Generalized information reuse for optimization under uncertainty with non-sample average estimators

In optimization under uncertainty for engineering design, the behavior of the system outputs due to uncertain inputs needs to be quantified at each optimization iteration, but this can be computationally expensive. Multi-fidelity techniques can significantly reduce the computational cost of Monte Carlo sampling methods for quantifying the effect of uncertain inputs, but existing multi-fidelity techniques in this context apply only to Monte Carlo estimators that can be expressed as a sample average, such as estimators of statistical moments. Information reuse is a particular multi-fidelity method that treats previous optimization iterations as lower-fidelity models. This work generalizes information reuse to be applicable to quantities with non-sample average estimators. The extension makes use of bootstrapping to estimate the error of estimators and the covariance between estimators at different fidelities. Specifically, the horsetail matching metric and quantile function are considered as quantities whose estimators are not sample-averages. In an optimization under uncertainty for an acoustic horn design problem, generalized information reuse demonstrated computational savings of over 60% compared to regular Monte Carlo sampling

Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures

Peer reviewe

A semi-Markov model for stroke with piecewise-constant hazards in the presence of left, right and interval censoring.

This paper presents a parametric method of fitting semi-Markov models with piecewise-constant hazards in the presence of left, right and interval censoring. We investigate transition intensities in a three-state illness-death model with no recovery. We relax the Markov assumption by adjusting the intensity for the transition from state 2 (illness) to state 3 (death) for the time spent in state 2 through a time-varying covariate. This involves the exact time of the transition from state 1 (healthy) to state 2. When the data are subject to left or interval censoring, this time is unknown. In the estimation of the likelihood, we take into account interval censoring by integrating out all possible times for the transition from state 1 to state 2. For left censoring, we use an Expectation-Maximisation inspired algorithm. A simulation study reflects the performance of the method. The proposed combination of statistical procedures provides great flexibility. We illustrate the method in an application by using data on stroke onset for the older population from the UK Medical Research Council Cognitive Function and Ageing Study

Bootstrap-after-Bootstrap Model Averaging for Reducing Model Uncertainty in Model Selection for Air Pollution Mortality Studies

Ba c k g r o u n d: Concerns have been raised about findings of associations between particulate matter (PM) air pollution and mortality that have been based on a single “best ” model arising from a model selection procedure, because such a strategy may ignore model uncertainty inherently involved in searching through a set of candidate models to find the best model. Model averaging has been proposed as a method of allowing for model uncertainty in this context. Objectives: To propose an extension (double BOOT) to a previously described bootstrap modelaveraging procedure (BOOT) for use in time series studies of the association between PM and mortality. We compared double BOOT and BOOT with Bayesian model averaging (BMA) and a standard method of model selection [standard Akaike’s information criterion (AIC)]. Me t h o d: Actual time series data from the United States are used to conduct a simulation study to compare and contrast the performance of double BOOT, BOOT, BMA, and standard AIC. Re s u l t s: Double BOOT produced estimates of the effect of PM on mortality that have had smaller root mean squared error than did those produced by BOOT, BMA, and standard AIC. This performance boost resulted from estimates produced by double BOOT having smaller variance than those produced by BOOTand BMA. Co n c l u s i o n s: Double BOOT is a viable alternative to BOOT and BMA for producing estimates of the mortality effect of PM. Key w o r d s: air pollution, Bayesian, bootstrap, model averaging, mortality, particulate matter. Environ Health Perspect 118:131–136 (2010). doi:10.1289/ehp.0901007 available vi

An efficient semiparametric maxima estimator of the extremal index

The extremal index $\theta$, a measure of the degree of local dependence in the extremes of a stationary process, plays an important role in extreme value analyses. We estimate $\theta$ semiparametrically, using the relationship between the distribution of block maxima and the marginal distribution of a process to define a semiparametric model. We show that these semiparametric estimators are simpler and substantially more efficient than their parametric counterparts. We seek to improve efficiency further using maxima over sliding blocks. A simulation study shows that the semiparametric estimators are competitive with the leading estimators. An application to sea-surge heights combines inferences about $\theta$ with a standard extreme value analysis of block maxima to estimate marginal quantiles.Comment: 17 pages, 7 figures. Minor edits made to version 1 prior to journal publication. The final publication is available at Springer via http://dx.doi.org/10.1007/s10687-015-0221-

