dataesr/scientific-paragraphs-categorization huggingface dataset:
(2018)  and  Clarisse et al. (2019). The retrieval was applied to each spectrum recorded by IASI/Metop-A (since October 2007) and -B (since March 2013), which represents about 1,300,000 observations per day and per instrument, before prefiltering to scenes with a cloud fraction below 10%. An additional postfilter discards the measurements where the observational sensitivity to PAN is too low to produce a meaningful column. The IASI daily global PAN product employed here uses the European Centre for Medium-range Weather Forecasts Reanalysis Version 5 (ERA5) data set  (Hersbach et al. , 2020)  to describe the state of the surface and atmosphere. The retrieval procedure was applied to both morning and evening IASI satellite overpasses. In the case of PAN, the sensitivity of the evening observations remains satisfactory and consistent with the daytime observations, hence both morning and evening PAN columns are used here.
GEOS-Chem model simulation Our investigations are supported with an O 3 -NO x -VOCaerosol standard full chemistry GEOS-Chem global CTM simulation  (Bey et al. , 2001). We used model version 12. 0. 2 (https://doi. org/10. 5281/zenodo. 1455215) with MERRA-2 assimilated meteorological fields at 2 Â 2. 5 (latitude/longitude) horizontal resolution and 72 vertical levels, reaching up to 0. 01 hPa (approximately 80 km). This version of GEOS-Chem implements the Harvard Emission Component (HEMCO;  Keller et al. , 2014)  Version 2. 1. 008 as well as the updates developed and described by  Fischer et al. (2014)  for the PAN simulation. The adopted inventories include EDGAR v4. 3 for fossil fuel emissions, the EMEP and NEI2011 for regional anthropogenic emissions, GFED v4 for fire emissions, MEGAN v2.
1 for biogenic emissions, and RETRO for the NMVOC emissions. Following  Philip et al. (2016) , we set the chemical and transport time steps operators at 20 and 10 min, respectively. The year 2010 was used to spin up the model and remove the initial conditions. The simulation extends from 2011 through 2014. The model outputs were saved at a 2-h frequency, and a mass conservative regridding tool (see Section 3. 1. 1 in  Bader et al. , 2017)  has been used to interpolate the simulated profiles from the closest pixel for each of the relevant NDACC stations onto the corresponding SFIT-4 layering scheme. The GEOS-Chem profiles were not smoothed by the FTIR averaging kernels, so that this study will consistently present direct pairwise comparisons involving model, FTIR or IASI data sets at their maximum resolution.
These were the reasons that led the University of Liege team to install an IR grating instrument as early as in the 1950s to record pioneering atmospheric IR spectra  (Zander et al. , 2008). High-resolution FTIR observations started in 1984, and since then, the monitoring program has been ongoing without any noticeable interruption, leading to a multidecadal observational data set covering more than 35 years. In the present study, we use a subset of the spectra recorded with the Bruker 120HR spectrometer, which has been in operation at the Jungfraujoch since the early 1990s.
Line-by-line spectral parameters from the HITRAN 2008 and 2016 compilations (high-resolution transmission molecular absorption database;  Rothman et al. , 2009; Gordon et al. , 2017)  as well as from ATM16 are used (see https://mark4sun. jpl. nasa. gov/ toon/atm18/atm18. html; Accessed 25 January 2021), supplemented with pseudo-linelists (available from https://mark4sun. jpl. nasa. gov/pseudo. html; Accessed 25 January 2021) for the interfering unresolved features of, namely, carbon tetrachloride (CCl 4 ), chlorine nitrate (ClO-NO 2 ), CFC-12 (CCl 2 F 2 ), HCFC-22 (CHClF 2 ), HFC-23 (CHF 3 ), and CFC-113 (CCl 2 FCClF 2 ). The solar lines compilation and model described (and maintained) by  Hase et al. (2006)  are used to fit the nontelluric absorptions present in the selected spectral ranges.
