dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion While developing our parsers, training and evaluation were mostly performed using the UDPipe pre-processing baseline with predicted UPOS and MSTAGs but gold tokenization and gold sentence segmentation. For several (bad) reasons, only in the very last days did we train on files tagged with our preprocessing chain. Even later, evaluation (but no training) was finally performed on dev files with predicted segmentation and tokenization, done by either UDPipe or by our pre-processing chains (TAG, TAG+TOK+SEG or TAG+TOK). Based on the results, we selected, for each language and treebank, the best preprocessing configuration and the best parsing model. In general, we observed that neural-based models without features often worked worse than pure feature-based parsers (such as srcat), but great when combined with features.
To summarize, a large majority of 47 selected models were based on DYALOG-SRNN with a rich feature set, 29 of them relying on predicted data coming from our processing chains (TAG or TAG+TOK+SEG), the other ones relying on the tags and segmentation predicted by UDPipe. 10 models were based on DYALOG-MSTNN, with 5 of them relying on our preprocessing chain. Finally, 5 (resp. 2) were simply based on DYALOG-SR (resp. DYALOG-MST), none of them using our preprocessing.
OOV Handling Besides the fact that we did not train on files with predicted segmentation, we are also aware of weaknesses in handling unknown words in test files. Indeed, at some point, we made the choice to filter the large word-embedding files by the vocabulary found in the train and dev files of each dataset. We made the same for the clusters we derived from word embeddings. It means that unknown words have no associated word embed-dings or clusters (besides a default one). The impact of this choice is not yet clear but it should be a relatively significant part of the performance gap between our score of the dev set (with predicted segmentation) and on the final test set. 20.
Impact of the Lexicon We also investigated the influence of our tagging strategy with respect to the UDPipe baseline. Figure  1  plots the parsing LAS F-scores with respect to training corpus size. It also show the result of logarithmic regressions performed on datasets for which we used the UDPipe baseline for preprocessing versus those for which we used the TAG configuration. As can be seen, using the UD-Pipe baseline results in a much stronger impact of training corpus size, whereas using our own tagger leads to more stable results. We interpret this.
Conclusion The shared task was a excellent opportunity for us to develop a new generation of NLP components to process a large spectrum of languages, using some of the latest developments in deep learning. However, it was really a challenging task, with an overwhelming number of decisions to take and experiments to run over a short period of time. We now have many paths for improvement. First, because we have a very flexible but newly developed architecture, we need to stabilize it by carefully selecting the best design choices and parameters. We also plan to explore the potential of a multilingual dataset based on the UD annotation scheme, focusing on cross-language transfer and language-independent models.
Acknowledgments We thank the organizers and the data providers who made this shared task possible within the core Universal Dependencies framework (Nivre et al. , 2016), namely the authors of the UD version 2. 0 datasets (Nivre et al. , 2017), the baseline UDPipe models (Straka et al. , 2016), and of course the team behind the TIRA evaluation platform (Potthast et al. , 2014) to whom we owe a lot.
Algorithmic Complexity and Discrete Approximations To summarize the previous section: we introduced 5 distinct continuous average Straightness measures, allowing to characterize various elements of a spatial graph (vertices, edges and the whole graph). As a reminder, they are described in Table  1  (top 5 rows). In this section, we first derive the algorithmic complexity of these 5 variants. Then, we describe how the two traditional approaches for averaging the Straightness can be considered as discrete approximations of S G (u) (average Straightness between a vertex u and the graph G) and S G (G) (average Straightness over the whole graph), and discuss their complexity. We implemented all our variants of the continuous average Straightness, as well as both discrete approximations, using the R language, and based on the R version of the igraph library  (Csardi and Nepusz 2006).
Acknowledgements The main author would like to thank: the on-going support of  EPSRC  through the  Engineering Doctorate Training Centre  ( EP/ G037426/1 ), the  Dalton Nuclear Institute  and the insight given by his previous and current supervisors at the  University of Manchester  in producing this work. This work was also done as part of a  TSB collaborative project between EDF Energy R&D UK Centre , EDF Energy Generation, EDF R&D in France and the  University of Manchester. The authors gratefully acknowledge EDF Energy Generation for their valuable input data and support. The views expressed in this paper are those of the authors, and not necessarily those of EDF Energy Generation. Technical support for this work from EDF R&D is greatly appreciated.
