informed continuum theory, we understand how strain anisotropy, adhesion and friction govern spontaneous wrinkling. We then propose a strategy to control the location of wrinkles through patterns of weaker adhesion. This strategy is deceptively simple, and can in fact fail in several ways, particularly under biaxial compression. However, within bounds set by the physics of wrinkling, it is possible to robustly create by strain a variety of wrinkle network geometries and junction configurations. Graphene is nearly unstrained in the planar regions bounded by wrinkles, highly curved along wrinkles, and highly stretched and curved at junctions, which can either locally attenuate or amplify the applied strain depending on their configuration. These mechanically self-assembled networks are stable under the pressure produced by an enclosed fluid and form continuous channels, opening the door to nano-fluidic applications

Adhesion and friction control localized folding in supported graphene

Graphene deposited on planar surfaces often exhibits sharp and localized folds delimiting seemingly planar regions, as a result of compressive stresses transmitted by the substrate. Such folds alter the electronic and chemical properties of graphene, and therefore, it is important to understand their emergence, to either suppress them or control their morphology. Here, we study the emergence of out-of-plane deformations in supported and laterally strained graphene with high-fidelity simulations and a simpler theoretical model. We characterize the onset of buckling and the nonlinear behavior after the instability in terms of the adhesion and frictional material parameters of the graphene-substrate interface. We find that localized folds evolve from a distributed wrinkling linear instability due to the nonlinearity in the van der Waals graphene-substrate interactions. We identify friction as a selection mechanism for the separation between folds, as the formation of far apart folds is penalized by the work of friction. Our systematic analysis is a first step towards strain engineering of supported graphene, and is applicable to other compressed thin elastic films weakly coupled to a substrate

Coexistence of wrinkles and blisters in supported graphene

Blisters induced by gas trapped in the interstitial space between supported graphene and the substrate are commonly observed. These blisters are often quasi-spherical with a circular rim, but polygonal blisters are also common and coexist with wrinkles emanating from their vertices. Here, we show that these different blister morphologies can be understood mechanically in terms of free energy minimization of the supported graphene sheet for a given mass of trapped gas and for a given lateral strain. Using a nonlinear continuum model for supported graphene closely reproducing experimental images of blisters, we build a morphological diagram as a function of strain and trapped mass. We show that the transition from quasi-spherical to polygonal of blisters as compressive strain is increased is a process of stretching energy relaxation and focusing, as many other crumpling events in thin sheets. Furthermore, to characterize this transition, we theoretically examine the onset of nucleation of short wrinkles in the periphery of a quasi-spherical blister. Our results are experimentally testable and provide a framework to control complex out-of-plane motifs in supported graphene combining blisters and wrinkles for strain engineering of graphene

Context-Aware Embeddings for Automatic Art Analysis

Automatic art analysis aims to classify and retrieve artistic representations from a collection of images by using computer vision and machine learning techniques. In this work, we propose to enhance visual representations from neural networks with contextual artistic information. Whereas visual representations are able to capture information about the content and the style of an artwork, our proposed context-aware embeddings additionally encode relationships between different artistic attributes, such as author, school, or historical period. We design two different approaches for using context in automatic art analysis. In the first one, contextual data is obtained through a multi-task learning model, in which several attributes are trained together to find visual relationships between elements. In the second approach, context is obtained through an art-specific knowledge graph, which encodes relationships between artistic attributes. An exhaustive evaluation of both of our models in several art analysis problems, such as author identification, type classification, or cross-modal retrieval, show that performance is improved by up to 7.3% in art classification and 37.24% in retrieval when context-aware embeddings are used