dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We would like to express our gratitude to the following experts for supporting this manuscript through the provision of review and guidance:  Austin Bowden-Kerby ,  Emily Corcoran ,  Alasdair Edwards ,  Jessica Levy ,  Jennifer Loder ,  Joanie Kleypas ,  Jennifer Koss ,  Elizabeth McLeod ,  Phanor Montoya-Maya ,  Buki Rinkevich ,  Francis Staub ,  David Suggett ,  Didier Zoccola ,  Amanda Brigdale ,  Anastazia Banaszak ,  Agnes LePort ,  Tory Chase , as well as members of the  ICRI Ad Hoc Committee  on coral reef restorations, and the leadership team of the  CRC. We thank them for providing their valuable time, knowledge and expertise, continuous trust and exemplary collaboration and professionalism.
Design This study draws data from the InSCI community survey. The InSCI study is a cross-sectional, multi-country survey on the health, function, and social situation of people with SCI living in the community. The first wave of this survey was launched in 2017 and 22 countries participated. The questionnaire was validated in each national language and tested in sub-samples of the target population. One important strength of this survey is that it uses the International Classification of Functioning, Disability and Health (ICF) data frame, ensuring that the questions measure the same aspects in all the countries, which allows comparison between countries.
Data analysis The health-income gap requires two components: (1) a comparable income measure and (2) health outcomes. For the first component, we estimated a new variable that allowed us to compare respondents within and between countries in terms of their relative income position. For the second component, we used the years a person has lived with SCI and a comorbidity index. To estimate the general gap, we implemented a hierarchical model considering the structure of the sample. The models were re-estimated using multiple imputations (see Appendix, table  A4 ). As the results did not show substantial changes, we kept the original data  (Gross-Hemmi et al. , 2017; Outhwaite and Turner, 2007; Buuren and Groothuis-Oudshoorn, 2010). In addition, as a sensitivity analysis, we re-estimated the results using multiple regressions country by country.
Descriptive statistics The InSCI survey included 12,591 participants in 22 countries; excluding missing values in the reported income, the sample counted with 11,530 observations. As we aimed to estimate and compare across countries the health-income gap, we included in the analysis only those countries with observations in almost all income levels. The excluded countries were those that presented clear self-selection in the sample related to the income distribution, yielding 9014 cases (72% of the original data). As we focused on traumatic SCI etiologies (81%), 7272 cases remained after the removal of non-traumatic cases. The final estimations used data for countries including Poland, Germany, France, Switzerland, Spain, Greece, Romania, South Korea, China, and Australia. The sample represented 51% of the original data, with a total of 6445 participants.
Discussion This study uses newly available data on the living situations of people with SCI to measure the health-income gap in 10 countries. Due to the lack of information on mortality data for persons with SCI, we use two health outcomes to estimate the gap: years living with SCI, and a comorbidity index. The results show that, on average, persons in the richest income groups live longer with the injury and have fewer comorbidities. In the case of years living with SCI, the results show that work-related injuries explain most of the gap, showing that the type of job before the accident are the key determinants of the disparities  (Bae et al. , 2019; Wu et al. , 2018). As for the comorbidity index, the steepest gap is displayed by injuries caused by falls, traffic accidents, sports accidents, and leisure injuries.
Limitations There are some limitations that have to be mentioned when considering our results: First, data concerning reported income levels was not collected with our research objective in mind, which resulted in several countries to be excluded. In fact, in some countries, people in specific income deciles were not observed, especially for the highest income groups, which means the income-gap does not include those at the top. This implies that our results are likely to be an underestimation of the health-income gap of persons with SCI. As the survey provided one question related to the household income, the accuracy of the income measurement was difficult to test. Second, as country registries of persons with SCI are almost inexistent, our results cannot be extrapolated to the population level, but they can be used as a reference point to identify factors that determine the gap.