The adopted settings are provided in Table  2. Panel A of Figure  1  shows a typical groundbased IR spectrum encompassing the two PAN candidate windows, delimited by the two shaded areas, denoted W1 (794 cm -1 ) and W2 (1,163 cm -1 ) on the figure and later in the text. This spectrum was recorded at the NDACC Jungfraujoch station on January 9, 2009, with a maximum optical path difference (OPD) of 82 cm at a solar zenith angle of 79. 56. The additional panels show the weak absorber features (B and C), including PAN, and the strong lines (D and E). Since HFC-23 is not part of the WACCM distribution, we adopted as a priori the ACE-FTS profile of  Harrison et al. (2012)  for 2007. For water vapor, we implement day-by-day vertical distributions from ERA-Interim, supplemented with WACCM seasonal data for the high- altitude levels.
A mean (median) column ratio of 1. 77 (1. 74) is computed (+0. 27, 1s), and a close correlation is found, with an R-factor of. 94, a slope of 1. 88, and an intercept of -1. 57 Â 10 14 molec. cm -2. We use this linear relationship to scale the IASI columns to the Jungfraujoch station altitude. Panel B of Figure  4  shows the scatter plot between the scaled satellite columns and the groundbased data, characterized by a slope of 0. 8 (+0. 08; 2s), a correlation coefficient of 0. 77, and an intercept of 7. 03 (+1. 95; 2s) Â 10 14 molec. cm -2. This improved agreement gives credit to the scaling method adopted for the IASI columns. The ground-based and the satellite daily mean time series for coincident days are displayed in Panel C as red and green circles, respectively. This figure confirms a particularly good agreement between the two data sets, which both capture consistent seasonal modulations for this challenging target and a mountainous region, as further confirmed by the gray shaded area that represents the range of the IASI monthly mean total columns and associated +1s standard deviation.
Both the IASI and FTIR data sets suggest that the seasonal modulation captured by GEOS-Chem is underestimated in terms of peak-to-peak amplitude, with simulated data for summertime below the observations, especially in the middle of the year. This is confirmed by a three-term Fourier fitting to the three 2011-2014 time series. Similar seasonal cycles are derived for the FTIR and IASI time series, with peak-to-peak amplitudes (with respect to the mean columns) of 123% and 121%, respectively. This is indeed significantly larger than the 93% amplitude derived from the GEOS-Chem simulation. As to the location of the extrema, the peak values are found at the end of June (FTIR), the beginning of July (IASI), and the middle of July (GEOS-Chem); the minima at the end of November (GEOS-Chem), the middle of December (FTIR), and the end of January (IASI).
Eureka (80 N) A subset of the FTIR spectra recorded at the Polar Environment Atmospheric Research Laboratory (PEARL, Eureka station, Ellesmere Island, Nunavut, Canada) has been analyzed, adopting the settings depicted in Table  2  for W1 (794 cm -1 ). PAN total columns have been retrieved for the 2010-2013 and 2017-2018 time periods, inclusive. The data showing strong vertical profile Overall, a mean total column of 4. 28 Â 10 15 molec. cm -2 is computed, slightly larger than the a priori columns (approximately 3. 8 Â 10 15 molec. cm -2 , mean tropospheric a priori mixing ratio of 0. 26 ppb), varying essentially between 1. 09 and 7. 47 Â 10 15 molec. cm -2 (+2s). A seasonal signal is observed, peaking in April, and showing minimum burden in June. The mean peak-to-peak amplitude evaluated with a three-term Fourier series amounts to 56%, with respect to the average total column.
