Small ensemble of kriging models for optimization

The Efficient Global Optimization (EGO) algorithm uses a conditional Gaus-sian Process (GP) to approximate an objective function known at a finite number of observation points and sequentially adds new points which maximize the Expected Improvement criterion according to the GP. The important factor that controls the efficiency of EGO is the GP covariance function (or kernel) which should be chosen according to the objective function. Traditionally, a pa-rameterized family of covariance functions is considered whose parameters are learned through statistical procedures such as maximum likelihood or cross-validation. However, it may be questioned whether statistical procedures for learning covariance functions are the most efficient for optimization as they target a global agreement between the GP and the observations which is not the ultimate goal of optimization. Furthermore, statistical learning procedures are computationally expensive. The main alternative to the statistical learning of the GP is self-adaptation, where the algorithm tunes the kernel parameters based on their contribution to objective function improvement. After questioning the possibility of self-adaptation for kriging based optimizers, this paper proposes a novel approach for tuning the length-scale of the GP in EGO: At each iteration, a small ensemble of kriging models structured by their length-scales is created. All of the models contribute to an iterate in an EGO-like fashion. Then, the set of models is densified around the model whose length-scale yielded the best iterate and further points are produced. Numerical experiments are provided which motivate the use of many length-scales. The tested implementation does not perform better than the classical EGO algorithm in a sequential context but show the potential of the approach for parallel implementations

A health study of South African Bantu school children

No abstract available

Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics

Dozens of new models on fixation prediction are published every year and compared on open benchmarks such as MIT300 and LSUN. However, progress in the field can be difficult to judge because models are compared using a variety of inconsistent metrics. Here we show that no single saliency map can perform well under all metrics. Instead, we propose a principled approach to solve the benchmarking problem by separating the notions of saliency models, maps and metrics. Inspired by Bayesian decision theory, we define a saliency model to be a probabilistic model of fixation density prediction and a saliency map to be a metric-specific prediction derived from the model density which maximizes the expected performance on that metric given the model density. We derive these optimal saliency maps for the most commonly used saliency metrics (AUC, sAUC, NSS, CC, SIM, KL-Div) and show that they can be computed analytically or approximated with high precision. We show that this leads to consistent rankings in all metrics and avoids the penalties of using one saliency map for all metrics. Our method allows researchers to have their model compete on many different metrics with state-of-the-art in those metrics: "good" models will perform well in all metrics.Comment: published at ECCV 201

The Role of the Novel Exopolyphosphatase MT0516 in Mycobacterium tuberculosis Drug Tolerance and Persistence

Inorganic polyphosphate (poly P) has been postulated to play a regulatory role in the transition to bacterial persistence. In bacteria, poly P balance in the cell is maintained by the hydrolysis activity of the exopolyphosphatase PPX. However, the Mycobacterium tuberculosis PPX has not been characterized previously. Here we show that recombinant MT0516 hydrolyzes poly P, and an MT0516-deficient M. tuberculosis mutant exhibits elevated intracellular levels of poly P and increased expression of the genes mprB, sigE, and rel relative to the isogenic wild-type strain, indicating poly P-mediated signaling. Deficiency of MT0516 resulted in decelerated growth during logarithmic-phase in axenic cultures, and tolerance to the cell wall-active drug isoniazid. The MT0516-deficient mutant showed a significant survival defect in activated human macrophages and reduced persistence in the lungs of guinea pigs. We conclude that exopolyphosphatase is required for long-term survival of M. tuberculosis in necrotic lung lesions

Low urine pH and acid excretion do not predict bone fractures or the loss of bone mineral density: a prospective cohort study

<p>Abstract</p> <p>Background</p> <p>The acid-ash hypothesis, the alkaline diet, and related products are marketed to the general public. Websites, lay literature, and direct mail marketing encourage people to measure their urine pH to assess their health status and their risk of osteoporosis.</p> <p>The objectives of this study were to determine whether 1) low urine pH, or 2) acid excretion in urine [sulfate + chloride + 1.8x phosphate + organic acids] minus [sodium + potassium + 2x calcium + 2x magnesium mEq] in fasting morning urine predict: a) fragility fractures; and b) five-year change of bone mineral density (BMD) in adults.</p> <p>Methods</p> <p>Design: Cohort study: the prospective population-based Canadian Multicentre Osteoporosis Study. Multiple logistic regression was used to examine associations between acid excretion (urine pH and urine acid excretion) in fasting morning with the incidence of fractures (6804 person years). Multiple linear regression was used to examine associations between acid excretion with changes in BMD over 5-years at three sites: lumbar spine, femoral neck, and total hip (n = 651). Potential confounders controlled included: age, gender, family history of osteoporosis, physical activity, smoking, calcium intake, vitamin D status, estrogen status, medications, renal function, urine creatinine, body mass index, and change of body mass index.</p> <p>Results</p> <p>There were no associations between either urine pH or acid excretion and either the incidence of fractures or change of BMD after adjustment for confounders.</p> <p>Conclusion</p> <p>Urine pH and urine acid excretion do not predict osteoporosis risk.</p

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Molybdenum in the lower lias of England and Wales in relation to the incidence of teart

RESP-495

The location of trace elements in sedimentary rocks and in soils derived from them

RESP-588