Even though our sample included some countries that displayed a convenience sampling, 73% of our data came from countries that applied random samples  (Gross-Hemmi et al. , 2017). Finally, due to the sample limitations, our study used data on traumatic SCI and excluded non-traumatic causes. As the health-income gap for non-traumatic causes could be more important, as inequalities play a more important role for this group, we think future versions of the survey should make an extra effort to gather data of this group. Excluding non-traumatic causes make our results rather conservative.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  801076 , through the  SSPH + Global PhD Fellowship Program in Public Health Sciences  ( GlobalP3HS ) of the Swiss School of Public Health. This study is part of the  International Spinal Cord Injury (InSCI) Community Survey. InSCI provides the evidence for the Learning Health System for Spinal Cord Injury (LHS-SCI , see also Am J Phys Med Rehabil 2017; 96(Suppl): S23-S34 ). InSCI and the LHS-SCI are efforts to implement the recommendations of  International Perspectives on Spinal Cord Injury (IPSCI, Bickenbach JE , Officer A, Shakespeare T, von Groote P. Geneva: WHO Press; 2013). The members of the  InSCI Steering Committee  are:  Julia Patrick Engkasan  (ISPRM representative),  James Middleton  (ISCoS representative; Member Scientific Committee; Australia),  Gerold Stucki  ( Chair Scientific Committee ),  Mirjam Brach  ( Representative Coordinating Institute ),  Jerome Bickenbach  ( Member Scientific Committee ),  Christine Fekete  ( Member Scientific Committee ),  Christine Thyrian  ( Representative Study Center ),  Linamara Battistella  (Brazil),  Jianan Li  (China),  Brigitte Perrouin-Verbe (France) ,  Christoph Gutenbrunner  (Member Scientific Committee; Germany),  Christina-Anastasia Rapidi (Greece), Luh Karunia Wahyuni (Indonesia) ,  Mauro Zampolini  (Italy),  Eiichi Saitoh  (Japan),  Bum Suk Lee (Korea), Alvydas Juocevicius (Lithuania), Nazirah Hasnan (Malaysia), Abderrazak Hajjioui (Morocco) ,  Marcel W.
During the measurement, the Rx antenna was fixed with a height of 1 m to the ground. The Tx antenna was installed on a rotator and rotated with 720 uniform steps on a circle with a radius of 0. 54 m. In each step, the channel transfer function from 28-30 GHz was swept with 1500 samples in the frequency domain using the VNA. Totally 19 channels between the Rx and the Tx uniform circular arrays (UCAs) at different locations were recorded. The selected data is from the first UCA location, where the distance between Tx and Rx was around 15 m.
III, with κ = 9 for the multivariate distributions, and κ = 6 for the independent marginal models. The proposed model comes out as the better choice for the joint fit for three out of five data-sets, with the multivariate Gaussian performing better for Lille Data and AAU-Outdoor. However, the AIC values for both the joint models are close to each other. It is evident that modeling the random variables independently leads to a significantly poorer fit than either of the joint models, no matter which distribution is chosen. We remark that using more complicated models such as copulas  [26]  to model the dependency structure may lead to a better fit, but could be harder to interpret.
The Q-Q plots of AAU-Outdoor data is representative of what we observed for the other data-sets, therefore we exclude reporting them. We observe that for AAU-Outdoor data, the marginals are well-modeled by both the log-normal and the Gaussian distributions. The fit is similar for both distributions, and it is not apparent which model performs better. As can be seen in Fig. 6 , the marginals in AAU-Outdoor data are very close to being symmetric, which means that the Gaussian fits well. However, for the Lille data, it is evident that the lognormal distribution outperforms the Gaussian in terms of the marginals. The log-normal is able to model the left tail and the center of the distribution very well, but sometimes performs poorly for the right tail. On the other hand, the Gaussian is not able to model either of the tails.
Moreover, the Gaussian assigns non-zero probabilities to quantiles below zero, which is not the case for the data as temporal moments are nonnegative random variables. Hence, the multivariate log-normal is a better choice. Note that a good marginal fit does not imply good overall fit in terms of AIC and vice-versa, as is the case for Lille data. This is simply because the AIC measures a different property of the model which does not require the marginals to fit perfectly. The deviation of the right tail of the data from the fitted marginals is to be expected due to the low number of such extreme points. Such points are in-frequent and could potentially arise due to a number of factors such as noise, interference, measurement conditions, etc. Therefore, we argue that the right tail is not as important to model perfectly, and thus make no adjustment for it.
