dataesr/scientific-paragraphs-categorization huggingface dataset:
The uncertainty of the tagging e ciency The statistical uncertainty on the tagging e ciency depends only on the sample size, in this case the number of simulated clusters n and is given by = r p t (1 p t ) n. ( 5. 1) For this study n = 10000, giving an statistical uncertainty of ±0. 5% However, as seen in figure  10  the simulation does not completely reproduce the data, leaving some discrepancy between data and simulation. To get a better idea of the value of the tagging e ciency and how accurate this value is, the tagging e ciency was also calculated by a mainly data-driven method. In the main data sample the number of correctly tagged annihilation clusters is given by where N t is the number of tagged clusters, N is the total number of clusters, f is the fraction of secondary clusters in the sample, and p f is again the false tagging rate.
Acknowledgments This project was financed by the  Research Council of Norway  through the  FRINATEK  and  CERN  related research project. We thank the  Timepix Collaboration  for providing their detector at our disposal for the duration of the tests, as well as for their help and support during this work. This work was supported by the  European Research Council , the  European Social Fund , the  Istituto Nazionale di Fisica Nucleare , the  Austrian Ministry for Science, Research and Economy , the  German Research Foundation , the  Research Council of Norway , the  Bergen Research Foundation , the  Russian Ministry of Education and Science , the  Russian Academy of Sciences  and the  John Templeton Foundation. This research project has been supported by a  Marie Sk≥odowska-Curie Innovative Training Network Fellowship  of the  European Commission 's  Horizon 2020 Programme  under contract number  721559 AVA.
In France, a database which repertory the historical earthquake  [30] [31]  is filled, but the information related to the damages are quite thin. Some other databases exist, developed by other countries, or within framework of some European Commission projects, but their accessibility, especially to original "raw" data, is fastidious. [32] systematically compared statistical modelling techniques for different empirical datasets and explored many of the issues raised regarding the treatment of uncertainty. Database typologies and their typical issues depend on the manner on which the observations were obtained they were been obtained: for Detailed "Engineering" Surveys and Surveys by Reconnaissance Teams the main issue is the possibility of unrepresentative samples; for the Rapid Surveys methods, generally the evaluation concern the habitability or the safety not the evaluation of the damage; while for remotely sensed survey method, which is quite new and not already able to efficiently evaluate other damage degrees than the collapse or very heavy damage  [33].
Historical Case Study and Available Observed Data on Damages A retro-scenario of the October 10, 1980 El Asnam earthquake was performed by the authors  [34]  [35] using Armagedom tools  [13]  developed by the French Geological Survey (BRGM). In this paper, we are just using the intensity and damage observations in order to find the vulnerability index that will give the smaller error between the observed damage and the damages calculated numerically with Armagedom software. Chlef city (formerly named El Asnam) situated 200 km west of the capital of Algeria Algiers, is an area that suffers of important seismic activities due to the interaction of the two Eurasian and African plates. Major earthquakes rocked the city during the last century, the last event that caused major damages being the one of October 10, 1980; known as El Asnam earthquake and killed around 3000 persons.
Model Description Figure  2  schematize the general methodology for seismic damage calculation. It presents each of the most important phases necessary for creating an earthquake scenario: regional seismic hazard, local seismic hazard, etc. In the case of El Asnam, the hazard modules (module 1, 2 and 3) are not performed, since we got the observed macroseismic intensity. Here all performed simulations (9800 runs) were executed on the Armagedom tools  [13]  developed by the French Geological Survey (BRGM) and used for simulation of the damage scenarios. For damage assessment, in Armagedom software the procedure developed within RISK-UE is used  [39]. Firstly, the vulnerability to earthquake shaking of exposed elements at risk (for this study, buildings) are characterized by vulnerability indices (V i ), which range from zero (not vulnerable) to one (building is highly vulnerable).