Because of the extreme sensitivity of the Weibull distribution, small changes in CWS can result in large differences in the probability of extreme events  (Gardiner et al. , 2008). The rationale of ForestGALES and the interplay between the inputs are shown in Figure  1. The diagram in Figure  1  shows that ForestGALES is composed of two main modules: the first one, often referred to simply as GALES  (Gardiner et al. , 2000) , makes use of tree and stand variables to calculate the CWS for breakage and overturning (henceforth: CWS(B, O)). In GALES, tree height and dbh are featured in the calculations of canopy dimensions and aerodynamic properties, as well as being involved in the calculations of the mean wind profile, together with Sph and size of an upwind gap. Soil type and rooting depth are used in species-specific sub-modules to retrieve the values of species-specific coefficients (Creg) of linear regressions of total overturning moment, as measured empirically in the field, against stem weight under different soil types and rooting depths.
For tree height and dbh, we found that our data source was well represented with Gaussian distributions. We have therefore fitted normal distributions to tree-pulling data for the three species used in this paper: for P. sitchensis, we used data from the UK Forestry Commission tree-pulling database (see  Nicoll et al. , 2006) ; for P. pinaster, we combined tree-pulling data from  Cucchi et al. (2005)  with data from a similar species (Scots pine, Pinus sylvestris (L. )), from the UK database, because complete data for P. pinaster was not available. In fact, tree-characteristics such as canopy structure and the architecture of the rooting system are very similar between P. pinaster and P. sylvestris. For E. globulus, we used tree-pulling data used in  Locatelli et al. (2016)  for the parameterisation of ForestGALES for this species.
The lack of variability in Soil type and Rooting depth for the parameterisation of this particular species means that our study cannot evaluate the sensitivity of ForestGALES to these two variables, and of their contributions to the interactions with each other and the other input variables, for E. globulus. We imposed lower bounds to the distributions of tree height (4m) and dbh (3cm), to ensure that the functional limits of ForestGALES were not exceeded. The parameters of the inputs distributions are shown in Table  3. Our proposed approach is data-driven, i. e. it is an exploration of the behaviour and sensitivity of the ForestGALES model from the point of view of the data used for its parameterisations. This ensures that the operational limits of the model are respected as much as possible, while allowing for large amount of data within the probability density functions of the inputs to explore the input space as thoroughly as possible.
Soil type 1 In Figure  2  we show the effect of the correlation between Rooting depth and Soil type (Pearson correlation coefficient: -0. 35; p-value < 0. 001) on the sampling matrix calculated with the copula method. The occurrence of the different Soil types modelled in Figure  2  is representative of the P. sitchensis tree-pulling database used for the calculation of the correlation coefficients. (level: 4) is higher than that the other types of soil. For deep Rooting depth (level: 3), the likelihood of a Soil type "Freely draining" (level: 1) is higher than that of the other types of soil. For medium Rooting depth (level: 2) all Soil types are almost equally likely to be selected with the Quasi-random number generator under the correlation structure of the copula.
Sensitivity analysis of the output: Critical wind speeds In order to investigate what variables the model users should focus on knowing more accurately to maximally reduce uncertainty in the model's predictions of CWS for each species, we applied the SA Factor Prioritisation (FP) setting. Following this setting, data collection of the variables with the largest Si should be prioritised for reducing the uncertainty of the inputs and optimising predictions calculated with ForestGALES. Conversely, to identify those variables which negligibly contribute to the variance of the CWS, we adopted the SA setting Factor Fixing (FF), which is based on S T i. When the S T i of a variable was found to be close to zero, we fixed that variable to different values within its range, and re-ran the simulations with ForestGALES, while maintaining the variation in all the other variables.