This is attributed to the contrasting scenarios that the measurements were taken in, along with the use of different equipment, antennas, and measurement settings. We also observe that the raw temporal moments are highly correlated random variables. Marginal distributions for Lille and AAU-Industry data are skewed, while those from other data-sets are more symmetric. We notice a fanning out of the scatter plots on the top-right of all the indoor data-sets, which is not present in the outdoor data. Despite the variability in the data, the proposed model fits the data well, even the skewed ones. There is a high correlation between the raw moments, in particular between m 0 and m 1 , since the basis functions used to compute them in (3) are not orthogonal. This is captured well by the model.
VII. MODEL FIT TO STANDARDIZED MOMENTS We now compare the distribution of the standardized temporal moments obtained from the measurements with those from the proposed model. Mean delay and rms delay spread are computed from the raw temporal moments using (4), while the received power is equal to m 0. Pair-wise scatter plots of P 0 , τ , and τ rms from the data and the proposed joint model are shown in Fig. 7. We also include the samples obtained from independently fitting a log-normal distribution to the standardized moments from the data-sets. The log-normal is chosen as it was the best in terms of AIC amongst the independent models as per Tab. III. Here we exclude the AAU-Industry data as the low number of sample points makes it difficult to make any useful conclusions on the correlation behavior.
ACKNOWLEDGMENT The authors would like to thank  Dr. Carl Gustafson  and  Prof. Fredrik Tufvesson  ( Lund University ) for providing the measurement data. This work is supported by the  Danish Council for Independent Research , grant no. DFF 7017-00265  and performed within the framework of the  COST Action   CA15104 IRACON. Ramoni Adeogun  was supported by grant no. DFF 9041-00146B. Francois-Xavier Briol was supported by the  Lloyds Register Foundation  Programme on Data-Centric Engineering at  The Alan Turing Institute  under the  EPSRC grant  [ EP/N510129/1 ].
We evaluate the efficacy of predicted UPOS tags as input features for dependency parsers in lower resource settings to evaluate how treebank size affects the impact tagging accuracy has on parsing performance. We do this for real low resource universal dependency treebanks, artificially low resource data with varying treebank sizes, and for very small treebanks with varying amounts of augmented data. We find that predicted UPOS tags are somewhat helpful for low resource treebanks, especially when fewer fully-annotated trees are available. We also find that this positive impact diminishes as the amount of data increases. * Lacking yeast-proven bread, a flatbread alternative will suffice, i. e. if you can't get more fully-annotated dependency trees, annotating UPOS tags can still be helpful.
, 2020). Even when more annotated data is available, it isn't clear how useful POS tags are for neural dependency parsers, especially when utilising character embeddings  (Ballesteros et al. , 2015; de Lhoneux et al. , 2017). Work investigating the utility of POS tags typically observe a small increase in performance or no impact when used as features for neural dependency parsers. Smith et al. (2018)  found that universal POS (UPOS) tags offer a marginal improvement for their transition based parser for multi-lingual universal dependency (UD) parsing. Dozat et al. (2017)  also observed an improvement in parsing performance for graph-based parsers when the predicted UPOS tags came from sufficiently accurate taggers. Zhang et al. (  2020 ) only found POS tags to be useful for English and Chinese when utilising them as an auxiliary task in a multi-task system.
Anderson and Gómez-Rodríguez (2020) found that a prohibitively high accuracy was needed to utilise predicted UPOS tags for both graph-and transitionbased parsers for UD parsing. They also obtained results that suggested smaller treebanks might be able to directly utilise less accurate UPOS tags. We evaluate this further by analysing the impact of tagging accuracy on UD parsing in low resource contexts, with regards to the amount of data available to train taggers and parsers.
Methodology We performed three experiments. The first is an evaluation of predicted tags as features for biaffine parsers for real low resource treebanks. It also includes parsers trained with UPOS tagging as an auxiliary task similar to the experiments in  Zhang et al. (2020). The second experiment evaluates the impact of different tagging accuracies on different dataset sizes using artificial low resource treebanks by sampling from high resource treebanks. The last experiment utilises a data augmentation technique to investigate the efficacy of predicted UPOS tags for very small treebanks (∼20 sentences) when arXiv:2106. 04222v1 [cs. CL] 8 Jun 2021 augmented with varying amounts of data.