Therefore, to run the simulation model (Armagedom software) we need to describe: • the total number of typologies; • the vulnerability indices (V i ) and T beta parameters for each typology; • the spatial distribution of the typologies in each district (polygon) of the affected area vulnerability (nbBat). In Armagedom software, these parameters are stored in file (*. txt) as follows: • the V i and T beta are regrouped in the same file (Figure  3 ) called *. tvi_t. The file is structured in four columns; • type of building as character, vulnerability index as float, T beta parameters as float and period as float. Each line represents a specific typology; • the nbBat are stocked in another file (Figure  3 ) called *. nbbat. This file is given as table in which, each line represents the area, the number of columns represent the number of typology and the value the number of buildings.
The proposed methodology was implemented in R programming language and environment  [43]  using the SOLNP algorithm available in "Rsolnp" package  [44]. This process was repeated 100 times to generate 100 values of V i , T beta and nbBat values. These solutions represent potential solution, which will be analyzed by the expert in order to select the most appropriate one. We will show in the next section, that all the 100 solutions present a very little difference. The algorithmic parameters used are as follows: population size = 50, generations = 200, crossover fraction = 0. 8, and stall generations = 100. In fitting convex function  For each observed damage grade, we compute the misfit error between observed and estimated damage (Equation (  5 )). In order to have a uniform distributed damage grade error for all polygons we define for each damage grade the entity defined in Equation  (6).
Conclusions We have aimed in this work to develop unified computational method for assessment of vulnerability of structures, to be used for seismic risk scenarios at the town scale, when the underlying uncertainties are modelled using random variables, interval analysis, and (or) fuzzy variables. The developed approach is based on the minimization of the error between observed and estimated damages and it allows the determination of the number of typologies classes, the estimation of their vulnerability index and associated T beta value as well as the spatial distribution in the studied area (nbBat). This method remains general and can be applied for any observed dataset, for which generally the information related to the typologies and their vulnerability are not available. The main insights after the analysis of the results are: • The Risk-EU methodology and the model that it proposed fit very well the damages D4 to D5.
The baseline task consisted of five tilted slashes that were equally oriented (target; e. g. , /////) or not (nontarget; e. g. , /////). Participants were asked to give a yesresponse in case a target was presented and a noresponse in case of a non-target. This task was used to assess non-linguistic visual pattern recognition as well as to capture basic decision processes related to yes/ no-responses. Functional data were acquired using a blocked design. Ten targets and 10 non-targets of the same task condition were presented during each block. At the beginning of each block, a cue-screen (4 s) informed the participants about the upcoming task. This was followed by the jittered presentation of a fixation cross with a mean duration of 6 s (range 3-9 s). The subsequently shown experimental trials consisted of a fixation cross (1 s) and the stimulus (2 s).
MRI data acquisition Functional and structural imaging was performed with a 3. 0 T Siemens Magnetom TimTrio MRI scanner (Siemens, Erlangen, Germany) using a 12-channel head coil. During the four runs, a total of 932 volumes were recorded applying a T2 * -weighted, gradient echo planar imaging (EPI) pulse sequence (TE = 30 ms, TR = 200 0 ms, FA = 70°, number of slices = 37, in-plane resolu tion = 3 Â 3 Â 3 mm 3 , 64 Â 64 data acquisition matrix, FoV = 192 Â 192 mm 2 ). After recording the functional images, a high-resolution 3D T1-weighted anatomical scan was additionally acquired for each participant (176 sagittal sections, 1 Â 1 Â 1 mm 3 , 256 Â 256 data acquisition matrix). To reduce head movements, foam padding was placed around the participants' heads. During functional scanning, images were visually checked for severe motion.
Data analysis Mean response times and accuracy were analyzed using R version 3. 3. 0 (R Core  Team, 2016). We chose a mixedeffects modeling approach  (Baayen et al. , 2008)  as implemented in the lme4-package  (Bates et al. , 2014)  with crossed random factors for subjects and items. Prior to analyzing RTs by means of linear mixed-effects regressions, including main effects and interactions for task and age as fixed factors, outliers deviating more than 3. 5 SDs from the individual's mean within each task Â sti mulus type experimental cell were removed (0. 4% of the data). Accuracy data were analyzed using logistic mixed-effects regressions, type III Wald chi-square tests to test for significant fixed effects (car-package;  Fox and Weisberg, 2011). We implemented intercepts for subjects and items, and random slopes for age within items and random slopes for task within subjects into the random factor structure  (Barr, 2013; Barr et al.
