dataesr/scientific-paragraphs-categorization huggingface dataset:
Parallel implementation Most interval-analysis-based algorithms are appropriate for a distributed implementation, since processing a given box does not generally depend on processing of the other boxes in the list. A master computer manages the list and it sends a box to a slave computer. The slave executes the algorithm, by performing a few bisections. Then, it returns the remaining boxes to the master, and it waits for a new box to process. Such a scheme may be easily implemented in a network of workstations. The decrease of computation time will be, in general, less than proportional to the number of slaves, due to the overhead of data transmission between the master and the slaves. This approach may also take advantage of modern multi-core CPU architectures. By following this scheme and by using POSIX thread libraries, a distributed implementation of the DGP code was set up on a single workstation with a multi-core CPU.
Acknowledgments We are indebted to AndreFasel and  Emanuel Gerber  ( Natural History Museum of Fribourg ) for the funding of this project by the  Natural History Museum of Fribourg. Our deep gratitude goes to our colleague and friend  Prof. Jean-Pierre Berger , who highly contributed to the study of this locality by supervising  Bachelor student (Noemie Philipona)  and funding a part of the research by the  University of Fribourg. Mennecart Bastien  received support from the  Swiss National Science Foundation  (project  200021-115995 ,  200021-126420 , and  PBFRP2_145900 ) and  Chiara Angelone  received support by the  Spanish Ministerio de Economı ´a y Competitividad  ( CGL2011-28681 ). We are particularly grateful to  Loı ¨c Costeur (Naturhistorisches Museum Basel) ,  Marguerite Hugueney  ( University of Lyon 1 ),  Damien Becker  ( Jurassica Museum , Porrentruy),  Florent Hiard , and  Laureline Scherler , who provided access to collections and helpful discussions.
We associate an array processing method, called progressive multi-channel correlation (PMCC), and statistical modelling, to detect and classify seismic events. PMCC detects any coherent wavefront crossing an array of seismometers, including the wavefronts not generated by actual seismic events. We use machine learning techniques to classify the PMCC detections between "events" and "noise". These techniques are based on the statistical modelling of features extracted from the seismic signal. The features we select combine features computed directly from the raw signal and features retrieved by the PMCC detector. We apply our method on a real data set from the Songino seismic station, in Mongolia. We compare the performance of fours classifiers: Gaussian naive Bayes classifier, logistic regression, Gaussian mixture models (GMM), and hidden Markov models (HMM).
Hence, it is necessary to discriminate the detections of events and the detections not related to an event, referred to as "noise". For that purpose, we transform the seismic signal using an adapted feature representation, then we train statistical models to classify the features between "event" and "noise". We compare the performance of four machine learning classification techniques: Gaussian naive Bayes classifier, logistic regression, Gaussian mixture models, hidden Markov models. The outline of the paper is as follows. In Section 2, we describe the PMCC algorithm. In Section 3, we present the feature representation and the statistical approach that we use to classify detections as events or noise. We apply our method in Section 4 on a real dataset from the Songino seismic station in Mongolia.
Feature extraction The raw data are seismic multi-channel signal segments that have triggered PMCC detections. To enhance relevant information with respect to our classification problem, we extract feature vectors from this raw data  [2]. We select features computed from the seismic signal (standard features), and features retrieved by the PMCC detector (PMCC features). For the standard features, the signal from each channel of the array is filtered in three overlapping frequency bands: low frequency band [0. 8, 3], middle frequency band  [1. 5, 6]   [3, 9]  (in Hz). For each channel, three standard features are computed after filtering in each frequency band: kurtosis, relative spectral energy, envelope variation (see Table  1 ). The feature values are then averaged over the channels, yielding nine standard features.
CASE STUDY As a case study, we consider the Songino seismic station in Mongolia, which belongs to the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organization 2. To built the training data and to assess the classification performance, we use as ground truth a seismic bulletin provided by analysts from the Research Center of Astrophysics and Geophysics (RCAG) of the Mongolian Academy of Sciences. The bulletin contains the P and S-wave arrival time pickings. Signal segments corresponding to PMCC detections from August 25th to August 31st 2008 (seven full days) are extracted and transformed into feature vectors to constitute the training data. If the detection matches a seismic wave arrival from the bulletin, it is associated with the class "event". Otherwise, it is associated with the class "noise".
