From Empirical Bayes to Full Bayes: Methods for Analyzubg Traffic Safey Data, October 25, 2004

Traffic safety engineers are among the early adopters of Bayesian statistical tools for analyzing crash data. As in many other areas of application, empirical Bayes methods were their first choice, perhaps because they represent an intuitively appealing, yet relatively easy to implement alternative to purely classical approaches. With the enormous progress in numerical methods made in recent years and with the availability of free, easy to use software that permits implementing a fully Bayesian approach, however, there is now ample justification to progress towards fully Bayesian analyses of crash data. The fully Bayesian approach, in particular as implemented via multi-level hierarchical models, has many advantages over the empirical Bayes approach. In a full Bayesian analysis, prior information and all available data are seamlessly integrated into posterior distributions on which practitioners can base their inferences. All uncertainties are thus accounted for in the analyses and there is no need to pre-process data to obtain Safety Performance Functions and other such prior estimates of the effect of covariates on the outcome of interest. In this light, fully Bayesian methods may well be less costly to implement and may result in safety estimates with more realistic standard errors. In this manuscript, we present the full Bayesian approach to analyzing traffic safety data and focus on highlighting the differences between the empirical Bayes and the full Bayes approaches. We use an illustrative example to discuss a step-by-step Bayesian analysis of the data and to show some of the types of inferences that are possible within the full Bayesian framework.

Creating possibilities for collective creativity : Brokerage Functions in Practice-Based Innovation

In the network era, creative achievements like innovations are more and more often created in interaction among different actors. The complexity of today‘s problems transcends the individual human mind, requiring not only individual but also collective creativity. In collective creativity, it is impossible to trace the source of new ideas to an individual. Instead, creative activity emerges from the collaboration and contribution of many individuals, thereby blurring the contribution of specific individuals in creating ideas. Collective creativity is often associated with diversity of knowledge, skills, experiences and perspectives. Collaboration between diverse actors thus triggers creativity and gives possibilities for collective creativity. This dissertation investigates collective creativity in the context of practice-based innovation. Practice-based innovation processes are triggered by problem setting in a practical context and conducted in non-linear processes utilising scientific and practical knowledge production and creation in cross-disciplinary innovation networks. In these networks diversity or distances between innovation actors are essential. Innovation potential may be found in exploiting different kinds of distances. This dissertation presents different kinds of distances, such as cognitive, functional and organisational which could be considered as sources of creativity and thus innovation. However, formation and functioning of these kinds of innovation networks can be problematic. Distances between innovating actors may be so great that a special interpretation function is needed – that is, brokerage. This dissertation defines factors that enhance collective creativity in practice-based innovation and especially in the fuzzy front end phase of innovation processes. The first objective of this dissertation is to study individual and collective creativity at the employee level and identify those factors that support individual and collective creativity in the organisation. The second objective is to study how organisations use external knowledge to support collective creativity in their innovation processes in open multi-actor innovation. The third objective is to define how brokerage functions create possibilities for collective creativity especially in the context of practice-based innovation. The research objectives have been studied through five substudies using a case-study strategy. Each substudy highlights various aspects of creativity and collective creativity. The empirical data consist of materials from innovation projects arranged in the Lahti region, Finland, or materials from the development of innovation methods in the Lahti region. The Lahti region has been chosen as the research context because the innovation policy of the region emphasises especially the promotion of practice-based innovations. The results of this dissertation indicate that all possibilities of collective creativity are not utilised in internal operations of organisations. The dissertation introduces several factors that could support collective creativity in organisations. However, creativity as a social construct is understood and experienced differently in different organisations, and these differences should be taken into account when supporting creativity in organisations. The increasing complexity of most potential innovations requires collaborative creative efforts that often exceed the boundaries of the organisation and call for the involvement of external expertise. In practice-based innovation different distances are considered as sources of creativity. This dissertation gives practical implications on how it is possible to exploit different kinds of distances knowingly. It underlines especially the importance of brokerage functions in open, practice-based innovation in order to create possibilities for collective creativity. As a contribution of this dissertation, a model of brokerage functions in practice-based innovation is formulated. According to the model, the results and success of brokerage functions are based on the context of brokerage as well as the roles, tasks, skills and capabilities of brokers. The brokerage functions in practice-based innovation are also possible to divide into social and cognitive brokerage.