, 2013). SPM12 software (revision 6685; Statistical Parametric Mapping, Wellcome Department of Imaging Neuroscience, University College London, UK, 2014) running in a Matlab 2016a (Mathworks Inc. , Natick, MA, USA) environment was used for preprocessing and statistically analyzing MRI data. Preprocessing of functional images included slice time correction, realignment to the mean image, co-registration to the individual T1-weighted anatomical scans and spatial normalization of structural and functional images to MNI space. Spatial normalization was achieved by first segmenting the co-registered anatomical images into gray matter, white matter, CSF, bone, non-brain soft tissue and air/background  (Ashburner and Friston, 2005). We then created a study-specific template by means of the DARTEL algorithm  (Ashburner, 2007)  and subsequently estimated the transformation from this template to MNI space.
Acknowledgments-We would like to thank  Jana Lu¨dtke  and  Teresa Sylvester  for their thoughtful advice and discussion of ideas presented in this paper as well as  Felix Blankenburg  for his advice on analyzing the data. We also thank our student assistants of the  Department of Experimental and Neurocognitive Psychology at the Freie Universita¨t Berlin  and the recruiting office of the  Max Planck Institute for Human Development, Berlin , for recruiting subjects and collecting the data.
All simulations used hourly local meteorological 25 Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-308 Manuscript under review for journal Biogeosciences Discussion started: 18 July 2018 c Author(s) 2018. CC BY 4. 0 License. forcing. Each site corresponds to one of the major soil texture classes according to USDA soil classification (Fig. 2  and Table  1 ). Secondly, regional historical simulations were performed at 1-degree spatial resolution over the Amazonian forest using up-scaled gridded climate forcing data from CRUNCEP, which combine monthly data from the Climate Research Unit (CRU) and 6-hourly fields from the National Center for Environmental Prediction (NCEP)  (Wei et al. , 2014)  (Table  1 ). All simulations started from a semi-analytical spin-up  (Lardy et al. , 2011)  to equilibrate carbon and hydrological variables by 5 recycling climate data from 1981 to 2000 under a constant CO 2 concentration set to 370 ppm.
Regional datasets To evaluate GPP patterns and seasonality at regional scale, we used the monthly global observation-based GPP model 5 FLUXCOM, running from 1981 to 2013 and produced at 0. 5° resolution using different methods by  Tramontana et al. , [2016]  and  Jung et al. , [2017]. We calculated the median of the following three methods, namely ANNs (artificial neural networks), RF (Random Forest) and MTE (Model Tree Ensemble) and chose the method proposed by  Lasslop et al. , [2010]  to retrieve GPP by fitting a respiration model to nighttime NEE values. All methods were highly consistent (not shown). Compared to the global network of flux-tower measurements, performances were reasonable in terms of annual mean and 10 spatial pattern representation (R 2 > 0. 7) and mean seasonal cycle (0.
67 < R 2 < 0. 77), but they showed a low predictive power for interannual variability  (Tramontana et al. , 2016). Also, GPP (and other fluxes) were better predicted in temperate climate sites than in the tropics due to a smaller amount of training data being available  (Tramontana et al. , 2016). For ET, we used the remotely sensed GLEAM v3. 1a product  [Martens et al. , 2017 and references within]  interpolated at 1-15 degree resolution from 1981 to 2016. To illustrate the uncertainties associated with this global dataset, GLEAMv3. 1 was compared to the ET measurement at K67 (M34) between  2000 and 2006 , Moreira et al. , [2018] ]  found a relatively strong bias of 0. 77 (0. 99) mm d -1 and low correlation -0. 08 (0. 32). Furthermore, we used a compilation of 413 ground inventories across Amazonia presented by  Mitchard et al.