The monthly mean IASI columns vary between 4 and 7 Â 10 15 molec. cm -2. They show a high bias relative to the ground-based measurements, which likely results from the poor observational conditions or low thermal contrast frequently encountered by IASI in these polar regions. A PAN time series has been extracted for Eureka from our v12. 0. 2 GEOS-Chem full chemistry simulation. A first direct superposition of the model and FTIR data indicates a low bias for GEOS-Chem, with mean fractional differences of 27% + 31% (1s). Better agreement is noticeable around the middle of the year, where minimum columns are found for both the model and the observations. For the years 2010-2013, we have 152 days with FTIR measurements available, and the comparison of the GEOS-Chem and FTIR daily means confirms a significant bias for the larger column measurements.
Quantitatively, the scatter plot shown in Figure  5B  indicates a good correlation (R-factor of. 69), but with a slope of only 0. 32. Note that the statistical results are not significantly different when removing the spin-up year (2010). Although in disagreement most of the time as to the magnitude of the PAN total columns, the seasonal cycles revealed by the observations and the model both show a PAN variation throughout the year above Eureka that is quite different from the one above the Jungfraujoch, with its summer maximum. The FTIR data for 2017 and 2018 confirm the shape of the seasonal cycle and the magnitude of the columns above PEARL. In August 2017, very large PAN columns were retrieved from the ground-based FTIR observations, about 3 times larger than the typical values for that period of the year.
Comparisons with IASI-A and IASI-B data have been conducted in order to confirm these outlying columns. Panel C of Figure  5  portrays as red circles the development of the PAN FTIR columns from August 4 until August 16, daily mean columns close to 4 Â 10 15 molec. cm -2 are observed, before reaching 11. 7 and 10. 6 Â 10 15 molec. cm -2 on August 19 and 20, respectively. The daily averaged satellite measurements between August 4 and 27 are reproduced as continuous lines on Figure  5C , together with the associated +1s uncertainty ranges around the daily means (shaded area). The IASI instruments show mean columns close to 7 Â 10 15 molec. cm -2 , with the exception of a few days characterized by peak columns around August 18, before progressively returning to background abundances, as consistently shown by both ensembles.
Settings adopted for the retrieval of PAN. DOI: https://doi. org/10. 1525/elementa. 2021. 00027. t2 " means that a profile retrieval is performed for the corresponding gas. Otherwise, a simple scaling of the a priori profile is achieved. All a priori vertical distributions are from WACCM (v4), except for HFC-23 and H 2 O (see text). PAN ¼ peroxyacetyl nitrate; WACCM ¼ Whole Atmosphere Community Climate Model; HITRAN ¼ high-resolution transmission molecular absorption database.
Acknowledgments We thank the  International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat (Bern)  for supporting the facilities needed to perform the Fourier transform infrared observations at Jungfraujoch. We thank  Canadian Network  for the  Detection of Atmospheric Change (CANDAC)/Polar Environment Atmospheric Research Laboratory (PEARL)/PAHA PI James Drummond, Canadian Arctic ACE/OSIRIS Validation Campaign PI Kaley Walker, PEARL Site Manager Pierre Fogal, CANDAC Data Manager Yan Tsehtik , the CANDAC operators, and the staff at  Environment and Climate Change Canada's Eureka Weather Station  for their contributions to data acquisition and for logistical and on-site support. We thank  Antarctica New Zealand  for providing support for the measurements at Arrival Heights.
268020496-TRR 172 , within the  Transregional Collaborative Research Center "ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes , and  Feedback Mechanisms  (AC)3" in subproject E02. The  University of Bremen  has further been funded by the  German Ministry of Education and Research (BMBF)  in the project  TROSTRA  ( 01LG1904A ) and the by  Senate of Bremen. We also thank the  AWI Bremerhaven  for logistical support at the AWIPEV station in Ny Ålesund and the personnel of the AWIPEV for their work. Measurements at Arrival Heights are core-funded by the  National Institute of Water and Atmospheric Research Ltd. (NIWA)  through  New Zealand's Ministry of Business, Innovation and Employment Strategic Science Investment Fund. The development of the infrared atmospheric sounding interferometer (IASI) peroxyacetyl nitrate product has been supported by the project  Oxygenated Compounds in the Tropical Atmosphere: Variability and Exchanges  ([OCTAVE], http://octave.