Improving educational functions in botanic gardens by employing landscape narratives

Landscape narrative, combining landscape and narrative, has been employed to create storytelling layouts and interpretive information in some famous botanic gardens. In order to assess the educational effectiveness of using "landscape narrative" in landscape design, the Heng-Chun Tropical Botanical Garden in Taiwan was chosen as research target for an empirical study. Based on cognitive theory and the affective responses of environmental psychology, computer simulations and video recordings were used to create five themed display areas with landscape narrative elements. Two groups of pupils watched simulated films. The pupils were then given an evaluation test and questionnaire, to determine the effectiveness of the landscape narrative. When the content was well associated and matched with the narrative landscape, the comprehension and retention of content was increased significantly. The results also indicated that visual preference of narrative landscape scenes was increased. This empirical study can be regarded as a successful model of integrating landscape narrative and interpretation practice that can be applied to the design of new theme displays in botanic gardens to improve both the effectiveness of interpretation plans and the visual preference of visitors. (c) 2008 Elsevier B.V. All rights reserved.

EMG signal filtering based on Empirical Mode Decomposition

This paper introduces a procedure for filtering electromyographic (EMG) signals. Its key element is the Empirical Mode Decomposition, a novel digital signal processing technique that can decompose my time-series into a set of functions designated as intrinsic mode functions. The procedure for EMG signal filtering is compared to a related approach based on the wavelet transform. Results obtained from the analysis of synthetic and experimental EMG signals show that Our method can be Successfully and easily applied in practice to attenuation of background activity in EMG signals. (c) 2006 Elsevier Ltd. All rights reserved.

Empirical mode decomposition: a novel technique for the study of tremor time series

Tremor is a clinical feature characterized by oscillations of a part of the body. The detection and study of tremor is an important step in investigations seeking to explain underlying control strategies of the central nervous system under natural (or physiological) and pathological conditions. It is well established that tremorous activity is composed of deterministic and stochastic components. For this reason, the use of digital signal processing techniques (DSP) which take into account the nonlinearity and nonstationarity of such signals may bring new information into the signal analysis which is often obscured by traditional linear techniques (e.g. Fourier analysis). In this context, this paper introduces the application of the empirical mode decomposition (EMD) and Hilbert spectrum (HS), which are relatively new DSP techniques for the analysis of nonlinear and nonstationary time-series, for the study of tremor. Our results, obtained from the analysis of experimental signals collected from 31 patients with different neurological conditions, showed that the EMD could automatically decompose acquired signals into basic components, called intrinsic mode functions (IMFs), representing tremorous and voluntary activity. The identification of a physical meaning for IMFs in the context of tremor analysis suggests an alternative and new way of detecting tremorous activity. These results may be relevant for those applications requiring automatic detection of tremor. Furthermore, the energy of IMFs was visualized as a function of time and frequency by means of the HS. This analysis showed that the variation of energy of tremorous and voluntary activity could be distinguished and characterized on the HS. Such results may be relevant for those applications aiming to identify neurological disorders. In general, both the HS and EMD demonstrated to be very useful to perform objective analysis of any kind of tremor and can therefore be potentially used to perform functional assessment.

Conditional mean functions of non-linear models of US output

We compare a number of models of post War US output growth in terms of the degree and pattern of non-linearity they impart to the conditional mean, where we condition on either the previous period's growth rate, or the previous two periods' growth rates. The conditional means are estimated non-parametrically using a nearest-neighbour technique on data simulated from the models. In this way, we condense the complex, dynamic, responses that may be present in to graphical displays of the implied conditional mean.

Global-scale climate impact functions: the relationship between climate forcing and impact

Although there is a strong policy interest in the impacts of climate change corresponding to different degrees of climate change, there is so far little consistent empirical evidence of the relationship between climate forcing and impact. This is because the vast majority of impact assessments use emissions-based scenarios with associated socio-economic assumptions, and it is not feasible to infer impacts at other temperature changes by interpolation. This paper presents an assessment of the global-scale impacts of climate change in 2050 corresponding to defined increases in global mean temperature, using spatially-explicit impacts models representing impacts in the water resources, river flooding, coastal, agriculture, ecosystem and built environment sectors. Pattern-scaling is used to construct climate scenarios associated with specific changes in global mean surface temperature, and a relationship between temperature and sea level used to construct sea level rise scenarios. Climate scenarios are constructed from 21 climate models to give an indication of the uncertainty between forcing and response. The analysis shows that there is considerable uncertainty in the impacts associated with a given increase in global mean temperature, due largely to uncertainty in the projected regional change in precipitation. This has important policy implications. There is evidence for some sectors of a non-linear relationship between global mean temperature change and impact, due to the changing relative importance of temperature and precipitation change. In the socio-economic sectors considered here, the relationships are reasonably consistent between socio-economic scenarios if impacts are expressed in proportional terms, but there can be large differences in absolute terms. There are a number of caveats with the approach, including the use of pattern-scaling to construct scenarios, the use of one impacts model per sector, and the sensitivity of the shape of the relationships between forcing and response to the definition of the impact indicator.

