dataesr/scientific-paragraphs-categorization huggingface dataset:
EEG dataset The EEG recordings were provided by the Epilepsy Monitoring Unit, St. Luke's Hospital, Thessaloniki, Greece. The data used in this work were acquired during a whole-night sleep EEG of a subject with history of right lobe epilepsy of fronto-temporal origin. Nocturnal sleep was recorded using multi-channel electrodes positioned according to the extended international 10-20 system on an electrode cap with sampling frequency 500 Hz. The spikes were visually identified by an experienced neurophysiologist as transients clearly distinguished from background activity with pointed peaks. The markers were manually placed (in T4 and F8 channels) at the peak of the negative phase, but imprecise markings were later corrected by automatically shifting them to the largest negative peak within a predefined neighborhood (equal to the defined detection latency) around the original marking.
Performance The method was assessed on 9-h recordings including 101 marked spikes. A total number of 4708 candidate spikes (99 TP and 4609 FP) were automatically detected during the 1st step of the method. Only two spikes were missed (not detected). Examples of data containing TP and FP waveforms, identified in this step of the method, are illustrated in Fig. 4. All TP and FP transients detected in the 1st step of the method are summarized in Fig. 5  in the form of probability maps, and are also averaged to highlight shape differences between TP and FP. It is evident that, on the average, the epileptic spikes follow a more distinguishable smooth pattern than the nonspecific sharp transients. Moreover, since the FP transients are many and also exhibit large (per point) variation, the mean values do not overlap with the most frequent values.
If different training and testing datasets were used, this is indicated by two numbers separated by '/. ' Although a direct comparison is not feasible due to the different data per study, it can be seen that our method performs better than all (  16 ) reviewed methods in Table  2 , Fig. 4  Illustration example of 3 correct (left) and 3 false (right) spike detections for the selected channels (1st channel: T4, 2nd channel: F8). The same amplitude scale (±50 lV) has been used for all plots. The spike location is approximately at the center of each plot and indicated with a white line whereas it has the highest sensitivity and 4th of 14) lowest selectivity among methods reviewed in Table  3. Furthermore, a recent method detecting interictal epileptiform discharges based on the merger of increasing and decreasing sequences and SVM classification  [16]  achieved average detection sensitivity *0.
The original dimensionality of the waveforms used for classification was d = 31 (corresponding to 301 ms at 100 Hz), whereas the reduced dimensionality estimated by the MLE method was l = 14. The F-score as a function of dimensionality is shown in Fig. 6  for the three best dimensionality reduction techniques (LLTSA, LPP, NPE). It can be seen that LPP is not very sensitive to the selection of l and achieves higher F-score for more values of l than LLTSA; thus, it is preferred as technique in this application. The method was developed in Matlab. The total computational time (including the ten-fold cross-validation scheme) in a Windows machine, Intel Core TM 2 Duo CPU 2. 2 GHz, was approximately 4 min for the applied dataset, but depends highly on the number of candidate spikes extracted in the 1st step of the method.
Acknowledgments The authors wish to acknowledge the contribution of  Dr. V. Kokkinos  from  St. Luke's Hospital, Thessaloniki, Greece , who supported the collection and annotation of the EEG recordings. They also wish to thank  Dr. A. Koupparis  and  Dr. G. K. Kostopoulos  from  University of Patras Medical School  for valuable discussions. This study is partially funded by the  European Commission  under the  Seventh Framework Programme  ( FP7/2007-2013 ) with grant ARMOR, Agreement Number  287720. This research has been co-financed by the  European Union (European Social Fund-ESF ) and Greek national funds through the  Operational Program ''Education and Lifelong Learning '' of the National Strategic Reference Framework (NSRF)- Research Funding Program :  Thales. Investing in knowledge society through the  European Social Fund.
His main research interests include medical image analysis, pattern recognition, data mining, data compression, biomedical informatics, and multimedia database systems. Prof. Megalooikonomou  is a member of the ACM,  IEEE ,  SIAM , and  SPIE. In 2003 he received a  CAREER award  from the  US National Science Foundation  for developing data mining methods for extracting patterns from medical image databases. He regularly serves as a program committee member or referee on several premier conferences and journals in his areas of research.
