dataesr/scientific-paragraphs-categorization huggingface dataset:
T1-weighted and diffusion datasets The analyses were applied to the ARCHI database  (Duclap et al. , 2013; Assaf et al. , 2013)  composed of 79 subjects (32 females, mean age 23. 65 years, SD = 5. 16) with high quality T1-weighted images and diffusion data, acquired on a Magnetom TimTrio 3T MRI System (Siemens Healthcare, Erlangen, Germany). The MRI protocol used in this study included anatomical T1-weighted images at one millimeter isotropic spatial resolution, a B0 fieldmap to correct for susceptibility artifacts, and a high angular resolution diffusion imaging (HARDI) dataset along 60 optimized directions at a b value of 1500 s/mm 2 with an isotropic spatial resolution of 1. 7 × 1. 7 × 1. 7 mm 3. The imaging parameters were as follows: T1-weighted data: 160 slices, FOV 256 mm, Phase FOV 93.
In practice, this surface is composed of a set of about n = 80,000 nodes or vertices. In addition, for visualization purpose, a slightly inflated inter-subject average cortical surface was also created  (Fischl et al. , 1999a). Tractography. The HARDI dataset was processed using BrainVISA and Connectomist 2. 0  (Duclap et al. , 2012). It was first corrected for artefacts using a threefold strategy: a detection and correction of spikes and signal dropouts, a correction of the geometrical distortions induced by susceptibility effects using a further fieldmap calibration providing a non linear deformation to be applied along the phase axis, and a joint correction for eddy currents and motion for each diffusion-weighted volume (each diffusion direction was corrected using the rotation stemming from the rigid motion of each DW volume)  (Dubois et al.
Next, each tractography dataset was filtered in Fig. 1. Flowchart of the proposed framework. For each patch, the complete method could be divided into five steps, grouped into two parts: an individual phase (red box) and an inter-subject phase (blue box). Step1: the surface-based cortical connectivity matrices C g, s and profiles P g, s were computed. Step2: all the connectivity profiles were averaged across subjects P g group for the identification of putative, spatially-informed, group target regions or basins. Step 3: reduction of the dimension of the connectivity data on these group target regions, for each subject C g,s reduced. Step 4: the joint reduced connectivity matrix C g group,reduced was computed on the group. Step 5: Clustering of the patch vertices in order to obtain one parcellation of the patch from a group of subjects.
Reproducibility of the parcellation results In order to validate the method, we evaluated the reproducibility of the results in two ways. First, we performed the group-wise connectivity-based parcellation on the whole cortex independently for two non-overlapping subgroups of subjects randomly chosen (40 subjects for A1 subgroup, and 39 subjects for A2 subgroup), forming a partition of our database. Then, on a restricted subregion of the cortex (pre-and postcentral gyri), we proceeded further: we defined several partitions of the database into two subgroups (partition A with subgroups A1 and A2, partition B with subgroups B1 and B2, partition C with subgroups C1 and C2). In each partition, the first subgroup was built by randomly drawing without replacement 40 subjects out of 79 and the second subgroup, with the 39 remaining subjects.
Reproducibility of the clustering on the pre and postcentral gyri Fig. 7  shows the results achieved on the pre-and postcentral gyri for three partitions of the database, for both hemispheres. On visual inspection, we can observe a striking stability between subgroups 1 and 2 of each partition A, B and C: the cluster borders and shapes are highly similar. The same observation was made in other parts of the brain as seen in Fig. 5  for the caudal middle frontal gyrus (b), the pars opercularis, pars triangularis and pars orbitalis (c) and the supra-marginal gyrus (d). Moreover some consistent continuities in clusters boundaries could be seen between adjacent patches (Fig. 8 ) in each subgroup. Continuities are found at the same location across subgroups and partitions (red circles in Fig. 8 ).