Solar wind-magnetosphere coupling functions on timescales of 1 day to 1 year

There are no direct observational methods for determining the total rate at which energy is extracted from the solar wind by the magnetosphere. In the absence of such a direct measurement, alternative means of estimating the energy available to drive the magnetospheric system have been developed using different ionospheric and magnetospheric indices as proxies for energy consumption and dissipation and thus the input. The so-called coupling functions are constructed from the parameters of the interplanetary medium, as either theoretical or empirical estimates of energy transfer, and the effectiveness of these coupling functions has been evaluated in terms of their correlation with the chosen index. A number of coupling functions have been studied in the past with various criteria governing event selection and timescale. The present paper contains an exhaustive survey of the correlation between geomagnetic activity and the near-Earth solar wind and two of the planetary indices at a wide variety of timescales. Various combinations of interplanetary parameters are evaluated with careful allowance for the effects of data gaps in the interplanetary data. We show that the theoretical coupling, P�, function first proposed by Vasyliunas et al. is superior at all timescales from 1-day to 1-year.

Extracting planetary waves from geomagnetic time series using Empirical Mode Decomposition

Empirical Mode Decomposition is presented as an alternative to traditional analysis methods to decompose geomagnetic time series into spectral components. Important comments on the algorithm and its variations will be given. Using this technique, planetary wave modes of 5-, 10-, and 16-day mean periods can be extracted from magnetic field components of three different stations in Germany. In a second step, the amplitude modulation functions of these wave modes can be shown to contain significant contribution from solar cycle variation through correlation with smoothed sunspot numbers. Additionally, the data indicate connections with geomagnetic jerk occurrences, supported by a second set of data providing reconstructed near-Earth magnetic field for 150 years. Usually attributed to internal dynamo processes within the Earth's outer core, the question of who is impacting whom will be briefly discussed here.

Empirical analysis of the Lieb-Oxford bound in ions and molecules

Universal properties of the Coulomb interaction energy apply to all many-electron systems. Bounds on the exchange-correlation energy, in particular, are important for the construction of improved density functionals. Here we investigate one such universal property-the Lieb-Oxford lower bound-for ionic and molecular systems. In recent work [J Chem Phys 127, 054106 (2007)], we observed that for atoms and electron liquids this bound may be substantially tightened. Calculations for a few ions and molecules suggested the same tendency, but were not conclusive due to the small number of systems considered. Here we extend that analysis to many different families of ions and molecules, and find that for these, too, the bound can be empirically tightened by a similar margin as for atoms and electron liquids. Tightening the Lieb-Oxford bound will have consequences for the performance of various approximate exchange-correlation functionals. (C) 2008 Wiley Periodicals Inc.

Longitudinal familial analysis of blood pressure involving parametric (co)variance functions

Abstract Background For analyzing longitudinal familial data we adopted a log-linear form to incorporate heterogeneity in genetic variance components over the time, and additionally a serial correlation term in the genetic effects at different levels of ages. Due to the availability of multiple measures on the same individual, we permitted environmental correlations that may change across time. Results Systolic blood pressure from family members from the first and second cohort was used in the current analysis. Measures of subjects receiving hypertension treatment were set as censored values and they were corrected. An initial check of the variance and covariance functions proposed for analyzing longitudinal familial data, using empirical semi-variogram plots, indicated that the observed trait dispersion pattern follows the assumptions adopted. Conclusion The corrections for censored phenotypes based on ordinary linear models may be an appropriate simple model to correct the data, ensuring that the original variability in the data was retained. In addition, empirical semi-variogram plots are useful for diagnosis of the (co)variance model adopted.

Reconstruction of spatiotemporal capture data by means of orthogonal functions: the case of skipjack tuna (Katsuwonus pelamis) in the central-east Atlantic

[EN] The information provided by the International Commission for the Conservation of Atlantic Tunas (ICCAT) on captures of skipjack tuna (Katsuwonus pelamis) in the central-east Atlantic has a number of limitations, such as gaps in the statistics for certain fleets and the level of spatiotemporal detail at which catches are reported. As a result, the quality of these data and their effectiveness for providing management advice is limited. In order to reconstruct missing spatiotemporal data of catches, the present study uses Data INterpolating Empirical Orthogonal Functions (DINEOF), a technique for missing data reconstruction, applied here for the first time to fisheries data. DINEOF is based on an Empirical Orthogonal Functions decomposition performed with a Lanczos method. DINEOF was tested with different amounts of missing data, intentionally removing values from 3.4% to 95.2% of data loss, and then compared with the same data set with no missing data. These validation analyses show that DINEOF is a reliable methodological approach of data reconstruction for the purposes of fishery management advice, even when the amount of missing data is very high.