In fact, DAMS is the only input variable directly involved in the wind climate model, and logically a non-influential variable for the calculation of CWS(B,O) cannot drive variation in the corresponding Prob(B, O). To investigate the sensitivity of the wind climate module to these variables, we plotted their marginal cumulative distribution functions (CDF) conditional on the behavioural and non-behavioural realisations of the model. We estimated the sensitivity of the model to these variables with Smirnov two-sample (two sided) tests, which determine the significance of the differences between the behavioural and nonbehavioural CDFs  (Saltelli et al. , 2008). In order to investigate the second order interactions between these variables, we used two-dimensional density plots to identify the regions in the bivariate input space that are more likely to result in behavioural or non-behavioural realisations of ForestGALES.
Lastly, for E. globulus, as expected fixing Rooting depth and Soil type would have no average approximation error for CWS(B,O), while fixing Gap would result in a 6% and 8% errors for CWSB and CWSO, respectively. The lack of correlation between Gap size and the other input variables ensures the reliability of this estimate. Because the S T i of Rooting depth, Soil type, and Gap size are practically zero for both CWS, we re-ran the simulations with ForestGALES using the same dataset apart from the values of these variables, which were fixed one at a time to different values within their range. For Rooting depth and Soil type, these are the discrete values in Table  3. For Gap size, we chose 0m, 2 times Tree height, 1000m, and 10 times Tree height. The latter was chosen because ForestGALES contains a trap in its code by which an upwind gap cannot be larger than that.
Probabilities of damage -Sensitivity in the wind climate module As stated in the Methods section, we have set the threshold for the probabilities of damage at 10% (i. e. a probability of 0. 1), to differentiate between behavioural (Prob(B,O) < 0. 1) and non-behavioural (Prob(B,O) > 0. 1) regions. The CDF plot and the 2D density plots throughout this section are based on this categorisation. In the CDF plot, the values of the D-statistic represent the maximum distance between the CDF curves: the larger this statistic is, the further apart the CDF curves are, suggesting that the variable's importance in differentiating between behavioural and non-behavioural realisations of ForestGALES is large We show only the first CDF plot, for ProbB for P. sitchensis, to illustrate the interpretation of the Smirnov test and its D-statistic.
The species differences in the FP setting results pose some questions on the inner workings of ForestGALES for different species. For P. sitchensis, the influence of Tree height is larger than that of dbh for CWSB, while the opposite is true for CWSO. This is surprising since, as seen in Eq. (  1 ) and (  2 ), dbh 3 is involved in the calculation of CWSB, while CWSO is calculated with stem weight, of which Tree height * dbh 2 is a good approximation, as shown by  Gardiner et al. (1997). However, as shown in Table  2  Tree height is involved in a large number of components of CWSB, and actually one more than for CWSO. The FP setting result of dbh being uninfluential for P. pinaster is also surprising. We ascribe this to the fact that the variance of our sample data for dbh was the smallest of the three species (see Table  3 ).
This finding suggests that ForestGALES describes well the effect of the wind climate on the probabilities of damage. In fact, it is well known that in the case of extreme wind storm, the effect of silvicultural practices on tree survival is minor  (Kohnle et al. , 2003) , while for sheltered stands the effect of stand and tree characteristics is more prominent  (Albrecht et al. , 2012). This raises the issue of the availability of accurate wind speed data, which can be obtained only from localised climate stations. While methods to extrapolate spatially the wind speeds exist (e. g. the Wind Atlas Analysis and Application Program (WAsP) used for estimates of wind energy productivity), their extrapolations are not devoid of error, especially in the case of complex terrain or large distances from the data source  (Venäläinen et al.