Data accessibility statement The Fourier transform infrared and infrared atmospheric sounding interferometer data sets as well as the Goddard Earth Observing System (GEOS)-Chem time series presented in this work are available on zenodo. org. The attributed DOI is http://doi. org/10. 5281/zenodo. 5111613. The GEOS-Chem model version v12. 0. 2 can be downloaded from https://doi. org/10. 5281/zenodo. 1455215. The SFIT-4 core code is publicly available at https:// github. com/NCAR/sfit-core-code.
9 7. 9 8%-Consistent with Tereszchuk et al. (2013) Line parameters for main interfering species 2. 6 3. 7 Considering HITRAN uncertainties for H 2 O, CO 2 , N 2 O, and O 3 ; consistent with Rinsland et al. (2012) for CCl 4 Temperature 5. 8 4. 5 See text for information on the assumed error patterns Instrumental alignment 0. 2 0. 8 Maximum 10% misalignment Total systematic in quadrature 10. 2 9. 9 Selection of spectral range limits 20. 0 20. 0 Comparison with arbitrarily enlarged windows Total systematic in quadrature 22. 5 22. 3 Spectral range limits impact included Relative standard deviation on the daily 7. 9 4. 7 See text means HITRAN ¼ high-resolution transmission molecular absorption database.
Introduction Surface Realization (SR) is a natural language generation task that consists in converting a linguistic representation into a well-formed sentence. SR is a key module in pipeline generation models, where it is usually the last item in a pipeline of modules designed to convert the input (knowledge graph, tabular data, numerical data) into a text. While end-to-end generation models have been 1 Our code and settings to reproduce the experiments are available at https://gitlab. com/shimorina /tacl-2021. proposed that do away with such pipeline architecture and therefore with SR, pipeline generation models  (Dušek and Jurčíček, 2016; Castro Ferreira et al. , 2019; Elder et al. , 2019; Moryossef et al. , 2019)  have been shown to perform on a par with these end-to-end models while providing increased controllability and interpretability (each step of the pipeline provides explicit intermediate representations that can be examined and evaluated).
Specifically, we explore the hypothesis according to which these metrics, which provide a measure of the SR input complexity, correlate with automatic metrics commonly used in NLG. We find that Dependency Edge Accuracy (DEA) correlates with BLEU, which suggests that DEA could be used as an alternative, more interpretable, automatic evaluation metric for surface realizers. We apply our framework to the results of two evaluation campaigns and demonstrate how it can be used to highlight some global results about the state of the art (e. g. , that certain dependency relations such as the list dependency have low accuracy across the board for all 174 submitted runs). We indicate various ways in which our error analysis framework could be used to improve a model or a dataset, thereby arguing for approaches to model and dataset improvement that are more linguistically guided.
Computing Performance Metrics. We compute sentence-level BLEU-4 with the smoothing method 2 from Chen and Cherry (2014), implemented in NLTK. 6 o compute dependency edge accuracy, we process systems' outputs to allow for comparison with the lemmatized dependency tree of the reference sentence. Systems' outputs were tokenized and lemmatized; contractions were also split to match lemmas in the UD treebanks. Finally, to be consistent with punctuation-less references, punctuation was also removed from systems' outputs. The preprocessing was done with the stanfordnlp library  (Qi et al. , 2018). For human judgments, we collect those provided by the shared tasks for a sample of test data and for some languages (en, es, fr for SR'18 and es ancora, en ewt, ru syntagrus, zh gsd for SR'19). Table  2  shows how many submissions each language received.