-blue box). Type of fiber tracking. For the same subjects, the ARCHI database has been processed with two fiber tracking algorithms: deterministic and probabilistic. The probabilistic tracking produces better results in terms of stability across subgroups and partitions. The Rand index lies within the range [0. 6 -0. 8] and below 0. 6 for the probabilistic and deterministic tracking respectively (Fig. 14 red box). The parcellation results for the same subgroup A1 are compared between the reference condition and tested condition. Besides K = 3, the Rand index values are over 0. 5 (Fig. 14 -blue box). In both cases, we find the same general pattern. Group size. The reference condition was applied to a partition of two subgroups of 40 and 39 subjects. To test if the parcellation stability is maintained for smaller groups, two subgroups of 10 subjects, taken within subgroup A1, were used for this condition.
This approach has been proposed in  Roca et al. (2009). We internally validated the results with our own database, algorithms, and parameter settings, but a comparison with previous research is difficult. Prior publications have worked with different datasets and trackings, and, generally, each one is based on an individual analysis, and therefore the variability of the clustering results could be explained by these differences. Our results depend on the accuracy of the tractography, which is known to have uncertainty problems, especially with crossing fibers and near the cortex. Typically fibers ascend along a gyrus wall, and then turn quite abruptly about ninety degrees to enter the cortex in a specific cortical area. Tractography has difficulties following the exact location of such severe changes in direction, and the tracts tend to go straight to the gyrus crest  (Jbabdi et al.
Perspectives In the future, a direct extension of this groupwise parcellation method would be to try a concatenated approach  (Roca et al. , 2010; Roca, 2011) : after the dimension reduction, the clustering could be applied to the concatenation of the patch connectivity data across all subjects. This kind of method has the benefit to provide one specific parcellation per subject with a direct correspondence between clusters across subjects. However, this approach requires additional developments regarding the problems encountered with large datasets. The group clustering relies on a point-by-point averaging of connectivity data for all subjects. This assumes that seed points are aligned across subjects, which proves imperfect. The approximation here relies on the FreeSurfer alignment, and on the connectivity matrix smoothing.
Acknowledgments The " CONNECT/Archi Database " is the property of the CEA I 2 BM NeuroSpin centre and was designed under the supervision of  Dr. Cyril Poupon  and  Dr. Jean-François Mangin , and was funded by the  Federative Research Institute 49 , by the  HIPPIP European grant , and the  European CONNECT  project (http://www. brain-connect. eu). Acquisitions were performed by the scientists involved in the  Multiscale Brain Architecture research program  of NeuroSpin and by the staff of the  UNIACT Laboratory of NeuroSpin  (headed by  Dr. Lucie Hertz-Pannier ), under the ethical approval  CPP100002/CPP100022.
Introduction Gyrokinetic modeling is appropriate for describing Tokamak plasma turbulence and the gyroaverage operator is a cornerstone of this approach. This is the model used by Gysela, a simulation code which is used to study the turbulence development in plasma fusion. The gyroaverage operator J transforms the so-called guidingcenter distribution into the actual particle distribution. It enables to take into account effects relative to the finite Larmor radius, which is the radius of gyration of the gyro-center (motion which is faster than the turbulence we are looking at). In the present paper, we improve the gyroaverage scheme based on interpolation of the Gysela code to speedup calculations. For the current gyroaverage operator, the Larmor radius ρ is assumed to be independent in space.
This code considers a computational domain in five dimensions (3D in space describing a torus geometry, 2D in velocity)  [6, 7]. Time evolution of the system consists in solving Vlasov equation non-linearly coupled to a Poisson equation (electrostatic approximation, quasi-neutrality is assumed). Routinely, physicists perform large Gysela simulations using from 1k to 16k cores on supercomputers. This work aims at improving the gyroaverage method based on Hermite interpolation which is too slow to be used in production for the moment. In achieving this optimization, we will allow the physicists to access to more accurate simulations and then to better understand the physical processes that arise in the simulations at finest scales. The use of the Fourier transform reduces the gyroaveraging operation by a multiplication in the Fourier space by the Bessel function.