Impact of Matrix-Element Corrections: In fig. 17  we show parton-level predictions of VINCIA in Z production events, i. e. multi-parton-interactions and hadronisation turned off, to focus solely on the shower properties and the impact of successive MECs. Comparisons to data including multiparton-interactions and hadronisation will be presented in the section sec. 4. We compare VINCIA with increasing orders of MECs included to ATLAS  [91]  and CMS  [92]  data. The inclusive cross section and the azimuthal angle between the reconstructed Z boson and the hardest jet (shown in the upper panel of fig. 17 ) clearly highlight that MECs improve the description of data sensitive to multiple hard emissions. The progressive improvements that are introduced through iterated MECs is particularly obvious in the inclusive jet multiplicity.
We use the conventional definition of the transverse region, spanning 60 • < ∆φ < 120 • in azimuth with respect to the leading charged-particle jet, and compare to CMS data  [110]. These comparisons satisfy us that at least the global properties of the UE are in acceptable agreement with the measurements, in particular in regards to the average p ⊥ density (top right-hand plot) and its event-to-event fluctuations (bottom right-hand plot). The charged-track multiplicity (top left-hand plot) is a more difficult observable to predict since it is less IR safe and hence more dependent on details of the hadronisation modelling; we presume that the small (O(10%)) discrepancies observed for both PYTHIA and VINCIA in this observables may be due to imperfections in PYTHIA's still rather crude modelling of colour reconnections.
This is not surprising given that the inclusive-jet cross section in PYTHIA/VINCIA is calculated at LO. The tails of the distributions unfortunately suffer from rather large weight-fluctuation effects, as was discussed above; nonetheless we note that the bins for which a reasonable statistical precision is obtained are generally closer to the data than the PYTHIA reference comparison. Finally, in fig. 29  we show the differential jet shape variable ρ(r) and its cumulative integral Ψ(r) for different ranges of the jet transverse momentum, compared with ATLAS data from  [115]. This validates that the FSR broadening of QCD jets is in reasonable agreement with the experimental measurements, though we note that VINCIA's distributions may be slightly too narrow, which we again regard as being consistent with the LL nature of VINCIA's antenna functions and analogous to the slightly too narrow Thrust distribution we allowed in the e + e -event shapes.
Acknowledgements We thank the  HepForge  project, www. hepforge. org[117] for providing the hosting and repository services used for developing and maintaining the VINCIA code. HepForge is funded via the  UK STFC  and hosted at the Durham IPPP. We thank  Johannes Bellm  for useful discussions on [87]. This work was supported in part by the  ARC Centre of Excellence for Particle Physics at the Terascale. SP is supported by the  US Department of Energy  under contract  DE-AC02-76SF00515. The work of MR was supported by the  European Research Council  under  Advanced Investigator Grant   ERC-AdG-228301  and  ERC  Advanced Grant no. 320651 , "HEPGAME". PS is the recipient of an  Australian Research Council Future Fellowship ,  FT130100744 : "Virtual Colliders: high-accuracy models for high energy physics".
The effective Neodymium cumulative fission yields for 235 U have been measured in the fast reactor PHENIX relatively to the 235 U fission cross-section. The data were derived from isotope-ratio measurements obtained in the frame of the PROFIL-1, PROFIL-2A and PROFIL-2B programs. The interpretations of the experimental programs were performed with the ERANOS code in association with the Joint Evaluated Fission and Fusion library JEFF-3. 1. 1. Final results for 143 Nd, 145 Nd, 146 Nd, 148 Nd and 150 Nd were 5. 61%, 3. 70%, 2. 83%, 1. 64% and 0. 66%, respectively. The relative uncertainties attached to each of the cumulative fission yields lie between 2. 1% and 2. 4%. The main source of uncertainty is due to the fluence scaling procedure (<2%). The uncertainties on the Neodymium capture cross-sections provide a contribution lower than 1%.
The energy dependence of the fission yields was studied with the GEF code from the thermal energy to 20 MeV. Neutron spectrum average corrections, deduced from GEF calculations, were applied to our effective fission yields with the aim of estimating fission yields at 400 keV and 500 keV, as given in the International Evaluated Nuclear Data Files (JEFF, ENDF/B and JENDL). The neutron spectrum average correction calculated for the PROFIL results remains lower than 1. 5%.