Evaluation of the performance of our GSA The most important step of variance-based methods of sensitivity analysis is the characterisation of the variables with reliable probability distribution functions. In order to investigate the behaviour and sensitivity of the ForestGALES model, in our study we have adopted a data-driven approach, as we fitted PDFs to Tree height and dbh from available tree-pulling data, to ensure that the model is investigated within the limits of its parameterisations for different species. Large trees are typically under-represented in these field experiments for safety reasons and technical limitations  (Nicoll et al. , 2006) , even more so for data that was gathered in the past  (Fraser and Gardiner, 1967). As a consequence, tall trees are under-represented in the P.
sitchensis and P. pinaster tree-pulling datasets that were used for the PDFs in our study, while short trees with a high taper are common. The high correlation coefficient between Tree height and dbh (P. sitchensis: 0. 54; P. pinaster: 0. 73; pvalues < 0. 001) resulted in our simulated trees for the two conifer species to be quite short and with a high taper. The stability of high tapering trees with regards to wind damage is well known, especially for breakage (e. g. Slodicak and Novak, 2006; Valinger and Lundqvist, 1992). As shown by  Zubizarreta-Gerendiain et al. (2012) , regardless of species trees with a high taper are associated with lower risk of uprooting, a tenet which is often interpreted as trees with larger diameters in the lower stem having likely allocated more resources in the formation of extensive root systems  (Nicoll and Ray, 1996).
Our study shows that the GSA method of  Kucherenko et al. (2012)  is very sensitive to the correlations between variables in the correlation matrix of the copula. Therefore, it requires an accurate characterisation not only of the PDFs of the inputs, but also of their initial correlation matrix. This is important, to ensure that the generated dataset used for the SA is representative of the original sample's structure. We described Rooting depth and Soil type with uniform discrete distributions in order to explore the input space more thoroughly, but we did impose a correlation based on P. sitchensis data. This might not be representative of P. pinaster, and might have marginally influenced our results for this species. Gap size is the variable that is most likely to have been poorly characterised using the range of values in our simulations.
Correlation matrices for the ForestGALES input variables for P. sitchensis. Dbh: diameter at breast height; Sph: stems per hectare; Gap size and DAMS are not shown because uncorrelated to the other variables. Correlations involving the variables: Sph, Rooting depth, and Soil type, are calculated from P. sitchensis data from the UK Forestry Commission's tree-pulling database, and applied to the other two species. a The significance of the correlation between pairs of variables with Pearson's correlation coefficient > 0. 3 (thus retained in the calculation of the copula) is large (all p-values < 0. 001).
Acknowledgements This work was funded with the support of the  Natural Environment Research Council (NERC , Grant numbers  NE/I022183/1  and  NE/J019720/1 ), the  European Commission   FORRISK  project (Interreg IV B SUDOE 2007-2013), and an  INRA  package awarded to  Barry Gardiner. It benefited from discussions with many colleagues including  Bruce Nicoll ,  Stephen Bathgate , and  Sophie Hale  from  Forest Research. We are grateful to ForestRe's contribution to the  NERC-CASE studentship of Tommaso Locatelli.
Cerebellar volume abnormalities have been often suggested as a possible endophenotype for autism spectrum disorder (ASD). We aimed at objectifying this possible alteration by performing a systematic meta-analysis of the literature, and an analysis of the Autism Brain Imaging Data Exchange (ABIDE) cohort. Our meta-analysis sought to determine a combined effect size of ASD diagnosis on different measures of the cerebellar anatomy, as well as the effect of possible factors of variability across studies. We then analysed the cerebellar volume of 328 patients and 353 controls from the ABIDE project. The meta-analysis of the literature suggested a weak but significant association between ASD diagnosis and increased cerebellar volume (p=0. 049, uncorrected), but the analysis of ABIDE did not show any relationship.
Data abstraction. For each selected article we collected means and standard deviations of cerebellar region volumes or areas in order to calculate Cohen's d effect size. When there were more than one ASD or healthy control group, we reconstructed the mean () and standard deviations () of the merged group from the means ( ! ), standard deviations ( ! ) and numbers of subjects ( ! ) of the subgroups: 𝑥 = ! ! ! ! ! , 𝑠 = ! ! !! ! ! ! !! ! !!! ! ! !!!. We also collected mean age and Intelligence Quotient (IQ) for analysing their impact on volume or area differences using meta-regression. When such means were not reported, we approximated them from mid-ranges or textual descriptions. When the volume data were provided for two separate regions, we estimated the mean and the standard deviation of the combined region using the following formulae: 𝑥 + 𝑦 = 𝑥 + 𝑦, 𝑠 !!! = 𝑠 ! ! + 𝑠 ! ! + 2 • 𝜌 • 𝑠 ! • 𝑠 ! , where  is the assumed correlation coefficient between the two regions.