1 ), in TRUNK the background tree mortality is very simply set as a constant fraction of the woody carbon pool, defined by a "residence time" parameter which is poorly constrained by observations  (Sitch et al. , 2003). Thus, it is possible to capture a realistic AGB with TRUNK by adjusting this residence time value, while in CAN-RS and CAN tuning the model to reproduce AGB is not trivial. Nevertheless, the three model versions  With the mechanistic root water uptake module implemented in CAN-RS version of ORCHIDEE, trees preferentially used water in the deepest soil layer during the dry season. This reduced the effective decrease in leaf gas exchange induced by water stress. Through this process, the model better captured the seasonality of GPP and LE. The effect was strongest for soil types with a constraining soil water retention curve (e.
g. , clay USDA soil type 12). The root water uptake model was successfully validated under severe drought conditions  (Fisher et al. , 2006)  and for other ecosystems (e. g. by  Williams et al. 20 (2001) ), and can therefore be extrapolated to other PFTs for global simulations using CAN-RS. CAN, like other DGVMs Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-308 Manuscript under review for journal Biogeosciences Discussion started: 18 July 2018 c Author(s) 2018. CC BY 4. 0 License. (Getirana et al. , 2014; Guimberteau et al. , 2014; Restrepo-Coupe et al. , 2016) , was unable to maintain LE and GPP fluxes during the dry season. A previous modelling study using TRUNK showed the importance of deep roots by optimizing soil depth parameters at 10 m for seasonal variation of fluxes in Amazonia  (Poulter et al.
Concluding remarks In this study, we demonstrated that a 2gDGVM (CAN-RS) can reproduce carbon and water fluxes and carbon stocks across 15 Amazonia, and we validated these simulations using local and regional observations. We also compared CAN-RS with earlier versions of the same family of models (ORCHIDEE-TRUNK and ORCHIDEE-CAN). The mechanistic root water uptake module implemented in CAN-RS allows trees to take up water in the deepest soil layer during dry seasons. This feature enables the model to capture the seasonality of GPP and LE, especially over the Guianan and Brazilian Shield (high water retention). The modelled diameter size distribution is improved compared to the TRUNK big-leaf DGVM. However 20 CAN-RS, by construction, still does not capture functional diversity, and behaves similarly to TRUNK.
The evaluation of the number of mouse ovarian primordial follicles (PMF) can provide important information about ovarian function, regulation of folliculogenesis or the impact of chemotherapy on fertility. This counting, usually performed by specialized operators, is a tedious, time-consuming but indispensable procedure. The development and increasing use of deep machine learning algorithms promise to speed up and improve this process. Here, we present a new methodology of automatically detecting and counting PMF, using convolutional neural networks driven by labelled datasets and a sliding window algorithm to select test data. Trained from a database of 9 millions of images extracted from mouse ovaries, and tested over two ovaries (3 millions of images to classify and 2 000 follicles to detect), the algorithm processes the digitized histological slides of a completed ovary in less than one minute, dividing the usual processing time by a factor of about 30.
Data extraction and preprocessing for automated follicle count. Using the software Pannoramic Viewer (3DHistech Ltd), the slides are extracted in a Tagged Image File Format (TIFF), suitable for analysis with usual deep learning tools. At the same time, the annotation coordinates from the manual follicle count were extracted with Calopix Viewer ®. The result of this extraction is, for each slide, an Excel file containing every PMF, and its coordinates on the picture. This file is essential for the training of the supervised algorithm as it is used to automatically isolate the follicle images within the slide. Raw images extracted with the collection process presented above can approximate 200 GB in size, limiting the analytical possibilities. To reduce the resolution, thus the size of the input files, two following approaches were applied: (i) Resolution reduction: A compromise on image quality can be done to obtain lighter images that still allow the model to give good performances; (ii) Color removal: each pixel of a colored image is a combination of 3 values for red, green and blue, when black & white, each pixel is represented by one unique value, considerably reducing the size of the initial image.