Training data are made of 8816 PMCC detections, among which 7721 correspond to noise and only 1095 to events. The class population size is thus highly uneven (seven times more noise than events), which can lead to poor classification performance  [5]. The noise class is therefore undersampled to get the same size than the event class. For the training data, the extraction of signal segments starts 3s before the arrival time picking and ends 10s after, so that the extracted segment are 13s long. To test the four classifiers, we use data from September 1st to September 2nd 2008 (two full days), which were not used for training. Test data are made of 3916 PMCC detections (3664 for noise, 252 for events). These detections are classified as event or noise, and the estimated class is compared with the actual class thanks to the seismic bulletin.
CONCLUSION PMCC is an array processing method, which detects coherent wavefronts crossing a seismometer array. PMCC also triggers a detection for wavefronts not generated by actual seismic events, hence the need to distinguish detections triggered by actual events from detections not related to an event. We use machine learning techniques to perform this classification task. These techniques are based on the statistical modelling of features extracted from the raw seismic signal. The features we select are computed directly from the signal (standard features) or retrieved by the PMCC detector (PMCC features). We compare four classifiers: Gaussian naive Bayes classifier, logistic regression, GMM, and HMM. This comparison is performed with a real data set from the Songino seismic station, in Mongolia.
(2015). Water bodies and snow surfaces were identified as well. The absolute location accuracy is better than 0. 4 pixel, i. e. 3. 2 m  (Baillarin et al. 2008). Over the 36 images collected between March and October, 31 images were cloudless, with some gaps (less than 2 weeks) due to the presence of clouds: from March 12 th to April 2 nd , April 14 th to May 14 th , and after August 22 nd. where ρ are the Formosat-2 reflectances in band 3 (Red) and band 4 (NIR). The pyranometer measurements were associated to Formosat-2 data aggregated over a 32×32 m² area (4×4 pixels). It should be noticed here that Eq. (  1 ) relates the blue-sky albedo that depends on the atmosphere diffuse fraction, to atmospherically corrected reflectance.
Albedo estimates from Formosat-2 images However, the top of canopy -reflectance -blue-sky albedo relationship was calibrated by  Bsaibes et al. (2009)  using 30 different dates providing very good performances (RMSER of 7. 5% and negligible bias). This indicates that, for this study the impact of diffuse fraction, and thus atmospheric conditions, is low. These evaluation results were comparable to calibration residual errors reported by  Liang et al. (1999) ,  Weiss et al. (1999)  and  Jacob et al. (2002b) , and were close to relative accuracy of albedo measurements with the pyranometers and Formosat-2 corrected data (around 5%). Far from providing a generic and robust mean of estimating albedo using Formosat-2 data, the limitation of estimating albedo following Eq. (  1 ) lies in their application to our study region and retrieval period.
7˚ to 51. 1˚). The optical depth τ at 550 nm estimated by  Hagolle et al. (2015)  for atmospheric correction of Formosat-2 images was compared with that retrieved from the following 3 sources, depending on their availability following this order (Fig. 2 ): -For 14 dates, Aerosol Robotic Network (AERONET;  Holben et al. (1998) ) observations from 'La Crau' station located at the center of the study area and at about 15 km East of pyranometers location (see Fig. 1 ). -For 8 dates, AERONET observations from the 'Avignon' station located at about 33 km North of pyranometers location. -For the remaining 9 dates, MODIS Aerosol data product MOD04_L2 closest in time to 10:30 UTC (no data were available on product MYD04_L2). We considered only the best quality data by selecting a QA confidence flag of 3.
Estimating the equivalent PSF of MODIS albedo using Formosat-2 data To reduce the computational time for the PSF assessment and correct possible change in spatial resolution of Formosat-2 data for being targeted off-nadir, Formosat-2 albedo pixels were aggregated by 5×5 pixels to provide a 40 m resolution cell. Besides, since the method requires no missing data, images were cropped (remaining of about 15×30 km 2 , plotted in Fig. 1 ), and a specific processing over cloud and cloud shadow pixels was applied. Similarly to the strategy followed to produce the MODIS albedo, based on a 16-day compositing, we assumed that albedo was almost steady during a short period of few days. The albedo value of cloud and cloud shadow pixels was set to the Formosat-2 albedo value of the same pixels at the closest clear date (e.