Analysis of ABIDE We analysed the MRI data made available by the ABIDE (Autism Brain Imaging Data Exchange) project, which includes 539 brain MRI of individuals with ASD and 573 brain MRI of controls. Cerebellum volumes were automatically segmented using FreeSurfer 5. 1. We developed a tool for FreeSurfer segmentations to visually check the quality of the segmentation for each subject (Figure  1 , https://github. com/r03ert0/QCApp-Cb). We kept only the subjects for whom the segmentation quality was clearly good. Some MRIs with minor segmentation errors could have been manually corrected, however, because of their number we chose to exclude them from the present analyses. Our quality control focused on the cerebellum, but we combined its results with our previous quality control of the whole brain  (Lefebvre et al.
The first model included group, age, IQ, scanning site, sex and brain volume as fixed effects; the second model included in addition the interactions of group with age, IQ, scanning site, sex and brain volume. Because these statistical models are not the same as those used in the meta-analysis of the literature, we also analysed the ABIDE data using the same meta-analytical approach as for the literature. In the metaanalysis of ABIDE the volume estimations of each site were combined using a random effects model, where each * no measure on cerebellum: n=7, mean or SD not retrievable: n=16, no typical control group: n=7, no autism group n=1, review or meta-analysis: n=5, subjects included in another study: n=10 Volumes: Cb, cerebellum; WM, cerebellum white matter; GM, cerebellum grey matter Mid-sagittal areas: whole vermis and its lobules numbered (I-V, anterior; VI-VII, superior-posterior; VIII-X, inferior-posterior).
Discussion Neuroanatomical diversity appears to capture a substantial proportion of the risk to ASD  (Sabuncu et al. 2016 ). However, and even though several candidate neuroanatomical biomarkers have been proposed, it is not yet clear which are exactly the neuroanatomical traits that more strongly influence diagnosis. In this report we looked at one specific structure, the cerebellum, that has been widely discussed in the literature. We performed a meta-analysis of the literature and an analysis of the data from the ABIDE project. The meta-analysis of the literature did not show conclusive evidence for a difference between individuals with ASD and controls neither for the total cerebellar volume nor for its subregions. Whole cerebellum and cerebellar white matter volumes appeared slightly larger in ASD whereas vermal lobules VI-VII area was found to be slightly smaller in ASD, but the significance of these results would not survive correction for multiple comparisons.
e. , whole cerebellar volume, lob I-V, lob VI-VII and lob VIII-IX, see table  2 ). Heterogeneity may be due to a combination of technical and physiological causes, for example, differences in MRI equipment, acquisition sequences, segmentation protocol, but also the age of the subjects, IQ distribution, etc. We analysed the effect of 2 of these factors, age and IQ, using meta-regression. First, our analysis did not reveal a differential effect of age or IQ on cerebellar volume for patients and controls. Second, residual heterogeneity was still statistically significant for 3 out of the 7 regions studied (whole cerebellar volume, lob VI-VII, lob VIII-IX). This indicates that sources other than age and IQ level may be causing significant heterogeneity in the literature. The ABIDE project data provides a very interesting point of comparison for previous findings in the literature.
The subjects in ABIDE come in most cases from research projects that had already been published, and should be then of similar characteristics as those in our literature meta-analysis. However, because the raw MRI and behavioural data is available, there is no issue of publication bias or p-hacking having an effect on the cohort selection. Additionally, the availability of raw data makes it possible to run methodologically homogenous analyses: same segmentation protocol, quality control procedures, and statistical analyses. There still remain, of course, many additional sources of heterogeneity due to the grassroots nature of the project (some of them being currently addressed in ABIDE II through an important harmonisation effort). Overall, the analyses of ABIDE data should provide a more precise, less heterogeneous estimation.