Technical details about this procedure are provided within the Supplementary Fig. S4. Hardware and software specifications. All the code was created in Python 3. The following modules were used for specific treatments: (i) Keras to build the CNN  26  : It is a high-level module that runs on top of Tensorflow  27  , the deep learning library developed by Google Brain. (ii) OpenCV to manipulate images during the preprocessing phase  28. It implements the contour detection algorithm and other specific functions for image manipulation. The code created for this project respects the best practices of Domain Driven Design  29  , aiming for a clear and well-structured code. The computer used to manipulate the images and to compute the CNN had a NVIDIA GeForce GTX 970 Graphical Processing Unit (GPU) with 1664 CUDA cores and 128 GB of RAM.
The GPU allows better computing time for image processing and analysis. It is to be noted that more recent GPU could easily improve the computing performances of the project. The software specifications for this work allow the code to be easily reran on another computer or server since it only uses open source tools, developed for multiple operating systems. It does not depend on a specific hardware but the use of a last generation GPU and 128 GB of RAM is advised for good performances and reasonable execution time. Evaluation design and protocol. Two metrics were chosen to evaluate the performances of the model. The first one is the recall, representing the percentage of follicles actually found with the algorithm. This number is excpected to be as close as one as possible, meaning the algorithm does not miss any follicle.
Results Manual follicle counting. Twenty-four mouse ovaries were evaluated in this pilot study. A total of 194 slides were captured with the Slide Scanner and 1454 ovarian sections were analyzed by a single observer using Calopix Viewer ®. All type of visualized follicles were classified according to Pedersen classification 16 (Fig. 1 ) and specif- ically annoted using Calopix Viewer annotation system. Click annotation extraction in Excel format allows to count every follicles. A total of 40488 follicles were manually counted. Among them 21335 PMF were identified. Thus, each ovary contained an average of 888. 9 ± 277. 6 PMF. The time estimated for PMF counting was approximately one hour for one slide namely about 7-8 hours for one ovary. Data extraction. Sixty-four slides containing 7-8 ovarian sections were extracted in TIFF image as represented in Fig.
The size of the frame is constant for all PMF and empirically calculated to make sure it always contains the PMF. Many negative examples, with the same frame size, were randomly picked in the cut images to balance the dataset. A last phase of data augmentation was conducted to increase the relevance of the dataset for feeding a detection and classification algorithm. It stands on the fact that some image distortions produces other relevant images for the study (namely, images that could have been observed too)  31. In our context only the following geometrical distorsions were applied to increase the dataset: rotations, image translation and horizontal and vertical image reversals.
Developing and training a deep learning approach. In the present study, 48 slides corresponding to 6 whole ovaries were used to develop and train the algoritm process. Follicle detection was defined as a two-stages process. First, a CNN was trained to classify whether an image contains a follicle or not, as illustrated in Fig. 4a. The training dataset contains small frames of both real follicles and random parts of the ovary section. Secondly, a sliding window was used to analyze the image and, for each image extracted by this way, predicted the presence of a follicle using the model previously trained. The window was the same size as the frames previously used to train the CNN (Fig. 4b ). Note that, in the prediction process, successive windows overlap by half to make sure that no follicle is missed due to position in the window (for example section between two windows or in an edge of the window).
=. precision number of correctly detected follicles number of detected follicles For this study, a relatively low threshold was chosen in order to keep a high recall, thus finding nearly all the actual PMF, but at the cost of many FP. A recall of 99. 46% was found with a precision of 11. 32%. This means that 1658 out of 1667 follicles were found whereas only one out of ten positive prediction from the CNN was a right one (nearly 89% of detected follicules were FP). A hard negative mining (HNM) methodology  33  was implemented in order to reduce the huge number of false positive. It consists in, after a first phase of training, adding false positive from a specific HNM dataset forced as negative examples in the training set and continuing with training. The negative examples of the training set are resampled to avoid training on unbalanced data.