The percentage burnup of actinide in the fuel can be determined by analyzing the fission product inventories. One of the fission yields routinely used for reactor applications is 148 Nd. The burnup of fuel can be estimated using the stable nuclide 148 Nd, which is determined by destructive assay. The precursors of 148 Nd are all short lived and have small cross-sections and thus the cumulative yield can be used to determine the number of fissions that has taken place in the fuel. Previous interpretations of the PROFIL-1, PROFIL-2A and PROFIL-2B integral experiments carried out in the PHENIX reactor (CEA Marcoule, France) with the ERANOS code  [6, 7]  in association with the evaluated nuclear data library JEFF-3. 0 and JEFF-3. 1 showed some significant discrepancies for the cumulative fission yields of several Neodymium isotopes for the fast fission of 235 U  [8, 9].
The average calculated-to-experimental ratios C/E associated to the Neodymium produced by fission in several samples are gathered in Figure  1. Conspicuously, there is a large drift with respect to the atomic mass number A from A = 143 to A = 150, far exceeding the claimed experimental uncertainties on fission yields, which lie in the range between 1% and 2. 4%. Such a drift is not observed for the 239 Pu and 241 Pu samples. A reanalysis of the PROFIL experiments was performed with the ERANOS code by using the nuclear data of the JEFF-3. 1. 1 library  [5, 10]. A similar drift of the average C/E values related to the Nd isotopes measured in the isotopes. Additional calculations performed with the GEF code  [12, 13]  allowed accounting for the systematic behavior of the fission yields with the incident neutron energy.
Principle of the experiments The PROFIL experiments were designed to study the fast energy range of the neutron cross-sections, called "continuum" in Figure  2. In the same figure, the 235 U capture and fission cross-sections are compared to the fast-neutron spectrum calculated by using the ECCO lattice code of the ERANOS system  [6, 7]  with 1968 energy groups and a critical buckling. Fission yields for the main actinides such as 235 U, 238 U, 238 Pu, 239 Pu, 240 Pu, 241 Pu, 242 Pu and 241 Am are also available. Only results obtained for the fission of 235 U are presented in this paper. The principle of the experiments consists of irradiating samples containing a small amount of pure isotope in a fastneutron flux. The two experiments were carried out four decades ago during the first cycles of the 250 MWe sodiumcooled fast reactor PHENIX (CEA Marcoule, France).
A simplified radial view of the core is presented in Figure  3. The first phase (PROFIL-1) was composed of a single experimental pin loaded in a standard PHENIX assembly placed at the center of the core. The pin contained 46 separate samples. The second phase (PROFIL-2A and PROFIL-2B) was a more ambitious program with two experimental pins, labelled A and B, placed in the inner core of PHENIX. Each pin contained 42 separate samples. Figure  3  also shows the pin location in their respective fuel subassemblies. A total of 130 samples were irradiated during four cycles for PROFIL-1 and PROFIL-2 (>10 months). Samples loaded in the PROFIL pins were cylindrical containers with an outer diameter of 5. 5 mm and a height of 8 mm. An example of a stainless steel double container is shown in Figure  3.
Fluence scaling issue The interpretation of the PROFIL experiments is performed with the deterministic code ERANOS  [6, 7]. The calculation scheme relies on several hypotheses that are used to obtain a mean neutron flux over the irradiation period dt. The dispersion of the C/E ratios, observed for the 235 U samples, suggests that such a raw treatment overestimates the magnitude of the global fluence. The origins of the observed dispersions could be due to the approximations used in the calculation scheme but also to the influence of the surrounding materials, such as the steel containers, that could locally change the neutron spectrum. The strategy for solving the inconsistency between the calculations and the experimental results consists in normalizing the results utilizing the 235 U fission cross-section.
Integral results on Neodymium produced by fission Integral results obtained from the interpretation of the PROFIL experiments with the ERANOS code are expressed in terms of calculated-to-experimental ratios (Eq. (  4 )), normalized to the 235 U fission cross-section by using the fluence scaling operations shortly explained in Section 3. 3. The main sources of uncertainties discussed in the following sections are the uncertainties related to (i) the calculation scheme, (ii) the calculation of the mean value (Eq. (  5 )), (iii) the calculation of the axial shape of the neutron flux, (iv) the fluence scaling and (v) the Nd capture cross-sections. The C/E values obtained with the ERANOS code by using the JEFF-3. 1 and JEFF-3. 1. 1 libraries are listed in Table  2  for each 235 U sample. The two JEFF libraries share the same Nd cumulative fission yields  [5].
