In Situ Statistical Analysis for Parametric Studies

International audienceIn situ processing proposes to reduce storage needs and I/O traffic by processing results of parallel simulations as soon as they are available in the memory of the compute processes. We focus here on computing in situ statistics on the results of N simulations from a parametric study. The classical approach consists in running various instances of the same simulation with different values of input parameters. Results are then saved to disks and statistics are computed post mortem, leading to very I/O intensive applications. Our solution is to develop Melissa, an in situ library running on staging nodes as a parallel server. When starting, simulations connect to Melissa and send the results of each time step to Melissa as soon as they are available. Melissa implements iterative versions of classical statistical operations, enabling to update results as soon as a new time step from a simulation is available. Once all statistics ar updated, the time step can be discarded. We also discuss two different approaches for scheduling simulation runs: the jobs-in-job and the multi-jobs approaches. Experiments run instances of the Computational Fluid Dynamics Open Source solver Code_Saturne. They confirm that our approach enables one to avoid storing simulation results to disk or in memory

Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin

Groundwater depletion has been one of the major challenges in recent years. Analysis of groundwater levels can be beneficial for groundwater management. The National Aeronautics and Space Administration’s twin satellite, Gravity Recovery and Climate Experiment (GRACE), serves in monitoring terrestrial water storage. Increasing freshwater demand amidst recent drought (2000–2014) posed a significant groundwater level decline within the Colorado River Basin (CRB). In the current study, a non-parametric technique was utilized to analyze historical groundwater variability. Additionally, a stochastic Autoregressive Integrated Moving Average (ARIMA) model was developed and tested to forecast the GRACE-derived groundwater anomalies within the CRB. The ARIMA model was trained with the GRACE data from January 2003 to December of 2013 and validated with GRACE data from January 2014 to December of 2016. Groundwater anomaly from January 2017 to December of 2019 was forecasted with the tested model. Autocorrelation and partial autocorrelation plots were drawn to identify and construct the seasonal ARIMA models. ARIMA order for each grid was evaluated based on Akaike’s and Bayesian information criterion. The error analysis showed the reasonable numerical accuracy of selected seasonal ARIMA models. The proposed models can be used to forecast groundwater variability for sustainable groundwater planning and management

Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules.

109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen's Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT

Influence of experimental conditions on sound pleasantness evaluations

ICA 2016, 22nd International Congress on Acoustics, BUENOS AIRES, ARGENTINE, 05-/09/2016 - 09/09/2016Being able to characterize and estimate the urban sound perception is a key point to improve the city dwellers environmental quality. In the past decade, various studies have focused on collecting perceived global sound pleasantness at specific locations. Some of them were carried out on field in order to evaluate the soundscape perception of the participants directly in their context. Other studies were realized in laboratory to better control the stimuli and to increase the number of participants who were subjected to the same sound environment. Most of the laboratory experiments are done in large or semi-anechoic chamber with calibrated and highly realistic audio reproduction in order to respect the ecological validity of the experiment. On one hand, even with a high immersive level, the laboratory context is not as rich as the field context and the two types of experiment could lead to different results. On the other hand, few studies exist showing the influence of decreasing ecological validity for the same experience. This work presents a short statistical analysis of perceptive evaluations of ten urban locations under 4 different test conditions. First, evaluations are carried out directly in-situ in the city of Paris. Then audio-visual recordings of these locations are evaluated in three different experimental conditions: (i) in a well-controlled acoustic laboratory in Paris region with French people, (ii) in an acoustic laboratory in Buenos Aires with Argentinean participants and lowest immersive conditions, (iii) in a habitational room with Argentinean participants and subjective calibration. The study reveals that both the 'country' factor and the experimental conditions in laboratory do not show any significant impact on the perceived sound pleasantness and perceived loudness assessments

Intercalibration of the barrel electromagnetic calorimeter of the CMS experiment at start-up

Calibration of the relative response of the individual channels of the barrel electromagnetic calorimeter of the CMS detector was accomplished, before installation, with cosmic ray muons and test beams. One fourth of the calorimeter was exposed to a beam of high energy electrons and the relative calibration of the channels, the intercalibration, was found to be reproducible to a precision of about 0.3%. Additionally, data were collected with cosmic rays for the entire ECAL barrel during the commissioning phase. By comparing the intercalibration constants obtained with the electron beam data with those from the cosmic ray data, it is demonstrated that the latter provide an intercalibration precision of 1.5% over most of the barrel ECAL. The best intercalibration precision is expected to come from the analysis of events collected in situ during the LHC operation. Using data collected with both electrons and pion beams, several aspects of the intercalibration procedures based on electrons or neutral pions were investigated

Effect of Exercise on Photoperiod-Regulated Hypothalamic Gene Expression and Peripheral Hormones in the Seasonal Dwarf Hamster Phodopus sungorus

Acknowledgments: Many thanks to Dana Wilson, Susan Hay, David Brown and Vivienne Buchan at RINH, Siegrid Hilken and Esther Lipokatic-Takacs at UVMH for the excellent technical support and advice provided. Many thanks are due to Claus Mayer of Biomathematics, Statistics Scotland for assistance with the statistical analysis of data. Author Contributions: Conceived and designed the experiments: IP SS FS PB. Performed the experiments: IP RD FS. Analyzed the data: IP RD FS SS PB. Wrote the paper: PB SS FS IP.Peer reviewedPublisher PD