Tab. 1 shows the execution time of the gyroaverage for the different sizes of the input plane. The given measurements have been obtained as the average over 100 runs of the case presented in Section 2. 3. The time corresponding to the building of the variable rhs product is given in lines "RHS" and the time for the product between the weights and the W f in lines "Product". The second data structure implementation is roughly twice faster than the first one. If you pay attention to the details, the first data structure is competitive during the "Product" step, but the time needed to build the rhs product represents a large overhead. As the second data structure gives shorter execution times, this is the chosen implementation. With this data structure, the accesses to the W f are not contiguous during the sparse dot product (see Fig.
Benchmarking small cases After integration of the different versions of the gyroaverage operator in Gysela code  [5] , different runs have been done to compare their effectiveness. To keep a reasonably short execution time, the simulations are composed of only four time steps. These tests were done on the Helios 2  machine. Computation nodes used are equipped with two Intel Xeon E5-2450 2. 10GHz processors, so 16 cores a node. On Tab. 3, the different test cases have been performed over the two following meshes (  6 ) and  (7) : N r = 256, N θ = 256, N ϕ = 32, N v = 16, N µ = 4 (6) N r = 512, N θ = 512, N ϕ = 32, N v = 16, N µ = 4. (7) To highlight the difference of performance between the different gyroaverage implementations, the size of the poloidal plane is multiplied by 4 between the two meshes.
This quite artificial setting will be revised in the next Subsection dedicated to production runs. Tab. 3 shows some results concerning the Padé approximation compared to 4 versions of the Hermite implementation: original implementation; compact vector (described in Section 4. 1), blocking (Section 4. 2) and layout (Section 4. 3) optimizations. Also, the Gysela code can be executed without any gyroaverage at all (solution named Disable gives invalid results of course but consitutes a reference execution time). This last option allows us to determine accurately the fraction of the execution time corresponding to the gyroaverage operator over the total execution time for the other cases. As expected, the computation time of the gyroaverage operator grows along with the size of the poloidal mesh.
Modern nuclear data Quality Assurance (QA) is, in practice, a multistage process that aims at establishing a thorough assessment of the validity of the physical information contained in an evaluated nuclear data file as compared to our best knowledge of available experimental data and theoretical models. It should systematically address the performance of the evaluated file against available pertinent integral experiments, with proper and prior verification that the information encoded in the evaluation is accurately processed and reconstructed for the application conditions. The aim of the NDEC (Nuclear Data Evaluation Cycle) platform currently being developed by the Data Bank is to provide a correct and automated handling of these diverse QA steps in order to facilitate the expert human assessment of evaluated nuclear data files, both by the evaluators and by the end users of nuclear data.
Introduction Quality Assurance (QA) applied to nuclear data is designed to ensure that a comprehensive set of quality requirements are fulfilled for electronic evaluated nuclear data files. These requirements go well beyond the necessary verification of compliance with the ENDF-6 format for the electronic files. The verification that the information encoded in the evaluation is accurately processed and reconstructed for the application conditions (e. g. energy grouping and application temperatures) is necessary. Giving a systematic assessment of the performance of each file in pertinent (i. e. sensitive to the nuclide and reactions being tested) internationally evaluated integral benchmark experiments is necessary. The Data Bank is currently developing an automated platform called NDEC (Nuclear Data Evaluation Cycle) that centralizes such QA processes in an user friendly approach.
Development of the NDEC platform In practice, evaluated nuclear data QA is a multistage process that implies having the appropriate computational framework and expertise to run different codes and corresponding input sequences, a e-mail: franco. michel-sendis@oecd. org with the capability to interpret the resulting output and handle the large numbers of secondary files generated per evaluated file tested. The problem of modern Evaluated Nuclear Data QA is an example where centralization and automation of sequenced processes become necessary in order to facilitate a thorough diagnose of the data. Such diagnoses aim at providing both evaluators and end-users of nuclear data with an in-depth understanding of the data's quality, domain of applicability and, importantly, its limitations. This information is essential when selecting candidate evaluations for a reference Nuclear Data Library.