MODIS albedo evaluation Because urban and water land covers were neither used for the calibration of the regression Eq. (  2 ), nor for its evaluation, we excluded MODIS pixels containing more than 50% of cloud, cloud shadows, urban or water areas (e. g. , of about 9% of 1-km pixels, mostly located on the Eastern part of the image). The MODIS product quality flag was also used to keep only MODIS albedo data of best quality. To further analyze the impact of land cover on the evaluation results, a set of pixels characterized by a predominant land cover type were selected. The composition of these pixels in terms of land cover type was computed without considering boundary pixels within the PSF footprint: the variation of the PSF size between days would imply too much complexity for this analysis.
Nevertheless, the weights associated to these pixels are very low and correspond to the tail of the Gaussian function. Three metrics were considered to quantify the deviation between both datasets: the bias, the absolute (RMSEA) and the relative (RMSER) Root Mean Square Errors, used to quantify the accuracy, the absolute uncertainty and the relative uncertainty, respectively  (Vermote and Kotchenova 2008) : ∑ (7) ∑ (8) 100 (9).
With 6662 pixels used for the comparison, MCD43D yielded an albedo uncertainty of 0. 007 (4. 0%), with no bias. Albedo estimates from dry pastures, irrigated pastures or orchards were accurate (<0. 003), with low uncertainty (<0. 006; <3. 8%). On the contrary, albedo estimates from rice and marshes were less accurate (<0. 011) and with a higher uncertainty (<0. 015; <10. 7%). These discrepancies were attributed to the lack of water sensitive shortwave infrared spectral bands within the Formosat-2 configuration. The inter-comparison displayed as well a good overall temporal consistency. The variability exhibited by Formosat-2 data was a little larger. The method used in this study is sensitive to the heterogeneity of the area, with the constraint that a correct characterization of the PSF would not be possible on a homogeneous area.
Acknowledgements The Formosat-2 images used in this paper are ©NSPO (2006), distributed by  SPOT Image S. A. All rights reserved. We are grateful to  Prof. Crystal Schaaf  ( University of Massachusetts, Boston ) for providing us with the MCD43D V006 data that are yet to be released, as well as for the valuable discussions with her and  Dr. Qingsong Sun  ( Boston University ). We would also like to thank the farmers for allowing us to make use of their fields for in situ measurements. We kindly thank the three anonymous reviewers for their valuable comments and suggestions that have significantly improved this article. This work was made through different projects funded by the  Centre National d'Études Spatiales, CNES (TOSCA)  and the  European SIRRIMED  ( Sustainable use of irrigation water in the Mediterranean )  FP7 project.
Cardiac echographic features may be associated with diabetes and most often with older age, male gender, higher body mass index (BMI), hypertension, renal dysfunction, or other metabolic parameters such as dyslipidemia  [2, 3, 12, 13]. Although several recent studies have confirmed higher prevalence and severity of heart failure in diabetic than in nondiabetic patients  [14] [15] [16] , the specificity and the importance of diabetic cardiomyopathy are still debated  [17, 18]. Here, we report the prevalence of LV mass and function abnormalities on echocardiography and their determinants in a retrospective series of 656 asymptomatic diabetic patients referred for cardiac and vascular complications screening. Patients were then sorted out according to the presence of hypertension and silent CAD and data on the group without CAD and without hypertension were analysed in order to characterize the determinants of diabetic cardiomyopathy in the absence of confounding factors.