1. 1. The normalization factors range from 1. 08 (for 143 Nd) to 0. 6 (for 150 Nd). The comparison with a fast-neutron spectrum representative of the PROFIL experiments shows the smooth variation of the fission yields in the energy range of interest for fast-reactor applications (around 400 keV and 500 keV). The black circles are the cumulative fission yields at 400 keV (for the JEFF library) and 500 keV (for the ENDF/B and JENDL libraries) also deduced from the PROFIL results, but they account for a neutron spectrum average correction calculated with the GEF code (Eq. (  17 )). ' ðEÞ ¼ Y c G ð A Nd; EÞ Y c G ð A NdÞ : ð17Þ In the case of the PROFIL experiments, such a neutron spectrum average correction lies between 0. 5% and 1. 4%. For each Nd isotope, we also observe that the obtained correction varies slowly with the neutron energy, between 400 keV and 500 keV.
The calculated corrections D ' (E) reported in Table  8  were applied on the effective fission yields ⟨Y c ð A NdÞ⟩ given in Section 5. 1 as follow: The results are reported in Figure  9  (black circle). We observe a slight reduction of the dispersion of the Nd values in the case of the comparison with the JENDL and ENDF/B fission yields. The variation of the fission yields with the neutron energy could also contribute to explain the PROFIL trends with the Nd mass number obtained with the JEFF library (Figs. 1  and 5 ). The encouraging results from the GEF code (version 1. 7, 2013) are still preliminary and have to be considered with care.
1% and 2. 4%. The final uncertainty includes the contribution of the fluence scaling uncertainty, which is close to 2%. This well-known source of uncertainty mainly depends on the accuracy of the 235 U(n,f) reaction. The contribution of the uncertainties on the Nd capture cross-sections was also quantified. The magnitude of such a contribution is low (<1%), confirming statements found in previous works. We also report for the first time the impact of the energy dependence of the cumulative fission yields that smoothly decrease in the fast-energy range. The GEF code was used to provide neutron spectrum average corrections ranging from 0. 5% to 1. 4%. According to these calculations, the neutron spectrum average correction seems to be rather small in the case of the PROFIL experiments. The GEF calculations have provided encouraging results for future evaluation works.
However, this preliminary result has to be confirmed by using an improved version of the code over the seven other fissile systems Table  8. Results calculated with the GEF code after normalization to the thermal fission yields of JEFF-3. 1. 1. Columns (a) and (b) give the cumulative fission yields calculated at 400 keV and 500 keV. The column (c) contains the fission yields from GEF weighted by the uranium fission rate calculated for the 235 U samples in the PROFIL experiments. The last two columns give the neutron spectrum average correction defined as the ratios of the fission yields at 400 keV and 500 keV to the effective fission yield.
February-March 2009)   were modelled using the regional air quality model CAMx with VBS (volatility basis set) approach for the first time in Europe within the framework of the EURODELTA-III model intercomparison exercise. More detailed analysis and sensitivity tests were performed for the period of February-March 2009 and June 2006 to investigate the uncertainties in emissions as well as to improve the modelling of organic aerosol (OA). Model performance for selected gas phase species and PM 2. 5 was evaluated using the European air quality database AirBase. Sulfur dioxide (SO 2 ) and ozone (O 3 ) were found to be overestimated for all the four periods, with O 3 having the largest mean bias during June 2006 and January-February 2007 periods (8. 9 pbb and 12. 3 ppb mean biases respectively).
CAMx The Comprehensive Air quality Model with extensions, CAMx-VBS (CAMx5. 41_VBS, kindly provided by EN-VIRON before its public release), was used in this study. The model domain consisted of one grid with a horizontal resolution of 0. 25 • × 0. 25 •. The latitude and longitude grids extended from 25. 125 • W to 45. 125 • E and 29. 875 to 70. 125 • N, resulting in 281 × 161 grid cells covering the whole of Europe. Hourly four-dimensional meteorological fields for wind speed and direction, pressure, temperature, specific humidity, cloud cover and rain required by CAMx simulations were calculated from ECMWF IFS (Integrated Forecast System) data at 0. 2 • resolution. Vertical diffusivity coefficients were estimated following the Kz approach of O'Brien (1970) using planetary boundary layer depth profiles as available in IFS data.
