dataesr/scientific-paragraphs-categorization huggingface dataset:
Reference values were calculated with the Monte-Carlo code TRIPOLI4 ®  [20]  by using the evaluated nuclear data library JEFF-3. 1. 1  [21]. They are compared to results obtained with JEFF-3. 1. 1 in which the TSL are replaced by those of the US library ENDF/B-VII. 1  [22]  and of the CAB library  [23] , produced at the atomic center of Bariloche. 2 Thermal scattering law for light water.
Processing of the TSL data files for TRIPOLI4 ® The Monte-Carlo code TRIPOLI4 ®  [20]  was used for the interpretation of the MISTRAL experiments. For this purpose, thermal scattering files of H in H 2 O were generated for each temperature step in a format compatible with the official nuclear data library of TRIPOLI4 ® based on JEFF-3. 1. 1. The processing of the TSL data files was performed with the NJOY code  [25]. Two modules of NJOY are specifically dedicated to this treatment. The LEAPR module calculates the S(a, b) tables by using the formalism briefly described in Section 2. 1. The THERMR module uses the S(a, b) tables for calculating the double differential inelastic crosssections (Eq. (  3 )). Figure  3  shows the flowchart representing the processing scheme applied to the TSL files of JEFF-3.
Beyond this energy, the static Assymptotic Kernel (AK) approximation is applied. The importance of the TSL depends on the size of the neutronic core. A small core yields a high thermal neutron leakage, so a high effect of the thermal neutron models is expected. In our case, the free gas model overestimates the experimental reactivity excess by approximately +800 pcm. Such a large difference confirms the importance of the thermal scattering laws and their processing with the LEAPR and THERMR modules of the NJOY code for a correct interpretation of the MISTRAL experiments. The two NJOY modules were tested separately. The THERMR module was applied to the S(a, b) tables given with the official TRIPOLI4 ® library. In order to test the compatibility of the LEAPR calculations, we used the input files for H in H 2 O reported by Mattes and Keinert in reference  [28].
The input file contains the model parameters listed in Table  2  and the continuous frequency spectra shown in Figure  1. As reported in Table  3 , the differences between the k eff values calculated with the TSL files coming from our processing scheme and the official library of ® code  [20]. The processing of the S(a, b) tables from the three TSL data files of interest for this work is performed with the NJOY code  [25]. The cross-sections of the JEFF-3. 1. 1 library is used for the neutron transport and only S(a, b) of light water are replaced by taking the needed information from alternatively the JEFF-3. 1. 1, ENDF/B-VII. 1 and CAB libraries. The CADTOOL package  [31]  provides an easy-to-use interface for automated sequential processing schemes.
Linear  [4] , quadratic  [19]  or cubic  [16]  polynomials are used in the literature: DrðT Þ ¼ a 0 þ X n i¼1 a i T i ; ð12Þ where the coefficients a i are free parameters. In the present work, a systematic study is reported as a function of the degree of the polynomial (n = 1, 2, 3). The fitting algorithm of the CONRAD code was used  [36]. A Chi-square test provides a measure of the goodness-of-fit. It was used to select the optimal degree of the polynomial. Table  5  reports the final Chi-square values provided by the CONRAD code for n = 1, 2, 3. The originality of our work is the simultaneous analysis of the Dr(T) values calculated for the MISTRAL-2 and MISTRAL-3 programs. The calculations were performed by introducing a free normalization factor which does not change the shape of Dr as a function of the temperature.
This approach aims to provide a global trend for the MOX configurations. For MISTRAL-1, a quadratic polynomial (n = 2) gives a rather good description of the Dr(T) results. A different trend is observed for MOX fuel. A simple linear fit (n = 1) of the MISTRAL-2 and MISTRAL-3 results provides better Chi-square values than a cubic polynomial. Best fit curves are reported in Figure  9. The corresponding polynomial coefficients a i are given in Table  6. The quoted uncertainties account for the statistical uncertainties in order to quantify the contribution of the Monte-Carlo calculations only. The propagation of the experimental uncertainties was already addressed in the frame of the previous analysis performed with the deterministic code APOLLO2  [16, 17].