Low resource data We take all UD v2. 6 treebanks  (Zeman et al. , 2020)  with less than 750 sentences in both its training dataset and development dataset. We cluster these treebanks into two groups, very low with less than 50 sentences and low with less than 750. The very low resource treebanks consist of Buryat BDT (bxr), Kazakh KTB (kk), Kurmanji MG (kmr), Livvi KKPP (olo), and Upper Sorbian UFAL (hsb). The low resource set is made up of Belarusian HSE (be), Galician TreeGal (gl), Lithuanian HSE (lt), Marathi UFAL (mr), Old Russian RNC (orv), Tamil TTB (ta), and Welsh CCG (cy). We combined the training and development data (when available) to then split them 80|20. The statistics for the resulting splits are shown in Table  1. We use the original test data for analysis. Artificial low resource data We use Indonesian GSD (id), Irish IDT (ga), Japanese GSD (ja), and Wolof WTB (wo) to create artificially low resource treebanks.
For the data augmentation experiment we used accuracy bins of 41, 44, 48, and 51. Network details Both the taggers and parsers use word embeddings and character embeddings. The parsers use UPOS tag embeddings except for the MTL setup and the baseline models without tags. The embeddings are randomly initialised. The parsers consist of the embedding layer followed by BiLSTM layers and then a biaffine mechanism  (Dozat and Manning, 2017). The taggers are similar but with an MLP following the BiLSTMs instead. We ran a hyperparameter search evaluated on the development data of Irish and Wolof. This resulted in 3 BiLSTM layers with 200 nodes, 100 dimensions for each embedding type with 100 dimensions for input to the character LSTM. The arc Figure  1 : Impact of tagging accuracy for varying amounts of data for both taggers and parsers using artificial low resource data.
However, taking parsers using only 100 fully-annotated training sentences as a baseline, the average performance using 232 parsed sentences without UPOS tags is over 10 points higher, whereas the increase gained training the taggers with 541 tagged sentences is only 5 points. So it is clear that if time permits such that annotators can increase the number of tree annotations, they will likely prove to be more useful. But UPOS tags could be obtained using projection methods and/or active learning techniques  (Baldridge and Palmer, 2009; Das and Petrov, 2011; Garrette et al. , 2013; Täckström et al. , 2013). Also, multilingual projection methods could be used, but they typically generate trees as well as POS tags  (Agić et al. , 2016; Johannsen et al. , 2016). Figure  2  shows the impact of predicted UPOS accuracy when using data generated with subtree swapping augmentation.
Conclusion We have presented results which suggest that lower accuracy taggers can still be beneficial when little data is available for training parsers, but this requires a high ratio of UPOS annotated data to tree annotated data. Experiments using artificial low resource treebanks highlight that this utility diminishes if the number of samples reaches a fairly small amount. We have also shown that very small treebanks can benefit from augmented data and utilise predicted UPOS tags even when they come from taggers with very low accuracy. Our experi-ments haven't considered pretrained multilingual language models (LMs) which could potentially offset the small benefits of using POS tags. It would be interesting to develop this analysis further by testing whether the implicit information encoded in these LMs are more useful than explicit but potentially erroneous POS tag information.
Acknowledgments This work has received funding from the  European Research Council (ERC) , under the European Union's  Horizon 2020 research and innovation programme (FASTPARSE , grant agreement No  714150 ), from  ERDF/MICINN-AEI (ANSWER-ASAP ,  TIN2017-85160-C2-1-R ), from  Xunta de Galicia  ( ED431C 2020/11 ), and from  Centro de Investigación de Galicia "CITIC" , funded by  Xunta de Galicia  and the  European Union  ( ERDF -Galicia 2014-2020 Program ), by grant  ED431G 2019/01.
Reversal learning Spatial memory was assessed by the Morris water maze test  (D'Hooge & De Deyn, 2001)  in which mice use visual cues to locate an escape platform (9 cm in diameter) in an open circular swimming arena (150 cm in diameter, 40 cm deep) filled with opaque water (Acusol, 20°C ± 1°C). The escape platform was hidden 1 cm below the water surface. Room temperature was kept constant at 24°C and both arena placement and surrounding visual cues were kept fixed during all experiments. Data were acquired by the SMART recording system and tracking software (Panlab). Data processing was automated with NAT (Navigation Analysis Tool), an in-house tool rooted in MATLAB  (Jarlier et al, 2013). Mice underwent a twophase training protocol  (Fig 4E). The first phase consisted of five training days, 1 d of rest followed by probe test 1 (initial learning).