2. 1. Patients. We collected the data of type 2 diabetic inpatients of the Department of Endocrinology-Diabetology-Nutrition, Jean Verdier Hospital (Bondy, France), between 1991 and 2008. Criteria for inclusion in this study were normal 12-lead resting ECG and the presence of at least one of the following additional cardiovascular risk factors: dyslipidemia (serum total cholesterol > 6. 5 mmol/L, or triglycerides > 2. 3 mmol/L, or lipid-lowering treatment), hypertension (blood pressure ≥ 140/90 mmHg or antihypertensive treatment), smoking, microalbuminuria (albumin excretion rate > 30 mg/day on at least two assessments), family history of premature CAD (before the age of 60 in first-degree relatives), proximal peripheral (stenosis ≥ 50% on femoral or popliteal arteries), or carotid (stenosis ≥ 50% on extracranial carotid artery) occlusive arterial disease detected by ultrasound examination.
Screening for Silent Myocardial Ischemia and CAD. Each patient underwent a thallium-201 myocardial scintigraphy after an ECG stress test and/or a pharmacological stress test (dipyridamole injection). The ECG stress test protocol was previously reported  [20]. Briefly, the ECG stress test was performed according to the modified Bruce protocol. Single photon emission computed tomography (SPECT) gated acquisition was carried out with early images, that is, peak exercise or 4 min after a dipyridamole injection if the exercise test was not contributive (when the patient was unable to reach 85% of the maximal predicted heart rate (220-age) or when the ECG was indeterminate) and with delayed images (4. 0 ± 0. 5 hours later). The perfusion pattern (normal or showing stable or reversible defects) and ECG data (considered as positive if 1 mm flat or down-sloping ST segment occurred at 0.
08 s after the J point with or without angina pectoris) were assessed by a nuclear medicine physician and a cardiologist, respectively, who were unaware of the clinical or ECG data and of the imaging data. SMI was defined as abnormal results of the ECG stress test and/or myocardial scintigraphy imaging. Patients with SMI underwent a selective coronary angiography within 2 months after the noninvasive investigation. CAD was defined as a ≥70% narrowing of the luminal diameter in either the left anterior descending artery, the circumflex artery, the well-developed marginal vessel, or the right coronary artery or a ≥50% diameter narrowing of the left main coronary artery. The percentage of narrowing was visually determined by the consensus of two experienced investigators. In case of discrepancy between the two investigators, automatic quantification was used.
Statistical Analyses. Data are summarized as means ± SD for continuous variables, number of cases, and percentages for qualitative variables. Differences between groups were assessed by ANOVA tests or by Mann Whitney test for continuous variables. The Chi square test or the exact Fisher test was used for qualitative variables. To determine the significant independent predictors of echocardiographic abnormalities, stepwise logistic regression analyses were performed with the variables/parameters that were associated with each echocardiographic feature in univariate analysis with  < 0. 1. Statistical analyses were carried out using SPSS software (SPSS, Chicago, IL). The 0. 05 probability level was used for statistical significance.
Patients Characteristics. A total of 656 type 2 diabetic patients having been screened for SMI with myocardial scintigraphy and having interpretable echographic data were included. Of them, 483 (73. 6%) had hypertension and SMI was diagnosed in 206 (31. 4%) patients. Out of these 206 patients, 189 subsequently underwent a coronary angiography. Among them, 71 (11. 1%) had significant CAD. Thus, 157 patients had neither CAD nor hypertension (Table  1 ). Compared to patients who had hypertension or CAD or both, those without hypertension and CAD were younger and had shorter diabetes duration, lower BMI, and higher total and LDL cholesterol levels, maybe related to a lower prevalence of lipid-lowering treatment (odds ratio (OR) 0. 49 [95% confidence interval 0. 33-0. 71]) (Table  1 ). By definition, they were less likely to have antihypertensive treatment and SMI.
Echographic Abnormalities. LV hypertrophy, dilation, systolic dysfunction, and hypokinesia could be assessed in 566, 584, 548, and 603 patients, respectively. Missing data were due to poor echogenicity or segmental hypokinesia, which did not allow a reliable evaluation of the ejection fraction with Teicholz method. Echocardiographic examination showed LV hypertrophy in 181 patients (31. 9%), LV dilation in 43 (7. 3%), systolic dysfunction in 21 (3. 8%), and hypokinesia in 49 (8. 1%) (Table  2 ). Figure  1  shows the prevalence of echographic disorders in four exclusive groups of patients defined according to the presence or the absence of CAD and/or hypertension. Kinds of prevalence of LV hypertrophy, dilation, systolic dysfunction, and hypokinesia were the highest in patients with CAD and hypertension and were, respectively, 46.
