Generalised Geometry and type II Supergravity

Ten-dimensional type II supergravity can be reformulated as a generalised geometrical analogue of Einstein gravity, defined by an $O(9,1)\times O(1,9)\subset O(10,10)\times\mathbb{R}^+$ structure on the generalised tangent space. To leading order in the fermion fields, this allow one to rewrite the action, equations of motion and supersymmetry variations in a simple, manifestly $Spin(9,1)\times Spin(1,9)$-covariant form.Comment: 5 pages, contribution to the proceedings of the XVII European Workshop on String Theory 2011, Padua, Italy, to appear in Fortschritte der Physi

Mobile Anesthesia Applications as Point-of-Care Tools for CRNAs in Clinical Practice

Background: The practice of anesthesia requires knowledge of procedures, patient conditions, comorbidities, and medications, as well as the ability to continually assess and respond to the patient’s status. The use of mobile anesthesia applications (apps) has become increasingly common among Certified Registered Nurse Anesthetists (CRNAs) to provide immediate access to current information regarding anesthesia administration and to support optimal patient care.The purpose of this study was to assess the use of mobile anesthesia apps used by CRNAs as point-of-care (POC) tools in their anesthesia practice.Methodology: We report data collected from a survey designed to sample CRNAs who have been in practice for three years or less (“recent” graduates) and who utilize mobile anesthesia apps. The survey was offered to members of a Facebook group called CRNAs and SRNAs.Results: A total of 160 CRNAs in practice three years or less completed the survey and reported the ways they currently use a mobile anesthesia app. In this report we quantify the various ways such apps are used and conclude that mobile apps are widely used among student and/or recent graduate CRNAs as point-of-care tools and who believe they improve the safety and efficacy of their anesthesia practice.Conclusions: The benefits to practice that users report should be encouraging to the developers of mobile health care apps and a motivating factor for more practitioners to utilize them

Mechanics Of Colloidal Assemblies

Amorphous solids -- solids that lack long-range order of their constituent particles -- are common in both nature and industry. Window glass, dense polymers, and food grains are three examples of amorphous solids familiar to us. In many amorphous solids, shear banding -- plastic deformation in which strain is accumulated in a thin band of the material -- is common. Consequently, many amorphous solids are brittle, a trait which has limited the technological adoption of otherwise promising materials such as metallic glasses. Therefore, a fundamental understanding of shear banding -- i.e., the progression from particle level plastic events to a macroscopic shear band, identification of the sites in the material from which shear banding is most likely to originate, the effect of structural modifications on shear banding, and mechanisms that arrest shear band operation before failure -- is crucial for predicting failure and engineering ductility in amorphous materials. This dissertation describes efforts to illuminate elements of plasticity in amorphous solids using model systems of colloidal particles. The bulk of the results focuses on colloidal pillars subjected to uniaxial compression. Results from instrumented compression experiments reveal that the pillars exhibit a scaling of strength with stiffness that is similar to the scaling found in metallic glasses, which we interpret in the context of the energetics and kinematics of a critical shear band nucleus. In 4D \emph{in-situ} compression experiments we are able to observe the microscopic evolution of a shear band and the associated mechanical response in and around the shear band. The results from this experiment lend credence to the interpretation of shear banding as localized, anisotropic glass transition. In addition to the pillar geometry, we perform confined compression experiments on a confined colloidal glass to investigate the structural fingerprints of the particles that are most likely to rearrange in an amorphous solid. The results from these experiments are interpreted in the context of a recently introduced machine-learning based approach to the identification of particles most susceptible to rearrangement termed softness . We report preliminary application of softness to the shear banding pillars

Social Identity and Preferences

Social identities prescribe behaviors for people. We identify the marginal behavioral effect of these norms on discount rates and risk aversion by measuring how laboratory subjects’ choices change when an aspect of social identity is made salient. When we make ethnic identity salient to Asian-American subjects, they make more patient choices. When we make racial identity salient to black subjects, non-immigrant blacks (but not immigrant blacks) make more patient choices. Making gender identity salient has no effect on intertemporal or risk choices.

Exceptional generalised geometry for massive IIA and consistent reductions

We develop an exceptional generalised geometry formalism for massive type IIA supergravity. In particular, we construct a deformation of the generalised Lie derivative, which generates the type IIA gauge transformations as modified by the Romans mass. We apply this new framework to consistent Kaluza-Klein reductions preserving maximal supersymmetry. We find a generalised parallelisation of the exceptional tangent bundle on S^6, and from this reproduce the consistent truncation ansatz and embedding tensor leading to dyonically gauged ISO(7) supergravity in four dimensions. We also discuss closely related hyperboloid reductions, yielding a dyonic ISO(p,7-p) gauging. Finally, while for vanishing Romans mass we find a generalised parallelisation on S^d, d=4,3,2, leading to a maximally supersymmetric reduction with gauge group SO(d+1) (or larger), we provide evidence that an analogous reduction does not exist in the massive theory.Comment: 69 pages; v2: version published in JHE

Scalable iterative methods for sampling from massive Gaussian random vectors

Sampling from Gaussian Markov random fields (GMRFs), that is multivariate Gaussian ran- dom vectors that are parameterised by the inverse of their covariance matrix, is a fundamental problem in computational statistics. In this paper, we show how we can exploit arbitrarily accu- rate approximations to a GMRF to speed up Krylov subspace sampling methods. We also show that these methods can be used when computing the normalising constant of a large multivariate Gaussian distribution, which is needed for both any likelihood-based inference method. The method we derive is also applicable to other structured Gaussian random vectors and, in particu- lar, we show that when the precision matrix is a perturbation of a (block) circulant matrix, it is still possible to derive O(n log n) sampling schemes.Comment: 17 Pages, 4 Figure