, 2017). The youngest sediment deposited over the entire Kola region throughout MIS 1 (Holocene) includes marine, lacustrine, alluvial, peat and bog sediment  (Niemela et al. , 1993; Kolka and Korsakova, 2005). Here we present high-resolution seismic and echo-sounder data complemented by sedimentological data derived from two sediment cores, Co1410 and Co1411, from Lake Imandra. Our study puts a particular focus on (i) reconstruction of the basin evolution and the history of sediment deposition, (ii) the glacial history and changes in the environmental and climatic conditions, and (iii) the influence of physical mechanisms, such as neotectonics and lake currents, on modes and patterns of deposition.
Seismic and echo-sounder survey In summer 2017, the subsurface of central Bolshaya Imandra was investigated using seismic and echo-sounder systems from an inflatable platform equipped with an outboard engine. In total, ~80 km of seismic and echo-sounder data were acquired along 17 profiles across the lake (Fig. 2 ). High-resolution reflection seismic data were gathered by means of a Micro-GI air gun (2 × 0. 1 L) and a 50-m-long 32-channel digital Geometrics GeoEel streamer, with a group spacing of 1. 5625 m (Table  2 ). The dominant frequency of the seismic signal was between 100 and 500 Hz, resulting in a vertical resolution of a few metres. Seismic data processing was conducted with the Schlumberger Vista software and included geometry assignment, exponential gain correction, band-pass filtering (50-100-1000-1500 Hz), binning at 5 m, normal move out (NMO) correction, stacking and time migration using a constant velocity of 1430 m s -1.
Moreover, surfacerelated multiple elimination (SRME) was tested during processing, but not retained in the final steps as it erased too much of the original signal preventing further interpretation of the data. Sediment echo-sounder data were collected simultaneously using an Innomar SES-2000 compact system operating at 10 kHz. This parametric echo-sounding system provided a vertical resolution of the upper fill at a decimetre scale. The bathymetric data (Fig. 2 ) are partly derived from the echosounder data acquired for this study and complemented by a pre-existing bathymetric map  (Richter and Egorov, 1926)  using the Qgis3 software  (QGIS Development Team, 2020). Interpretation of the seismic and echo-sounder data was performed with the IHS Kingdom software. Time-to-depth conversion of the individual sediment packages was calculated assuming a constant seismic velocity of 1600 m s -1 , which is a typical velocity for water-saturated sediment  (Krastel et al.
Coring Based on the seismic and echo-sounder data pre-processed in the field, two sites were selected, where sediment showed the widest stratigraphic range and longest stratigraphic succession of postglacial sediment at the highest possible resolution. At these sites, sediment successions were retrieved using a gravity corer to recover undisturbed near-surface sediment, and a percussion piston corer system with a 2-m-long core barrel to retrieve deeper sediment (both UWITEC, Austria). Site Co1410 is located in the central western region of Bolshaya Imandra (67°42′56. 76″N, 33°5′6. 42″E, Fig. 1C ) at a water depth of ~23 m. The sediment succession comprises cores from two parallel holes sampled to depths of 7. 92 and 8. 46 m below the lake bottom, respectively, and is described in detail in  Lenz et al.
Results are semi-quantitative estimates of relative concentrations of the detected elements  (Croudace et al. , 2006) , which are derived from the detected peak area intensities given in total counts per second (cps). Post-processing of the data was conducted using the QSpec 6. 5 software (Cox Analytical Systems). Measurements may be influenced by inaccuracies caused by matrix effects due to variations of the core's water content, surface structure, grain size, porosity and organic matter content  (Bertin, 1975; Beckhoff et al. , 2006; Tjallingii et al. , 2007; Hennekam and De Lange, 2012). To compensate for these inaccuracies, the data are standardized to the element aluminium  (Löwemark et al. , 2011). Based on the core description, optical images, magnetic susceptibility values and selected XRF data (Ca counts and Si/Ti ratio) the core segments were merged using the CoreWall Corelyzer 2.