The required ozone column densities to determine the spatial and temporal variation of the photolysis rates were extracted from TOMS data (NASA/GSFC, 2005). Removal processes as dry and wet deposition were simulated using the Zhang resistance model  (Zhang et al. , 2003)  and a scavenging model approach for both gases and aerosols  (Environ, 2011) , respectively. For the gas phase chemistry the carbon bond (CB05) mechanism  (Yarwood et al. , 2005)  with 156 reactions and up to 89 species was used. Partitioning of inorganic aerosols (sulfate, nitrate, ammonium, sodium and chloride) was performed using the ISORROPIA thermodynamic model  (Nenes et al. , 1998). Aqueous sulfate and nitrate formation in cloud water was simulated as well, using the RADM aqueous chemistry algorithm  (Chang et al. , 1987).
Anthropogenic emissions Annual total gridded anthropogenic emissions were prepared and provided by INERIS for the EDIII exercise, which is based on a merging process of databases from different sources, i. e. TNO-MACC  (Kuenen et al. , 2011) , EMEP  (Vestreng et al. , 2007)  and GAINS (The Greenhouse Gas and Air Pollution Interactions and Synergies). For specific countries where TNO-MACC emissions were missing (Iceland, Liechtenstein, Malta and Asian countries), the EMEP 0. 5 • × 0. 5 • emissions were used and re-gridded using adequate proxies such as "artificial land-use" and EPER (European Pollutant Emission Register) data (http://www. eea. europa. eu/) for industries. Total primary particle emissions were made available by EMEP in two different size ranges: below 2. 5 µm (fine) and between 2.
Statistical methods Statistical procedures as available in the Atmospheric Model Evaluation Tool (AMET;  Appel et al. , 2010)  were used in this study to evaluate model performance. Daily ambient measurements of main gas phase species, i. e. O 3 , NO 2 , CO, SO 2 and fine particulate matter (PM 2. 5 ), were extracted from the AirBase database in Europe and statistics reported in terms of mean bias (MB), mean error (ME), mean fractional bias (MFB) mean fractional error (MFE) and correlation coefficient (r). Due to the coarse grid resolution, only rural-background stations, defined as stations far from city sources of air pollution with pollution levels determined by the integrated contribution from all sources upwind of the station (ETC/ACC, 2004/7), with at least 80 % daily average observations available were considered for the statistical analysis.
NO 2 and O 3 NO 2 was found to be underpredicted for all the four periods with mean fractional bias between -54 and -28 % and NO 2 concentrations being particularly underpredicted during June 2006. Evaluation of the EDIII model intercomparison exercise showed that all models performed similarly for NO 2 in terms of correlation with r values in the range 0. 6-0. 7 and the spatial correlation was much higher in the range 0. 7-0. 9 for all models  (Bessagnet et al. , 2016)  with a general underestimation in the afternoon. The NO 2 performance could be influenced by several factors. -Uncertainties in the emission inventories. Although NO x emission estimates in Europe are thought to have an uncertainty of about ±20 %, the complete dataset used in the inventories has much higher uncertainty  (Kuenen et al.
, 2014). A recent study identified a significant discrepancy between emission estimates and actual flux measurements, with the highest underestimation being a factor of two in central London mainly due to underrepresentation of real world road traffic emissions  (Vaughan et al. , 2016)  -The relatively coarse resolution of the domain may result in too low NO x emissions or isolated local events that the model cannot resolve. We report daily average time series of NO 2 for the period of February-March 2009 for stations in Table  2  as well as daily average time series of NO 2 for stations not exceeding 5 ppb (which represents 92 % of the stations in Table  2 ) (Fig. S1  in the Supplement). The model performance for NO 2 significantly improved when the 5 ppb threshold was applied to the dataset.
Conclusions A modelling study using the regional air quality model CAMx with a VBS scheme was performed for the first time in Europe within the EDIII model intercomparison exercise. An evaluation for the main gas phase species and PM 2. 5 for four different periods was performed using the European air quality database AirBase as well as AMS measurements. The period in February-March 2009 was further analysed in more detail using different assumptions regarding the volatility of emitted organic aerosol and precursor emissions. The main findings of this study are summarized below. Although total PM 2. 5 mass concentrations and its variations were well reproduced by the model in all four periods, comparisons with AMS measurements for the February-March 2009 period revealed that the good agreement between model and measurements was due, most of the time, to overestimation of the inorganic fraction, especially NO - 3 , and underestimation of OA.