Calculation errors on the reactivity temperature coefficient The temperature effect on the reactivity can be expressed by the temperature coefficient a iso , defined as the change in reactivity due to a change in temperature. The deviation from the experimental values is given by the derivative of equation (  12 ) with respect to the temperature: where Da cor is a correction factor introduced to account for the thermal expansion of the materials. Such a correction was applied in the previous interpretation of the MISTRAL-1 experiment with the deterministic code APOLLO2  [16]. In the present work, we decided to use a similar strategy for a better comparison of the MISTRAL-1 results. Da iso ðT Þ ¼ X n i¼1 ia i T iÀ1 þ Da cor ; ð13Þ Temperature variation produces a thermal expansion of the fuel pellet, clad and grid that will have an impact on the effective multiplication factor.
Unfortunately, no obvious explanations were found for understanding the differences. For MISTRAL-2 and MISTRAL-3, the calculated reactivity includes the thermal dilatation effects and no correction is needed (Da cor = 0). The calculation errors on RTC are summarized in Table  7. Results are averaged over broad temperature intervals between the temperature T 1 and T 2 : Da iso ¼ X n i¼1 a i T i 2 À T i 1 T 2 À T 1 þ Da cor : ð14Þ The present work provides the first interpretation of the RTC errors with the Monte-Carlo code TRIPOLI4 ®. Previous calculations were performed with the deterministic code APOLLO2. For the UOX configuration (MISTRAL-1), we observe differences of about 0. 8 pcm/°C between the APOLLO2 and TRIPOLI4 ® results. The origin of such a systematic bias is hard to explain, especially since a better agreement is achieved for the MOX configurations (MISTRAL-2 and MISTRAL-3).
Conclusions A 3D model of the EOLE reactor by using the TRIPOLI4 ® Monte-Carlo code was used for the first time to achieve the interpretation of the RTC experiments performed in the MISTRAL-1, MISTRAL-2 and MISTRAL-3 configurations as a function of the temperature. This approach has not only confirmed previous results established with the deterministic code APOLLO2 but also provided new integral trends in relation with the thermal scattering laws of hydrogen bound to H 2 O. The comparison of the excess of reactivity calculated with three different sets of thermal scattering laws (JEFF-3. 1. 1, ENDF/B-VII. 1 and CAB model) shows the impact of the intermolecular vibration modes of the water molecule in the neutron transport. The decrease of the translational mode in favor to the rotational mode leads to an increase of the calculated reactivity that can reach +180 pcm when the CAB model is used for the interpretation of the MISTRAL-2 configuration (MOX fuel).
Experimental Framework Table  2. 1 describes our experimental framework. Aggressive compiler options as -ffast-math are disabled to prevent the modification of the sensitive floatingpoint properties of these algorithms. Rounding intermediate results to the bi-nary64 format (53 bit mantissa) and value safe optimizations are provided with -fp-model double and -fp-model strict options. Runtimes are measured in cycles with the hardware counters thanks to the RDTSC assembly instruction. We display the minimum cycle measures over more than fifty runs for each data. Condition dependant data are computed with the dot product generator from  [9]. We compare our solutions to the well optimized but non-reproducible MKL BLAS implementation [6].
Implementation, Optimization and Test For a fair comparison, all algorithms are manually optimized by a best effort process. AVX vectorization, data prefetching and loop unrolling are carefully It is not a surprise that HybridSum and OnlineExact are interesting for larger size vectors. These algorithms produce one or two short vectors (length = 2048 in binary64) whose distillation is of constant time compared to the linear times of the data preprocessing step (exponent extraction) or also, of the successive error free transformations in the other algorithms. Moreover they are very less sensitive to the conditionning of the entry vector. Shorter size vectors benefit from the other algorithms, especially from FastAccSum while their conditionning remains small. In the following we take advantage of these different behaviors according to the size of the entry vector.
Acknowledgements The authors would like to thank  Maxime Cordy ,  Bernhard Aichernig , and the reviewers for their helpful comments, the  AGE (University of Namur ) and the Elsa organisation for providing logs of WordPress sites. This research was partly funded by  Walloon region  under the  INOGRAMS  project (n • 7171). Mike Papadakis is supported by the  National Research Fund, Luxembourg ,  INTER/MOBILITY/14/7562175.
