Single hadron response measurement and calorimeter jet energy scale uncertainty with the ATLAS detector at the LHC

The uncertainty on the calorimeter energy response to jets of particles is derived for the ATLAS experiment at the Large Hadron Collider (LHC). First, the calorimeter response to single isolated charged hadrons is measured and compared to the Monte Carlo simulation using proton-proton collisions at centre-of-mass energies of root s = 900 GeV and 7 TeV collected during 2009 and 2010. Then, using the decay of K-s and Lambda particles, the calorimeter response to specific types of particles (positively and negatively charged pions, protons, and anti-protons) is measured and compared to the Monte Carlo predictions. Finally, the jet energy scale uncertainty is determined by propagating the response uncertainty for single charged and neutral particles to jets. The response uncertainty is 2-5 % for central isolated hadrons and 1-3 % for the final calorimeter jet energy scale.

Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set

Stepwise uncertainty reduction (SUR) strategies aim at constructing a sequence of points for evaluating a function  f in such a way that the residual uncertainty about a quantity of interest progressively decreases to zero. Using such strategies in the framework of Gaussian process modeling has been shown to be efficient for estimating the volume of excursion of f above a fixed threshold. However, SUR strategies remain cumbersome to use in practice because of their high computational complexity, and the fact that they deliver a single point at each iteration. In this article we introduce several multipoint sampling criteria, allowing the selection of batches of points at which f can be evaluated in parallel. Such criteria are of particular interest when f is costly to evaluate and several CPUs are simultaneously available. We also manage to drastically reduce the computational cost of these strategies through the use of closed form formulas. We illustrate their performances in various numerical experiments, including a nuclear safety test case. Basic notions about kriging, auxiliary problems, complexity calculations, R code, and data are available online as supplementary materials.

5-year results comparing mineral trioxide aggregate and adhesive resin composite for root-end sealing in apical surgery

INTRODUCTION Recent meta-analyses of the outcome of apical surgery using modern techniques including microsurgical principles and high-power magnification have yielded higher rates of healing. However, the information is mainly based on 1- to 2-year follow-up data. The present prospective study was designed to re-examine a large sample of teeth treated with apical surgery after 5 years. METHODS Patients were recalled 5 years after apical surgery, and treated teeth were classified as healed or not healed based on clinical and radiographic examination. (The latter was performed independently by 3 observers). Two different methods of root-end preparation and filling (primary study parameters) were to be compared (mineral trioxide aggregate [MTA] vs adhesive resin composite [COMP]) without randomization. RESULTS A total of 271 patients and teeth from a 1-year follow-up sample of 339 could be re-examined after 5 years (dropout rate = 20.1%). The overall rate of healed cases was 84.5% with a significant difference (P = .0003) when comparing MTA (92.5%) and COMP (76.6%). The evaluation of secondary study parameters yielded no significant difference for healing outcome when comparing subcategories (ie, sex, age, type of tooth treated, post/screw, type of surgery). CONCLUSIONS The results from this prospective nonrandomized clinical study with a 5-year follow-up of 271 teeth indicate that MTA exhibited a higher healing rate than COMP in the longitudinal prognosis of root-end sealing.

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.

Networks of Information Exchange Under Uncertainty: The Case of Unconventional Gas Development in the United Kingdom

Modern policy-making is increasingly influenced by different types of uncertainty. Political actors are supposed to behave differently under the context of uncertainty then in “usual” decision-making processes. Actors exchange information in order to convince other actors and decision-makers, to coordinate their lobbying activities and form coalitions, and to get information and learn on the substantive issue. The literature suggests that preference similarity, social trust, perceived power and functional interdependence are particularly important drivers of information exchange. We assume that social trust as well as being connected to scientific actors is more important under uncertainty than in a setting with less uncertainty. To investigate information exchange under uncertainty analyze the case of unconventional shale gas development in the UK from 2008 till 2014. Our study will rely on statistical analyses of survey data on a diverse set of actors dealing with shale gas development and regulation in the UK.