Current Status The Data Bank started in early 2015 to develop a platform for the systematization of a number of these QA processes, called NDEC (Nuclear Data Evaluation Cycle). Currently, NDEC is an operational prototype that automatically performs a number of steps following the submission of an ENDF-6 evaluated file. These steps will be described below. The longer aim of NDEC is to be available as an online NEA Data Bank service providing access to the output and diagnoses of these different QA steps. Currently the steps handled by NDEC are: evaluated format checking, processing, differential comparisons and integral benchmarking. The resulting output is organized in a user-friendly interface. The codes and tools presently used are briefly described below.
Evaluated format checking and processing of ENDF-6 files ENDF-6 format compliance checks are performed with the utility codes CHECKR, FIZCON, PSY-CHE, INTER. Their outputs are compiled. Processing of the submitted file is currently performed with NJOY-2012, PREPRO-2015 and AMPX-6. 2. The use of the latter is being carried out by the Data Bank in collaboration with ORNL/SCALE development team under a beta-tester agreement, pending the official release of AMPX (see these proceedings on the processing of JEFF-3. 2 with AMPX  [1] ). As a general principle, NDEC provides access to all associated inputs used and the outputs obtained by constructing a modular XML organization accessible from a webpage, with modules that are visually color-coded according to the errors or warnings received (see Figure  1 ) in each sequence performed.
Integral benchmarking Assessing the integral performance of each evaluated nuclear data file in benchmark calculations is an essential step in the QA process, and an important criterion to consider when selecting candidate evaluations for a nuclear data library. Special care should be first given to the selection of benchmarks to run, which depend on the nuclide evaluation / reaction channel / energy domain and, in general, the application being tested for. In order to develop the relevant 'validation suites', the use of the different tools developed by the NEA for these purposes is foreseen. Currently in NDEC, this step is focusing exclusively in criticality benchmarking, with the aims to expand this scope in the future. A large computation and compilation effort of criticality sensitivity profiles for available ICSBEP  [3]  and IRPHE  [4]  benchmark experiments has been undertaken at the NEA in recent years, resulting in the creation of software searching tools that are able to interrogate the integral databases and select the pertinent benchmark cases (nuclide/reaction specific, above a given sensitivity threshold to k e f f ); these are the DICE (Database for the International Criticality Safety Benchmark Evaluation Project) or the IDAT (International Reactor Physics Handbook Database and Analysis Tool) tools.
Foreseen developments The development of NDEC is still ongoing, and many developments could still be envisaged in the medium to longer term. Concerning integral validation, the enlarged scope beyond criticality benchmarking should be considered and could include the selection of relevant decay cases (decay heat, doses, reaction rates), the comparison to spectral average cross sections included in EXFOR, inclusion of transmission and shielding benchmarks from ICSBEP and SINBAD databases, or even performing simple computational benchmarks between evaluated files (e. g. critical mass, shielding transmission, particle emission, etc. ) among others.
Value creation Market positioning Star-rating Local competition Localization outside popular destinations a b s t r a c t Although user-generated reviews on social media are greatly influencing competition and customer purchase patterns in the hospitality industry, empirical research has so far marginally investigated whether hotels are able to appropriate the economic value that the use of social media features can bring. In order to fill this gap, this article uses panel data from 2004 to 2012 on a sample of 240 small and mediumsized hotels for which we have collected data on profitability and 50,115 user-generated reviews on TripAdvisor, one of the most popular and largest online community for travellers. The results from fixed effects regression models show that online ratings from user-generated reviews on TripAdvisor have a positive effect on hotel revenue growth that is outweighed by a negative effect on gross profit margins.
Sample and data collection Our data collection involved a random selection of 240 Italian small and medium hotels, choosing for statistical power a confidence level of 95% and a confidence interval of 6%. These hotels were extracted from a population of 2,862 small and medium Italian hotels. Specifically, hotels had fewer than 250 employees, in line with the European Union's definition of small and medium enterprises and other research studies  (Neirotti & Raguseo, 2016). The hotels selected were listed on the AIDA public database (distributed by Bureau Van Dijk), which is the main compendium of financial information of firms in Italy. Beginning with the population of small and medium-sized hotels listed on AIDA, we randomized the extraction of 240 hotels. Then, we verified whether each of these hotels was present on Tri-pAdvisor.