Conclusion We presented a framework for error analysis that supports a detailed assessment of which syntactic factors impact the performance of surface realisation models. We applied it to the results of two SR shared task campaigns and suggested ways in which it could be used to improve models and datasets for shallow surface realisation. More generally, we believe that scores such as BLEU and, to some extent, human ratings do not provide a clear picture of the extent to which SR models can capture the complex constraints governing word order in the world natural languages. We hope that the metrics and tools gathered in this evaluation toolkit can help address this issue.
Acknowledgments We are grateful to  Kim Gerdes  for sharing his thoughts at the initial stage of this research project and giving us useful literature pointers, and we thank  Shashi Narayan  for making his tree error mining code available to us. This research project would not also be possible without the data provided by the  Surface Realization  shared task organisers, whose support and responsiveness we gratefully acknowledge. We also thank our reviewers for their constructive and valuable feedback. This project was supported by the  French National Research Agency  ( Gardent ; award  ANR-20-CHIA-0003 , XNLG '' Multilingual, Multi-Source Text Generation '').
, 2012]  by Dana Patchick  [Kronberger et al. , 2016]  from our affiliated 'Deep Sky Hunters' (DSH) amateur astronomer group, and is included in the HASH PN database 2   [Parker et al. , 2016]  as Pa 30. The WISE W3 (11µm) band shows a round nebula, whereas the dominant shape in the W4 (22µm) band is donut-like inside a much fainter halo. Narrow-band [O III] imaging observations obtained by the DSH group on the 2. 1m KPNO telescope in September 2013 indicate a very faint, diffuse, circular emission feature. The nebula is a strong source of diffuse X-ray emission  [Oskinova et al. , 2020]. Multi-wavelength images of the nebula are shown in Figure  1. The bright central star (CS) is both hydrogen-poor and helium-poor, and has a unique emission-line spectrum as shown by Gvaramadze et al. [2019] and from our independent spectroscopy.
Interstellar extinction The neutral hydrogen column density estimates  [HI4PI Collaboration et al. , 2016]  yield an extinction of A V = 2. 24 ± 0. 1 mag. The higher resolution 3-D IPHAS extinction map  [Sale et al. , 2014]  indicates a higher extinction of A 0 = 2. 52 ± 0. 40 mag (monochromatic). We investigated 3-D extinction maps based on the Gaia-2MASS  [Lallement et al. , 2019]  and Gaia-PanSTARRS-2MASS  [Green et al. , 2019]  databases. In the first case  (Lallement et al. ) , we find a colour excess E(B -V ) = 0. 87 and therefore A V = 2. 7 mag. In the latter case  (Green et al. ) , E(g -r) = 0. 70 was found at 2. 3 kpc. E(B -V )/E(g -r) = 0. 884 to 0. 996, therefore A V ranges between 1. 92 and 2. 17 mag. We also selected stars within 10 arcminutes of Pa 30 in the Gaia EDR3 catalogue with parallax uncertainty σ π /π < 0.
Acknowledgements We acknowledge with thanks  Matthias Kronberger  and  Dana Patchick of the Deep Sky Hunters amateur group  for their dedication in discovering new PNe and uncovering Pa 30. We also thank  Dr. Laurence Sabin  ( UNAM ) for designing the GTC/OSIRIS observations on behalf of other team members Q. A. P and A. A. Z. Q. A. P thanks the  Hong Kong Research Grants Council  for GRF research support under grants  17326116  and  17300417. A. R. and F. L. thank  HKU  for the provision of postdoctoral fellowships, while FL also acknowledges funding from  MTA Hungary  ( OTKA  project no. 132406 ). A. A. Z. thanks the  Hung Hing Ying Foundation  for the provision of a visiting professorship at  HKU  and acknowledges funding from the  UK STFC  under grant  ST/T000414/1. M. A. G. was funded under grant number  PGC2018-102184-B-I00  of the  Ministerio de Educación, Innovación y Universidades  cofunded with FEDER funds.