Policy Instrument Selection under Uncertainty: The Case of Micropollution Regulation

The paper addresses the question of which factors drive the formation of policy preferences when there are remaining uncertainties about the causes and effects of the problem at stake. To answer this question we examine policy preferences reducing aquatic micropollutants, a specific case of water protection policy and different actor groups (e.g. state, science, target groups). Here, we contrast two types of policy preferences: a) preventive or source-directed policies, which mitigate pollution in order to avoid contact with water; and b) reactive or end-of-pipe policies, which filter water already contaminated by pollutants. In a second step, we analyze the drivers for actors’ policy preferences by focusing on three sets of explanations, i.e. participation, affectedness and international collaborations. The analysis of our survey data, qualitative interviews and regression analysis of the Swiss political elite show that participation in the policy-making process leads to knowledge exchange and reduces uncertainties about the policy problem, which promotes preferences for preventive policies. Likewise, actors who are affected by the consequences of micropollutants, such as consumer or environmental associations, opt for anticipatory policies. Interestingly, we find that uncertainties about the effectiveness of preventive policies can promote preferences for end-of-pipe policies. While preventive measures often rely on (uncertain) behavioral changes of target groups, reactive policies are more reliable when it comes to fulfilling defined policy goals. Finally, we find that in a transboundary water management context, actors with international collaborations prefer policies that produce immediate and reliable outcomes.

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

A specialist root herbivore reduces plant resistance and uses an induced plant volatile to aggregate in a density-dependent manner

1.Leaf-herbivore attack often triggers induced resistance in plants. However, certain specialist herbivores can also take advantage of the induced metabolic changes. In some cases, they even manipulate plant resistance, leading to a phenomenon called induced susceptibility. Compared to above-ground plant-insect interactions, little is known about the prevalence and consequences of induced responses below-ground. 2.A recent study suggested that feeding by the specialist root herbivore Diabrotica virgifera virgifera makes maize roots more susceptible to conspecifics. To better understand this phenomenon, we conducted a series of experiments to study the behavioural responses and elucidate the underlying biochemical mechanisms. 3.We found that D. virgifera benefitted from feeding on a root system in groups of intermediate size (3–9 larvae/plant in the laboratory), whereas its performance was reduced in large groups (12 larvae/plant). Interestingly, the herbivore was able to select host plants with a suitable density of conspecifics by using the induced plant volatile (E)-β-caryophyllene in a dose-dependent manner. Using a split root experiment, we show that the plant-induced susceptibility is systemic and, therefore, plant mediated. Chemical analyses on plant resource reallocation and defences upon herbivory showed that the systemic induced-susceptibility is likely to stem from a combination of (i) increased free amino acid concentrations and (ii) relaxation of defence inducibility. 4.These findings show that herbivores can use induced plant volatiles in a density-dependent manner to aggregate on a host plant and change its metabolism to their own benefit. Our study furthermore helps to explain the remarkable ecological success of D. virgifera in maize fields around the world.

Collaboration patterns, external shocks and uncertainty: Swiss nuclear energy politics before and after Fukushima

Energy shocks like the Fukushima accident can have important political consequences. This article examines their impact on collaboration patterns between collective actors in policy processes. It argues that external shocks create both behavioral uncertainty, meaning that actors do not know about other actors' preferences, and policy uncertainty on the choice and consequences of policy instruments. The context of uncertainty interacts with classical drivers of actor collaboration in policy processes. The analysis is based on a dataset comprising interview and survey data on political actors in two subsequent policy processes in Switzerland and Exponential Random Graph Models for network data. Results first show that under uncertainty, collaboration of actors in policy processes is less based on similar preferences than in stable contexts, but trust and knowledge of other actors are more important. Second, under uncertainty, scientific actors are not preferred collaboration partners.