Estimation of contact detection and contact-triggered GPS location success rate (objective 2) We used the proximity and contact-triggered GPS location data collected during the trials to measure the reliability of contact detection and acquisition of contact-triggered GPS locations by WildScope in close-toreal settings. We did this by comparing expected and observed contact events and acquisitions of contacttriggered GPS locations during the trials, to eventually determine the success rate both for contact detection and for acquisition of contact-triggered GPS locations. Specifically, we transposed contacts and contact-triggered GPS locations into a list of true positives (TP; expected and occurred contact detection events or.
We then applied a model selection procedure based on AIC scores to determine the model of best fit  [31]. We further evaluated the importance of each of the terms retained in the best-fit model to contribute to the goodness-of-fit of the model by means of an ANOVA based on deviance procedure (see Additional file 2). We applied an F test to account for overall differences within the variances of the covariates levels retained in the best-fit model. Lastly, we tested the significance of β-coefficients with respect to the reference level by means of a Student's t test. All the statistical analyses were programmed in SAS software 9. 3  [32].
Accuracy on the objective task was also analyzed with an identical ANOVA. Data from two participants were excluded from these analyses due to technical problems collecting objective task responses in one of the sessions. These two participants were included in the fMRI analysis because their responses to the subjective task were correctly recorded. Even thought target contrast was titrated to achieve ~50% "seen" and "unseen" targets in each condition, we analyzed the percentage of seen targets to ensure that the titration procedure was effective, with statistically similar results for endogenously valid and invalid trials, and for tone-present and tone-absent trials (all ps ≥ 0. 146, for main effects and interactions). Mean RT data from the objective task showed the significant main effects of Session, F(1,17) = 9.
Methods Participants. Nineteen right-handed voluntaries took part in the experiment (10 females, mean age 26 years, SD = 4). Additional data from one participant were excluded from the analyses due to excessive head motion during imaging. All participants had no neurological or psychiatric conditions and followed the safety requirements to underwent MRI scanning. They were naive to the purpose of the experiment, had normal or corrected-to-normal vision, and received monetary compensation for their participation. They gave signed informed consent to participate in the experiment. The study was reviewed by the INSERM ethical committee and received the approval of an Institutional Review Board (CPP Ile de France 1, Paris, France). The study was carried out in accordance with the approved guidelines.
Stimuli. E-prime software was used to control the presentation of stimuli, timing operations, and behavioral data collection. Images were projected to the head of the bore of the scanner via a display projector (Epson EMP-8300, 1024 × 768, 60 Hz) and viewed with a mirror attached to the head coil. Three black boxes (6° height × 5. 5° width) were displayed, one in the center of the display, and the other two centered 8. 5° to the left and right. The fixation point consisted of a black plus sign (0. 5° × 0. 5°) presented on the central box. The spatial cue consisted of an either a blue or yellow color circle subtending 0. 5° in diameter, presented at the fixation point. This spatial cue was either blue or yellow, indicating that the targets will likely appear on one side or the other (with color left and right side assignments being counterbalanced between participants).
We used color instead of arrows as cues to be sure to induce a purely endogenous orienting of attention. White noise (22. 050 Hz, 74 dB) was presented through the headphones as the alerting cue. A custom Matlab script was used to create 100 Gabor stimuli (4 cycles/deg. spatial frequency, 3° in diameter, SD of 0. 3°), with a maximum and minimum Michelson contrast of 0. 92 and 0. 02, respectively. The target contrast was adjusted during the fMRI session, so that the percentage of consciously perceived targets was ~50%. This titration procedure was done based on individuals' performance on the practice task (see task description below), and carried out independently for valid, invalid, tone present, and tone absent trials. All participants started with a high contrast stimulus (Michelson contrast = 0.
