Analysis, Tracing, Characterization and Performance Modeling of Select ASCI Applications for BlueGene/L Using Parallel Discrete Event Simulation

Caltech's Jet Propulsion Laboratory (JPL) and Center for Advanced Computer Architecture (CACR) are conducting application and simulation analyses of Blue Gene/L[1] in order to establish a range of effectiveness of the architecture in performing important classes of computations and to determine the design sensitivity of the global interconnect network in support of real world ASCI application execution

Bayesian estimation of parameters in a regional hydrological model

International audienceThis study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC) analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1) process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis</p

Gender and White-Collar Crime in Norway: An Empirical Study of Media Reports

This is the accepted, refereed and final manuscript to the article.Purpose: Recent work on gender and white-collar crime is extended through a case study examining gender differences in white-collar crime in Norway. Methods: Based on a content analysis of reports in Norwegian newspapers and court documents regarding white-collar crime cases that were of enough importance and notoriety so as to garner attention from national media outlets, this study investigates whether high level white-collar crime in Norway is gender neutral or gender specific (i.e., mostly male) as it is in the United States. Results: Even though gender inequality is much lower in Norway than the United States, the gender gap in Norwegian white-collar crime appears to be nearly identical to that observed in the United States. Out of 329 individuals identified in the newspaper reports only 22 (6.7%) were women. Conclusions: Formal gender equality does not appear to lead to increased involvement of women in white-collar crime, thus providing little support for the emancipation hypothesis and suggesting that theories focused on gendered focal concerns and gendered access to criminal opportunities have greater utility as explanations of the gender gap in white-collar crime.1, forfatterversjo

Considerations in the Development of a Scientific Social Work

A key issue in social work\u27s struggle to develop a legitimate and distinct knowledge base is the development of a scientific model suited to the needs and objectives of the profession. Although various approaches have been proposed, they have tended to dichotomize the issues into one of science versus nonscience. In response to this situation, this paper presents an integrative approach to the development of a scientific social work. In addition, it is argued that values can (and should) be an integral part of a scientific approach and that they are legitimate criteria for the evaluation of social theories

A Bayesian spatial assimilation scheme for snow coverage observations in a gridded snow model

International audienceA method for assimilating remotely sensed snow covered area (SCA) into the snow subroutine of a grid distributed precipitation-runoff model (PRM) is presented. The PRM is assumed to simulate the snow state in each grid cell by a snow depletion curve (SDC), which relates that cell's SCA to its snow cover mass balance. The assimilation is based on Bayes' theorem, which requires a joint prior distribution of the SDC variables in all the grid cells. In this paper we propose a spatial model for this prior distribution, and include similarities and dependencies among the grid cells. Used to represent the PRM simulated snow cover state, our joint prior model regards two elevation gradients and a degree-day factor as global variables, rather than describing their effect separately for each cell. This transformation results in smooth normalised surfaces for the two related mass balance variables, supporting a strong inter-cell dependency in their joint prior model. The global features and spatial interdependency in the prior model cause each SCA observation to provide information for many grid cells. The spatial approach similarly facilitates the utilisation of observed discharge. Assimilation of SCA data using the proposed spatial model is evaluated in a 2400 km2 mountainous region in central Norway (61° N, 9° E), based on two Landsat 7 ETM+ images generalized to 1 km2 resolution. An image acquired on 11 May, a week before the peak flood, removes 78% of the variance in the remaining snow storage. Even an image from 4 May, less than a week after the melt onset, reduces this variance by 53%. These results are largely improved compared to a cell-by-cell independent assimilation routine previously reported. Including observed discharge in the updating information improves the 4 May results, but has weak effect on 11 May. Estimated elevation gradients are shown to be sensitive to informational deficits occurring at high altitude, where snowmelt has not started and the snow coverage is close to unity. Caution is therefore required when using early images