If it was on TripAdvisor, we included the hotel in the final sample. Otherwise, we excluded the hotel from the sample and we proceeded by randomly extracting another hotel from the population, again verifying its presence on TripAdvisor. The random extraction ended when we came up with 240 hotels for which we had both financial data available on AIDA and data on usergenerated reviews on TripAdvisor. Beginning with the financial information available for the 240 hotels selected, we built a panel dataset that spanned the period between 2004 and 2012. The dataset also included gathered data about user-generated reviews (number and rates assigned by users on a 5-level scale) from TripAdvisor on an overall amount of 50,115 reviews. By complementing data from TripAdvisor and data from the AIDA database, the final dataset contained 2,160 observations.
Discussions and conclusions In this study, we have analysed panel data on the value generation and appropriation mechanisms related to 240 Italian small and medium-sized hotels' online visibility on infomediary platforms, such as TripAdvisor. In doing so, our lens has focused on the characteristics of customer reviews and on how hotels' market positioning can moderate the relationship between such forms of online visibility and value appropriation. We decided to investigate this topic for two main reasons. First, recent literature has not shed light on the ambivalent role of social media as a threat or an opportunity for small hotels' profitability because the great part of empirical works on this theme have taken the relationship between online visibility and sales into account, overlooking the effects that online visibility can have on profitability (e.
Introduction: The aim of the EuReCa ONE study was to determine the incidence, process, and outcome for out of hospital cardiac arrest (OHCA) throughout Europe. Methods: This was an international, prospective, multi-centre one-month study. Patients who suffered an OHCA during October 2014 who were attended and/or treated by an Emergency Medical Service (EMS) were eligible for inclusion in the study. Data were extracted from national, regional or local registries. Results: Data on 10,682 confirmed OHCAs from 248 regions in 27 countries, covering an estimated population of 174 million. In 7146 (66%) cases, CPR was started by a bystander or by the EMS. The incidence of CPR attempts ranged from 19. 0 to 104. 0 per 100,000 population per year. 1735 had ROSC on arrival at hospital (25. 2%), Overall, 662/6414 (10.
Methods EuReCa ONE was an international, prospective, multi-centre one-month study, designed as initial first step to establish a European Registry of Cardiac Arrest (EuReCa). 18 Patients who had an OHCA during October 2014 which occurred in any participating region and who were attended and/or treated by an Emergency Medical Service (EMS) were eligible for inclusion in the study. Patients were eligible for inclusion regardless of performance or non-performance of a resuscitation attempt, arrest aetiology, initial arrest rhythm, age, or gender. The study dataset was developed by the Steering Committee (SC) in accordance with Utstein definitions. 19 A revised Utstein dataset was introduced close to the time of study inception, which included a new category of aetiology i. e. 'medical'. As well as 'presumed cardiac', the 'medical' category includes all other medical causes in which there is no obvious cause of cardiac arrest.
14 In order to facilitate data collection from existing systems, data on aetiology was collected for both the 'presumed cardiac' and 'medical' categories. The EuReCa ONE study is registered with ClinicalTrials. gov (T02236819). The study protocol has been published previously and describes all questions to be addressed in the EuReCa ONE Study (see Supplement S1). 20 ountries were recruited to participate in EuReCa ONE through an open invitation meeting during the European Resuscitation Council (ERC) Congress in 2013. The recruitment process resulted in 27 countries committing to participate in EuReCa ONE. Each participating country was requested to identify one National Coordinator (NC). All NCs signed a Memorandum of Understanding agreeing that they were responsible for obtaining ethical approval/waiver for participation in EuReCa ONE, coordinating national data collection, assuring data quality, and for submitting de-identified data for analysis.