The jitter fixation periods were interleaved with the experimental trials as determined by the optimization program. fMRI data acquisition. Whole-brain fMRI was conducted on a 3-T Siemens TRIO whole-body MRI scanner at the CENIR MRI center (Salpêtrière Hospital in Paris) using a standard whole-head coil. Functional images were acquired using a gradient-echo echo-planar pulse sequence (TR = 2000 ms, TE = 25 ms, 39 contiguous 3-mm cubic axial slides, no inter-slice gap, flip angle = 75°, field of view = 220 mm, 372 volumes acquired per run). Prior to each functional scan, four volumes were discarded to allow for T 1 -equilibration effects. High-resolution T1-weighted anatomical images were also collected. fMRI data analysis. Standard preprocessing routines and analyses were conducted in SPM8 (Welcome Department of Cognitive Neurology, London).
The normalization algorithm used a 12-parameter affine transformation together with a non-linear transformation involving cosine basis functions. During normalization, the volumes were sampled to 3-mm cubic voxels. Templates were based on the MNI305 stereotaxic space. Then, functional volumes were spatially smoothed with a 7-mm FWHM isotropic Gaussian kernel. Finally, time series were temporally filtered to eliminate contamination from slow drift of signals (high-pass filter: 128 sec). Statistical analyses were performed on individual participants' data using the general linear model (GLM). fMRI time series data were modeled by a series of events convolved with a canonical hemodynamic response function (HRF). Two different fMRI GLMs were used in the analyses. First, we used an event-related model where the three phases of each fMRI trial (i.
Finally, we assessed functional connectivity via the beta correlation method, implemented in SPM8 with custom Matlab scripts. These analyses were restricted to the cue period. The canonical HRF in SPM was fit to each occurrence of each condition and the resulting parameter estimates (i. e. , beta values) were sorted according to the study conditions of interest (endogenous orienting -valid and invalid, alerting state -alert and non-alert, and awareness -seen and unseen) to produce a condition-specific beta series for each voxel. Two different functional connectivity analyses were performed: (1) pairwise connectivity between the ROIs extracted from the Seen versus Unseen contrast (IPL, SPL, FEFs, ACC, SMA, caudate, and thalamus); and (2) whole-brain functional connectivity with the right ACC as the seed region.
Acknowledgements ABC was supported by a  Ramón y Cajal fellowship  from the  Spanish Ministry of Education and Science. DB and FB were supported by postdoctoral grants from the eraNET-NEURON  BEYONDVIS  project. PMP-A  was supported by a  Ramón y Cajal fellowship  ( RYC-2014-15440 ), and grants  PSI2015-65696  and  SEV-2015-049  from the  Spanish Ministry of Economy and Competitiveness (MINECO). Research was funded by research projects  PSI2008-03595PSIC ,  PSI2011-22416 , and eraNET-NEURON BEYONDVIS,  EUI2009-04082  to PB; and  PSI2014-58681-P  and  Ramón y Cajal  research project  RYC-2011-09320  to  Ana B. Chica.
Geological planar facets (stratification, fault, joint…)  are key features to unravel the tectonic history of rock outcrop or appreciate the stability of a hazardous rock cliff. Measuring their spatial attitude (dip and strike) is generally performed by hand with a compass/clinometer, which is time consuming, requires some degree of censoring (i. e. refusing to measure some features judged unimportant at the time), is not always possible for fractures higher up on the outcrop and is somewhat hazardous. 3D virtual geological outcrop hold the potential to alleviate these issues. Efficiently segmenting massive 3D point clouds into individual planar facets, inside a convenient software environment was lacking. FACETS is a dedicated plugin within CloudCompare v2. 6. 2 (http://cloudcompare. org/) implemented to perform planar facet extraction, calculate their dip and dip direction (i.
e. azimuth of steepest decent) and report the extracted data in interactive stereograms. Two algorithms perform the segmentation: Kd-Tree and Fast Marching. Both divide the point cloud into sub-cells, then compute elementary planar objects and aggregate them progressively according to a planeity threshold into polygons. The boundaries of the polygons are adjusted around segmented points with a tension parameter, and the facet polygons can be exported as 3D polygon shapefiles towards third party GIS software or simply as ASCII comma separated files. One of the great features of FACETS is the capability to explore planar objects but also 3D points with normals with the stereogram tool. Poles can be readily displayed, queried and manually segmented interactively. The plugin blends seamlessly into CloudCompare to leverage all its other 3D point cloud manipulation features.