Recessive germline SDHA and SDHB mutations causing leukodystrophy and isolated mitochondrial complex II deficiency

Background Isolated complex II deficiency is a rare form of mitochondrial disease, accounting for approximately 2% of all respiratory chain deficiency diagnoses. The succinate dehydrogenase (SDH) genes (SDHA, SDHB, SDHC and SDHD) are autosomally-encoded and transcribe the conjugated heterotetramers of complex II via the action of two known assembly factors (SDHAF1 and SDHAF2). Only a handful of reports describe inherited SDH gene defects as a cause of paediatric mitochondrial disease, involving either SDHA (Leigh syndrome, cardiomyopathy) or SDHAF1 (infantile leukoencephalopathy). However, all four SDH genes, together with SDHAF2, have known tumour suppressor functions, with numerous germline and somatic mutations reported in association with hereditary cancer syndromes, including paraganglioma and pheochromocytoma. Methods and results Here, we report the clinical and molecular investigations of two patients with histochemical and biochemical evidence of a severe, isolated complex II deficiency due to novel SDH gene mutations; the first patient presented with cardiomyopathy and leukodystrophy due to compound heterozygous p.Thr508Ile and p.Ser509Leu SDHA mutations, while the second patient presented with hypotonia and leukodystrophy with elevated brain succinate demonstrated by MR spectroscopy due to a novel, homozygous p.Asp48Val SDHB mutation. Western blotting and BN-PAGE studies confirmed decreased steady-state levels of the relevant SDH subunits and impairment of complex II assembly. Evidence from yeast complementation studies provided additional support for pathogenicity of the SDHB mutation. Conclusions Our report represents the first example of SDHB mutation as a cause of inherited mitochondrial respiratory chain disease and extends the SDHA mutation spectrum in patients with isolated complex II deficiency

Entanglement-free Heisenberg-limited phase estimation

Measurement underpins all quantitative science. A key example is the measurement of optical phase, used in length metrology and many other applications. Advances in precision measurement have consistently led to important scientific discoveries. At the fundamental level, measurement precision is limited by the number N of quantum resources (such as photons) that are used. Standard measurement schemes, using each resource independently, lead to a phase uncertainty that scales as 1/sqrt(N) - known as the standard quantum limit. However, it has long been conjectured that it should be possible to achieve a precision limited only by the Heisenberg uncertainty principle, dramatically improving the scaling to 1/N. It is commonly thought that achieving this improvement requires the use of exotic quantum entangled states, such as the NOON state. These states are extremely difficult to generate. Measurement schemes with counted photons or ions have been performed with N <= 6, but few have surpassed the standard quantum limit and none have shown Heisenberg-limited scaling. Here we demonstrate experimentally a Heisenberg-limited phase estimation procedure. We replace entangled input states with multiple applications of the phase shift on unentangled single-photon states. We generalize Kitaev's phase estimation algorithm using adaptive measurement theory to achieve a standard deviation scaling at the Heisenberg limit. For the largest number of resources used (N = 378), we estimate an unknown phase with a variance more than 10 dB below the standard quantum limit; achieving this variance would require more than 4,000 resources using standard interferometry. Our results represent a drastic reduction in the complexity of achieving quantum-enhanced measurement precision.Comment: Published in Nature. This is the final versio

Using technology to deliver cancer follow-up : a systematic review

Peer reviewedPublisher PD

Presynaptic partner selection during retinal circuit reassembly varies with timing of neuronal regeneration in vivo

Whether neurons can restore their original connectivity patterns during circuit repair is unclear. Taking advantage of the regenerative capacity of zebrafish retina, we show here the remarkable specificity by which surviving neurons reassemble their connectivity upon regeneration of their major input. H3 horizontal cells (HCs) normally avoid red and green cones, and prefer ultraviolet over blue cones. Upon ablation of the major (ultraviolet) input, H3 HCs do not immediately increase connectivity with other cone types. Instead, H3 dendrites retract and re-extend to contact new ultraviolet cones. But, if regeneration is delayed or absent, blue-cone synaptogenesis increases and ectopic synapses are made with red and green cones. Thus, cues directing synapse specificity can be maintained following input loss, but only within a limited time period. Further, we postulate that signals from the major input that shape the H3 HC's wiring pattern during development persist to restrict miswiring after damage