On trend estimation under monotone Gaussian subordination with long-memory: application to fossil pollen series

Fossil pollen data from stratigraphic cores are irregularly spaced in time due to non-linear age-depth relations. Moreover, their marginal distributions may vary over time. We address these features in a nonparametric regression model with errors that are monotone transformations of a latent continuous-time Gaussian process Z(T). Although Z(T) is unobserved, due to monotonicity, under suitable regularity conditions, it can be recovered facilitating further computations such as estimation of the long-memory parameter and the Hermite coefficients. The estimation of Z(T) itself involves estimation of the marginal distribution function of the regression errors. These issues are considered in proposing a plug-in algorithm for optimal bandwidth selection and construction of confidence bands for the trend function. Some high-resolution time series of pollen records from Lago di Origlio in Switzerland, which go back ca. 20,000 years are used to illustrate the methods.

Estimating and quantifying uncertainties on level sets using the Vorob'ev expectation and deviation with Gaussian process models

Several methods based on Kriging have recently been proposed for calculating a probability of failure involving costly-to-evaluate functions. A closely related problem is to estimate the set of inputs leading to a response exceeding a given threshold. Now, estimating such a level set—and not solely its volume—and quantifying uncertainties on it are not straightforward. Here we use notions from random set theory to obtain an estimate of the level set, together with a quantification of estimation uncertainty. We give explicit formulae in the Gaussian process set-up and provide a consistency result. We then illustrate how space-filling versus adaptive design strategies may sequentially reduce level set estimation uncertainty.

A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations

In the context of expensive numerical experiments, a promising solution for alleviating the computational costs consists of using partially converged simulations instead of exact solutions. The gain in computational time is at the price of precision in the response. This work addresses the issue of fitting a Gaussian process model to partially converged simulation data for further use in prediction. The main challenge consists of the adequate approximation of the error due to partial convergence, which is correlated in both design variables and time directions. Here, we propose fitting a Gaussian process in the joint space of design parameters and computational time. The model is constructed by building a nonstationary covariance kernel that reflects accurately the actual structure of the error. Practical solutions are proposed for solving parameter estimation issues associated with the proposed model. The method is applied to a computational fluid dynamics test case and shows significant improvement in prediction compared to a classical kriging model.

A Compensating Anastigmatic Submillimeter Array Imaging System for STEAMR

In this paper, we present a novel technique for the removal of astigmatism in submillimeter-wave optical systems through employment of a specific combination of so-called astigmatic off-axis reflectors. This technique treats an orthogonally astigmatic beam using skew Gaussian beam analysis, from which an anastigmatic imaging network is derived. The resultant beam is considered truly stigmatic, with all Gaussian beam parameters in the orthogonal directions being matched. This is thus considered an improvement over previous techniques wherein a beam corrected for astigmatism has only the orthogonal beam amplitude radii matched, with phase shift and phase radius of curvature not considered. This technique is computationally efficient, negating the requirement for computationally intensive numerical analysis of shaped reflector surfaces. The required optical surfaces are also relatively simple to implement compared to such numerically optimized shaped surfaces. This technique is implemented in this work as part of the complete optics train for the STEAMR antenna. The STEAMR instrument is envisaged as a mutli-beam limb sounding instrument operating at submillimeter wavelengths. The antenna optics arrangement for this instrument uses multiple off-axis reflectors to control the incident radiation and couple them to their corresponding receiver feeds. An anastigmatic imaging network is successfully implemented into an optical model of this antenna, and the resultant design ensures optimal imaging of the beams to the corresponding feed horns. This example also addresses the challenges of imaging in multi-beam antenna systems.

Collimated Field Emission Beams from Metal Double-Gate Nanotip Arrays Optically Excited via Surface Plasmon Resonance

The generation of collimated electron beams from metal double-gate nanotip arrays excited by near infrared laser pulses is studied. Using electromagnetic and particle tracking simulations, we showed that electron pulses with small rms transverse velocities are efficiently produced from nanotip arrays by laser-induced field emission with the laser wavelength tuned to surface plasmon polariton resonance of the stacked double-gate structure. The result indicates the possibility of realizing a metal nanotip array cathode that outperforms state-of-the-art photocathodes.

Uniqueness of the Embedding Continuous Convolution Semigroup of a Gaussian Probability Measure on the Affine Group and an Application in Mathematical Finance

Let {μ(i)t}t≥0 ( i=1,2 ) be continuous convolution semigroups (c.c.s.) of probability measures on Aff(1) (the affine group on the real line). Suppose that μ(1)1=μ(2)1 . Assume furthermore that {μ(1)t}t≥0 is a Gaussian c.c.s. (in the sense that its generating distribution is a sum of a primitive distribution and a second-order differential operator). Then μ(1)t=μ(2)t for all t≥0 . We end up with a possible application in mathematical finance.