INTRODUCTION Acquiring dense 3D point cloud has been a challenge up until a decade ago. With the advent of fast lidar scanners and Structure-from-Motion techniques, non-technical communities have started producing their own point clouds shifting the emphasis towards making actual use of this 3D data. Among other pieces of software to do so, CloudCompare (  2016 ) is an open source 3D visualization and computation software, widely acknowledged across communities: power production industry (where it stemmed from originally), engineering, forensic science, archaeology or geosciences, to name a few. As such, CloudCompare has gained many features over the years, and its user-friendliness and its increasing feature set has established it as a standard tool within these communities. In 3D environments, and point clouds thereof, one specific geometrical property retains many users attention: planes.
They are both the simplest 2D geometric figure and one of the most meaningful elementary objects for many applications. In geology, planes appearing in geological rock outcrops tell a lot to the practitioner: sedimentation processes, tectonic history, rock mass strength, etc. Although planes are the simplest 2D geometric object, research is very active at the moment to find good ways to extract them automatically (e. g. Assali et al, 2016; Riquelme et al. 2014; Vasuki et al, 2014). The reason for such research activity might be a convergent evolution from different communities toward the need to master such basic element and move on from there. This research however has produced computing codes in many different environments (eg. Matlab in  Riquelme et al. , 2014 and Vasuki et al. 2014 ; stand-alone C++ software for  Assali et al, 2016).
All of them thriving to make an exact geological facet extractor, from a practitioner's point of view, but are limited by the burden of having to develop not only the plane extraction code, but also to encapsulate it in a somewhat user friendly environment. Research algorithms and programmatic schemes have been, and are still being, developed but in specific environments, isolated from the rest of the community established processing pipeline. FACETS, a structural geology plugin, implanted in CloudCompare (from version 2. 6. 2), was designed to extract planes from unstructured 3D point clouds. With all its other visualization and computation tools, CloudCompare was a candidate of choice to improve processing pipeline within a unique environment. FACETS contains three aspects: (i) a data processing aspect with two different algorithms, each with a minimum number of parameters; (ii) a stereogram rendering tool to produce structural geology diagnostics with a community established standard, assorted with an interactive query and subset interface; and (iii) export facilities towards third party software (GIS specific and all-purpose ASCII export) for further specialized interpretation.
Then, a second level of clustering initially groups elementary planes into encompassing planes. These are single plane entities which are expressed locally by plane fragments and belong to the same overall plane. It is often the case in nature where a unique fault plane may outcrop at different locations, sometimes coming out in thin air, sometimes entering back into the rock massif, but still making up the same fault plane. And finally, parallel planes are merged into plane families. The first clustering operation produces series of planar facets (each is a folder in CloudCompare database tree). Each facet is defined by a centroid, a normal, a contour and an effective precision (root mean square of residuals from initial points to best-fitting plane), which is generally lower than the roughness threshold criterion, that served for initial segmentation.
The spatial display will be updated with only the object belonging to the selected subset. Further, when the filter is enabled, the user may click at a given position in the stereogram. This will update the dip/dip direction values of the filter. This is very practical when a cluster of normals stands out in the stereogram and the user want to check it out. Each query may produce a subset sample when hitting the "Export". Selected objects (facets or points) will create an new object in the database tree. There is one particular application where dip direction span may be set to 360°, it is to isolate objects which may point in any azimuth but for which the dip is vastly different. This is case for point clouds of buildings, underground quarries (corridors), etc. Ceilings and floor have a normal pointing up or down, whereas wall have normal pointing horizontally.
Guessing appropriate maximum distance The maximum distance of a point to a best-fitting plane is a criterion which is difficult to appreciate off hand. Which value is best? Well, it depends … on the point cloud quality. We suggest the following procedure to specify values adapted to your dataset. This is what one did back in the days when FACETS did not exist. Inside your 3D point cloud, identify a planar facet which is unambiguous and isolate it from the rest of the point cloud. Compute the best-fitting plane (Tools > Fit > Plane) for this subset. The resulting best-fitting plane is a meshed surface. To check for the statistical values of the residuals, compute a cloud-to-mesh difference (Tools > Distances > Cloud/Mesh dist). Statistical statistical distribution properties can be queried with the histogram tool (Tools > Statistics > Compute Stats Param.