Covid-19 infection recognition is very important step in the fighting against the new pandemic Covid-19. In fact, many methods have been used to recognize the Covid-19 infection including Reverse transcription polymerase chain reaction (RT-PCR), X-ray scan and CT-scan. In addition to the recognition of the Covid-19 infection, CT-scans can provide more important information about the evolution of this disease and its severity. With the extensive number of Covid-19 infections, estimating the Covid-19 percentage can help the intensive care to free up the resuscitation beds for the critical cases and follow other protocol for less severity cases. In this paper, we propose Covid-19 percentage estimation database. Moreover, we evaluate the performance of three Covolutional Neural Network (CNN) architectures which are ResneXt-50, Densenet-161 and Inception-v3.
Unlike the main stream that dealt with Covid-19 recognition and segmentation, this paper addresses the estimation of Covid-19 infection percentage. To this end, we created Per-Covid-19 database, then we used it to evaluate the performance of three CNN architectures with two loss functions and two pretrained models scenarios. In summary, the main contributions of this paper are: • We propose Per-Covid-19 dataset for estimating the Covid-19 infection percentage for both slice-level and patient-level. The proposed database consists of 183 CT-scans with the corresponding slice-level Covid-19 infection percentage which were estimated by two expert radiologists. To the best of our knowledge, our work is the first one who propose a finer granularity of Covid-19 virus presence and solve a challenging task related to exact estimation of Covid-19 infection percentage.
• In order to test some state-of-the art methods, we evaluate the performance of three CNN architectures which are ResneXt-50, Densenet-161 and Inception-v3. For the three CNN architectures, we use two loss functions which are MSE and Dynamic Huber loss. In addition, two pretrained scenarios are investigated. In the first scenario, the pretrained models on ImageNet are used. To study the influence of using pretrained models on medical imaging task, we use the pretrained models on X-ray images. • We make our database and codes publicly available to encourage other researchers to use it as a benchmark for their studies  1. ( Last accessed on May 2 nd 2021).
Per-Covid-19 dataset Our From each CT scan, the radiologists selected the slices that contain clear signs of Covid-19 infection and the ones that do not contain any Covid-19 signs. In our dataset, we kept the slices that have diagnosis agreement of the two radiologists. In summary, from each CT-scan, we can have between 2-45 typical slice images. Figure  1  shows the histogram of number of CT-scans over the number of CT slices. Figure  2  shows examples of slices images with their corresponding Covid-19 infection percentage. For evaluating different machine learning methods, we divided the Per-Covid-19 database into patient-independent five folds, where each patient slices are included into one fold.
In our experiments β decreases from 15 to 1 during the training. MAE = 1 n n ∑ i=1 |y i -ŷi | (4) RMSE = 1 n n ∑ i=1 (y i -ŷi ) 2 (5) PC = ∑ n i=1 (y i -y i ) ( ŷi -ŷi ) ∑ n i=1 (y i -y i ) 2 ∑ n i=1 ( ŷi -ŷi ) 2 (6) Where Y = (y 1 , y 2 ,. , y n ) are the ground-truth Covid-19 percentages of the testing data which consists of n slices and Ŷ = ( ŷ1 , ŷ2 ,. , ŷn ) are their corresponding estimated percentages. For equation 6, y i and ŷi are the means of the ground-truth percentages and the estimated ones, respectively. In addition, we used subject-level metrics which are MAE_subj, RMSE_subj and PC_subj which are defined in equations 7, 8 and 9, respectively. MAE_sub j = 1 s s ∑ i=1 |y s i -ŷs i | (7) RMSE_sub j = 1 s s ∑ i=1 (y s i -ŷs i ) 2 (8) 4/9 PC_sub j = ∑ s i=1 (y s i -y s i ) ( ŷs i -ŷs i ) ∑ s i=1 (y s i -y s i ) 2 ∑ s i=1 ( ŷs i -ŷs i ) 2 (9) Where Y s = (y s 1 , y s 2 ,.