Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data.

We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20-50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight

Towards the operational estimation of a radiological plume using data assimilation after a radiological accidental atmospheric release

International audienceIn the event of an accidental atmospheric release of radionuclides from a nuclear power plant, accurate real-time forecasting of the activity concentrations of radionuclides is required by the decision makers for the preparation of adequate countermeasures. The accuracy of the forecast plume is highly dependent on the source term estimation. On several academic test cases, including real data, inverse modelling and data assimilation techniques were proven to help in the assessment of the source term. In this paper, a semi-automatic method is proposed for the sequential reconstruction of the plume, by implementing a sequential data assimilation algorithm based on inverse modelling, with a care to develop realistic methods for operational risk agencies. The performance of the assimilation scheme has been assessed through the intercomparison between French and Finnish frameworks. Two dispersion models have been used: Polair3D and Silam developed in two different research centres. Different release locations, as well as different meteorological situations are tested. The existing and newly planned surveillance networks are used and realistically large multiplicative observational errors are assumed. The inverse modelling scheme accounts for strong error bias encountered with such errors. The efficiency of the data assimilation system is tested via statistical indicators. For France and Finland, the average performance of the data assimilation system is strong. However there are outlying situations where the inversion fails because of a too poor observability. In addition, in the case where the power plant responsible for the accidental release is not known, robust statistical tools are developed and tested to discriminate candidate release sites

Dispersion or Concentration for the 1.5 Generation?: Destination Choices of the Children of Immigrants in the U.S.

This paper examines determinants of destination choice for foreign-born and 1.5 generation adult children of immigrants in the U.S. An immigrant concentration- weighted accessibility parameter is included to assess the spatial structure of destination choice. A comparative origin-destination immigrant-native wage gap measure is also a strong determinant of destination choice, indicating the importance of relative labor market position. Although spatial assimilation perspectives would suggest that intergenerational social mobility should be connected with spatial dispersion, these models reveal the continuing importance of immigrant concentration for the 1.5 generation. Further, the increased model strength and parameter estimates associated with immigrant concentration and the accessibility measure suggest the spatial structure of destination choice depends on immigrant concentration at multiple scales – both to metro areas and to immigrant states or regions. The paper thus presents evidence for and suggests more attention to theorizing the geographic contexts of intergenerational immigrant incorporation

Dual state-parameter estimation of hydrological models using ensemble Kalman filter

Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state-parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can be estimated simultaneously; (2) the algorithm is recursive and therefore does not require storage of all past information, as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed, including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using a conceptual rainfall-runoff model. © 2004 Elsevier Ltd. All rights reserved

Towards the operational application of inverse modelling for the source identification and plume forecast of an accidental release of radionuclides

International audienceIn the event of an accidental atmospheric release of radionuclides from a nuclear power plant, accurate real-time forecasting of the activity concentrations of radionuclides is required by the decision makers for the preparation of adequate countermeasures. Yet, the accuracy of the forecast plume is highly dependent on the source term estimation. Inverse modelling and data assimilation techniques should help in that respect. In this presentation, a semi-automatic method is proposed for the sequential reconstruction of the plume, by implementing a sequential data assimilation algorithm based on inverse modelling, with a care to develop realistic methods for operational risk agencies. The performance of the assimilation scheme has been assessed through the intercomparison between French and Finnish frameworks. Three dispersion models have been used: Polair3D, with or without plume-in-grid, both developed at CEREA, and SILAM, developed at FMI. Different release locations, as well as different meteorological situations are tested. The existing and newly planned surveillance networks are used and realistically large observational errors are assumed. Statistical indicators to evaluate the efficiency of the method are presented and the results are discussed. In addition, in the case where the power plant responsible for the accidental release is not known, robust statistical tools aredeveloped and tested to discriminate candidate release sites

Imaging of a fluid injection process using geophysical data - A didactic example

In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts

Ensemble Kalman Filter Assimilation of ERT Data for Numerical Modeling of Seawater Intrusion in a Laboratory Experiment

Seawater intrusion in coastal aquifers is a worldwide problem exacerbated by aquifer overexploitation and climate changes. To limit the deterioration of water quality caused by saline intrusion, research studies are needed to identify and assess the performance of possible countermeasures, e.g., underground barriers. Within this context, numerical models are fundamental to fully understand the process and for evaluating the effectiveness of the proposed solutions to contain the saltwater wedge; on the other hand, they are typically affected by uncertainty on hydrogeological parameters, as well as initial and boundary conditions. Data assimilation methods such as the ensemble Kalman filter (EnKF) represent promising tools that can reduce such uncertainties. Here, we present an application of the EnKF to the numerical modeling of a laboratory experiment where seawater intrusion was reproduced in a specifically designed sandbox and continuously monitored with electrical resistivity tomography (ERT). Combining EnKF and the SUTRA model for the simulation of density-dependent flow and transport in porous media, we assimilated the collected ERT data by means of joint and sequential assimilation approaches. In the joint approach, raw ERT data (electrical resistances) are assimilated to update both salt concentration and soil parameters, without the need for an electrical inversion. In the sequential approach, we assimilated electrical conductivities computed from a previously performed electrical inversion. Within both approaches, we suggest dual-step update strategies to minimize the effects of spurious correlations in parameter estimation. The results show that, in both cases, ERT data assimilation can reduce the uncertainty not only on the system state in terms of salt concentration, but also on the most relevant soil parameters, i.e., saturated hydraulic conductivity and longitudinal dispersivity. However, the sequential approach is more prone to filter inbreeding due to the large number of observations assimilated compared to the ensemble size

Demographic growth and the distribution of language sizes

It is argued that the present log-normal distribution of language sizes is, to a large extent, a consequence of demographic dynamics within the population of speakers of each language. A two-parameter stochastic multiplicative process is proposed as a model for the population dynamics of individual languages, and applied over a period spanning the last ten centuries. The model disregards language birth and death. A straightforward fitting of the two parameters, which statistically characterize the population growth rate, predicts a distribution of language sizes in excellent agreement with empirical data. Numerical simulations, and the study of the size distribution within language families, validate the assumptions at the basis of the model.Comment: To appear in Int. J. Mod. Phys. C (2008

Atmospheric considerations for CTA site search using global models

The Cherenkov Telescope Array (CTA) will be the next high-energy gamma-ray observatory. Selection of the sites, one in each hemisphere, is not obvious since several factors have to be taken into account. Among them, and probably the most crucial, are the atmospheric conditions. Indeed, CTA will use the atmosphere as a giant calorimeter, i.e. as part of the detector. The Southern Hemisphere presents mainly four candidate sites: one in Namibia, one in Chile and two in Argentina. Using atmospheric tools already validated in other air shower experiments, the purpose of this work is to complete studies aiming to choose the site with the best quality for the atmosphere. Three strong requirements are checked: the cloud cover and the frequency of clear skies, the wind speed and the backward trajectories of air masses travelling above the sites and directly linked to the aerosol concentrations. It was found, that the Namibian site is favoured, and one site in Argentina is clearly not suited. Atmospheric measurements at these sites will be performed in the coming months and will help with the selection of a CTA site.Comment: 4 pages, 4 figures, ECRS'12 - 23rd European Cosmic Ray Symposium (July, 3-7, 2012) at Mosco