Forward treatment planning for modulated electron radiotherapy (MERT) employing Monte Carlo methods

PURPOSE This paper describes the development of a forward planning process for modulated electron radiotherapy (MERT). The approach is based on a previously developed electron beam model used to calculate dose distributions of electron beams shaped by a photon multi leaf collimator (pMLC). METHODS As the electron beam model has already been implemented into the Swiss Monte Carlo Plan environment, the Eclipse treatment planning system (Varian Medical Systems, Palo Alto, CA) can be included in the planning process for MERT. In a first step, CT data are imported into Eclipse and a pMLC shaped electron beam is set up. This initial electron beam is then divided into segments, with the electron energy in each segment chosen according to the distal depth of the planning target volume (PTV) in beam direction. In order to improve the homogeneity of the dose distribution in the PTV, a feathering process (Gaussian edge feathering) is launched, which results in a number of feathered segments. For each of these segments a dose calculation is performed employing the in-house developed electron beam model along with the macro Monte Carlo dose calculation algorithm. Finally, an automated weight optimization of all segments is carried out and the total dose distribution is read back into Eclipse for display and evaluation. One academic and two clinical situations are investigated for possible benefits of MERT treatment compared to standard treatments performed in our clinics and treatment with a bolus electron conformal (BolusECT) method. RESULTS The MERT treatment plan of the academic case was superior to the standard single segment electron treatment plan in terms of organs at risk (OAR) sparing. Further, a comparison between an unfeathered and a feathered MERT plan showed better PTV coverage and homogeneity for the feathered plan, with V95% increased from 90% to 96% and V107% decreased from 8% to nearly 0%. For a clinical breast boost irradiation, the MERT plan led to a similar homogeneity in the PTV compared to the standard treatment plan while the mean body dose was lower for the MERT plan. Regarding the second clinical case, a whole breast treatment, MERT resulted in a reduction of the lung volume receiving more than 45% of the prescribed dose when compared to the standard plan. On the other hand, the MERT plan leads to a larger low-dose lung volume and a degraded dose homogeneity in the PTV. For the clinical cases evaluated in this work, treatment plans using the BolusECT technique resulted in a more homogenous PTV and CTV coverage but higher doses to the OARs than the MERT plans. CONCLUSIONS MERT treatments were successfully planned for phantom and clinical cases, applying a newly developed intuitive and efficient forward planning strategy that employs a MC based electron beam model for pMLC shaped electron beams. It is shown that MERT can lead to a dose reduction in OARs compared to other methods. The process of feathering MERT segments results in an improvement of the dose homogeneity in the PTV.

Shakespeare, the Middle Ages and the Luck of the British: Medievalism and National Identity in The Hollow Crown (2012)

Images of the medieval past have long been fertile soil for the identity politics of subsequent periods. Rather than “authentically” reproducing the Middle Ages, medievalism therefore usually tells us more about the concerns and ideological climate of its own time and place of origin. To dramatise the nascent nation, Shakespeare resorts to medievalism in his history plays. Centuries later, the BBC-produced television mini-serial The Hollow Crown – adapting Shakespeare’s second histories tetralogy – revamps this negotiation of national identity for the “Cultural Olympiad” in the run-up to the 2012 London Olympics. In this context of celebratory introspection, The Hollow Crown weaves a genealogical narrative consisting of the increasingly “glorious” medieval history depicted and “national” Shakespearean heritage in order to valorise 21st-century “Britishness”. Encouraging a reading of the histories as medieval history, the films construct an ostensibly inclusive, liberal-minded national identity grounded in this history. Moreover, medieval kingship is represented in distinctly sentimentalising and humanising terms, fostering emotional identification especially with the no longer ambivalent Hal/Henry V and making him an apt model for present-day British grandeur. However, the fact that the films in return marginalise female, Scottish, Irish and Welsh characters gives rise to doubts as to whether this vision of Shakespeare’s Middle Ages really is, as the producers claimed, “for everybody”.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

Measurement of the drift field in the ARGONTUBE LAr TPC with 266 nm pulsed laser beams

ARGONTUBE is a liquid argon time projection chamber (LAr TPC) with a drift field generated in-situ by a Greinacher voltage multiplier circuit. We present results on the measurement of the drift-field distribution inside ARGONTUBE using straight ionization tracks generated by an intense UV laser beam. Our analysis is based on a simplified model of the charging of a multi-stage Greinacher circuit to describe the voltages on the field cage rings.

NA61/SHINE facility at the CERN SPS: beams and detector system

NA61/SHINE (SPS Heavy Ion and Neutrino Experiment) is a multi-purpose experimental facility to study hadron production in hadron-proton, hadron-nucleus and nucleus-nucleus collisions at the CERN Super Proton Synchrotron. It recorded the first physics data with hadron beams in 2009 and with ion beams (secondary 7Be beams) in 2011. NA61/SHINE has greatly profited from the long development of the CERN proton and ion sources and the accelerator chain as well as the H2 beamline of the CERN North Area. The latter has recently been modified to also serve as a fragment separator as needed to produce the Be beams for NA61/SHINE. Numerous components of the NA61/SHINE set-up were inherited from its predecessors, in particular, the last one, the NA49 experiment. Important new detectors and upgrades of the legacy equipment were introduced by the NA61/SHINE Collaboration. This paper describes the state of the NA61/SHINE facility — the beams and the detector system — before the CERN Long Shutdown I, which started in March 2013.

Bounding Standard Gaussian Tail Probabilities

We review various inequalities for Mills' ratio (1 - Φ)= Ø, where Ø and Φ denote the standard Gaussian density and distribution function, respectively. Elementary considerations involving finite continued fractions lead to a general approximation scheme which implies and refines several known bounds.