Development of empirical potentials for amorphous silica

Development of empirical potentials for amorphous silica Amorphous silica (SiO2) is of great importance in geoscience and mineralogy as well as a raw material in glass industry. Its structure is characterized as a disordered continuous network of SiO4 tetrahedra. Many efforts have been undertaken to understand the microscopic properties of silica by classical molecular dynamics (MD) simulations. In this method the interatomic interactions are modeled by an effective potential that does not take explicitely into account the electronic degrees of freedom. In this work, we propose a new methodology to parameterize such a potential for silica using ab initio simulations, namely Car-Parrinello (CP) method [Phys. Rev. Lett. 55, 2471 (1985)]. The new potential proposed is compared to the BKS potential [Phys. Rev. Lett. 64, 1955 (1990)] that is considered as the benchmark potential for silica. First, CP simulations have been performed on a liquid silica sample at 3600 K. The structural features so obtained have been compared to the ones predicted by the classical BKS potential. Regarding the bond lengths the BKS tends to underestimate the Si-O bond whereas the Si-Si bond is overestimated. The inter-tetrahedral angular distribution functions are also not well described by the BKS potential. The corresponding mean value of theSiOSi angle is found to be ≃ 147◦, while the CP yields to aSiOSi angle centered around 135◦. Our aim is to fit a classical Born-Mayer/Coulomb pair potential using ab initio calculations. To this end, we use the force-matching method proposed by Ercolessi and Adams [Europhys. Lett. 26, 583 (1994)]. The CP configurations and their corresponding interatomic forces have been considered for a least square fitting procedure. The classical MD simulations with the resulting potential have lead to a structure that is very different from the CP one. Therefore, a different fitting criterion based on the CP partial pair correlation functions was applied. Using this approach the resulting potential shows a better agreement with the CP data than the BKS ones: pair correlation functions, angular distribution functions, structure factors, density of states and pressure/density were improved. At low temperature, the diffusion coefficients appear to be three times higher than those predicted by the BKS model, however showing a similar temperature dependence. Calculations have also been carried out on crystalline samples in order to check the transferability of the potential. The equilibrium geometry as well as the elastic constants of α-quartz at 0 K are well described by our new potential although the crystalline phases have not been considered for the parameterization. We have developed a new potential for silica which represents an improvement over the pair potentials class proposed so far. Furthermore, the fitting methodology that has been developed in this work can be applied to other network forming systems such as germania as well as mixtures of SiO2 with other oxides (e.g. Al2O3, K2O, Na2O).

Anti-Americanism in Europe: Theoretical Mechanisms and Empirical Evidence

One of the most popular explanations for post-9/11 anti-Americanism argues that resentment against America and Americans is mainly a function of the US government’s unpopular actions. The present article challenges this interpretation: first, it argues that neither the vitality of the resentment in times when the United States had no influence in the respective parts of the world nor its recent radical manifestations are accounted for in a political reductionist framework. In fact, specific traditions of anti-Americanism have an influence on the negative attitudes observed today, as a comparison between Britain, France, Germany, and Poland reveals. Second, this article suggests an alternative theoretical approach. Anti-Americanism can be explained by two basic mechanisms: it functions as a strategy to project denied and disliked self-concepts onto an external object, and it offers an interpretation frame for complex social processes that allows to reduce cognitive dissonance. Multivariate analyses based on empirical data collected in the Pew surveys of 2002 and 2007 show the fruitfulness of our theoretical approach.

Executive functions after pediatric mild traumatic brain injury: A prospective short-term longitudinal study

Traumatic brain injuries (TBIs) occur frequently in childhood and entail broad cognitive deficits, particularly in the domain of executive functions (EF). Concerning mild TBI (mTBI), only little empirical evidence is available on acute and postacute performance in EF. Given that EF are linked to school adaptation and achievement, even subtle deficits in performance may affect children's academic careers. The present study assessed performance in the EF components of inhibition, working memory (WM), and switching in children after mTBI. Regarding both acute and postacute consequences, performance trajectories were measured in 13 patients aged between 5 and 10 years and 13 controls who were closely matched in terms of sex, age, and education. Performance in the EF components of inhibition, switching, and WM was assessed in a short-term longitudinal design at 2, 6, and 12 weeks after the mTBI. Results indicate subtle deficits after mTBI, which became apparent in the longitudinal trajectory in the EF components of switching and WM. Compared with controls, children who sustained mTBI displayed an inferior performance enhancement across testing sessions in the first 6 weeks after the injury in switching and WM, resulting in a delayed deficit in the EF component of WM 12 weeks after the injury. Results are interpreted as mTBI-related deficits that become evident in terms of an inability to profit from previous learning opportunities, a finding that is potentially important for children's mastery of their daily lives.