Study Limitations. The data collected in this paper concern a cohort that was established since the 1990s, implying that only conventional measurements were complete for the whole population. For example, the Teicholz method was the only available method to determine ejection fraction for some patients, especially before year 2000, although it is not recommended any more. Furthermore, complete diastolic parameters including tissue Doppler imaging measurements are lacking. At the present time, myocardial function examination needs the regional myocardial function assessment. The low prevalence of LV dilation, systolic dysfunction, or focal hypokinesia may have also weakened the statistical analysis. We have only explored inpatients with at least one risk factor in addition to diabetes; therefore, the results are not necessarily generalizable to the diabetic population.
Acknowledgments The authors thank  Dr. F. Paycha ,  AP-HP, Louis Mourier Hospital, Colombes , and  Professor P. Weinmann ,  AP-HP, Avicenne Hospital, Bobigny , for isotopic explorations and  Dr. S. Cattan ,  Centre Hospitalier Intercommunal, Le Raincy-Montfermeil, France , for coronary angiographic explorations.
lsce. ipsl. fr/. The simulations used in this study are those which were available on the CMIP5 database as of October 2012. In addition to the CMIP5-PMIP3 atmosphere-ocean coupled simulations we are using pre-industrial and LGM simulation results obtained from the AGCM (Atmospheric General Circulation Model) ECHAM5  (Roeckner et al. , 2006)  enhanced with stable water isotope diagnostics  (Werner et al. , 2011). For the LGM climate simulation, PMIP3conform boundary conditions have been applied. Glacial sea surface temperatures and sea ice coverage have been derived from the GLAMAP Atlantic reconstruction data set  (Schäfer-Neth and Paul, 2003). Both the PI and LGM simulation have been performed with a fine T106L31 spectral model resolution (horizontal grid box size of approx. 1. 1 • × 1. 1 • , 31 vertical levels).
All CMIP5-PMIP3 simulations are summarized in Table  1. From the different simulations we have used the following variables: tas (near surface air temperature) and pr (precipitation rate). As the sublimation rate was only available for 3 models out of 11 and as its values over the sites of interest were negligible compared to the precipitation rate in these models, we have not included the sublimation rate in the calculation of the accumulation rate changes from LGM to PI. We observe that tas is systematically higher than the measured mean atmospheric temperature, which is a typical bias of CMIP5-PMIP3 simulations in polar regions (see  Risi et al. , 2010). To evaluate the consequences of this bias, we have extracted the modelled inversion temperatures available in the IPSL-CM5A-LR model. These data show that, in the models, the slope of the relationship between tas and the modelled inversion temperature is 15 % higher than the one given by  Jouzel and Merlivat (1984)  (cf.
Acknowledgements. We acknowledge  F. Parrenin ,  L. Sime  and one anonymous referee for their useful comments, which helped to improve this manuscript. This work is a contribution to the European Project  for Ice Coring in Antarctica (EPICA ), a joint  European Science Foundation/European Commission (EC)  scientific programme, funded by the  EC  and by national contributions from  Belgium, Denmark, France ,  Germany, Italy , the  Netherlands, Norway, Sweden, Switzerland  and the  UK. The main logistic support was provided by  IPEV  and  PNRA. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP and the  Paleoclimate Modelling Intercomparison Project (PMIP). We thank the climate modelling groups (listed in Table 1 of this paper) for producing and making available their model output.
For CMIP, the  US Department of Energy 's  Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the  Global Organization for Earth System Science Portals. This work was funded by the  French ANR  project  Dome A and  through  ERC  grant  COMBINISO  (project no. 306045 ). The  ASTER AMS national facility  ( CEREGE, Aix en Provence ) is supported by the  INSU/CNRS , and the  ANR  through the  "Projets thématiques d'excellence" programme for the "Equipements d'Excellence" ASTER-CEREGE action ,  IRD , and CEA. Edited by:  E. Wolff.