dataesr/scientific-paragraphs-categorization huggingface dataset:
Revelle Factor R The Revelle factor, R, characterizes the capacity of the carbonate system to take up more CO 2. In detail, it relates the relative change in pCO 2 to the associated relative change in C T. Egleston et al. (2010)  provide an explicit formula to calculate R from the state of the carbonate system, given by the combination of any two carbonate system variables. A higher R means that a larger change in pCO 2 is needed to actually result in a respective change in the ocean's carbon content C T , i. e. , the oceans CO 2 uptake capacity is lower. R = pCO 2 pCO 2 C T C T With CONTENT, we can derive C T and A T from any Argo-O 2 profile or any other set of CTD-O 2 data, thus largely expanding the number of R estimates compared to using carbonate system data alone. It should be stressed that similar global (bulk) statistics between methods do not imply that they give the same result.
Variable In particular, the increase in bias toward the surface (upper 500-1,000 dbar) seen for all methods is accompanied by a higher local uncertainty for CANYON-B and CONTENT, leading to a smaller exceedance fraction compared to CANYON. In addition, CANYON Si(OH) 4 shows a considerable increase in | | > σ in intermediate waters, i. e. , a higher fraction of data with high bias that is not accompanied by an elevated uncertainty. LIR estimates of NO 3 , pH, and A T show a comparable bias to CANYON-B and CONTENT. However, their uncertainty is of a different character and shows a stronger decrease with depth. For NO 3 , LIR uncertainties are higher than CANYON-B uncertainties above 4,000 dbar, while for pH they intersect around 1,500 dbar. Except for very deep pH (>4,000 dbar), the two methods show a comparable | | > σ distribution for NO 3 and pH.
This is not entirely surprising, as CANYON-B uses an ensemble of neural networks for each variable (compared to a single "best-performing" one for CANYON) as well as other techniques to better assess uncertainty of the models  (Blundell et al. , 2015; Gal and Ghahramani, 2016). Their bulk mean biases are comparable and close to zero. The crucial difference lies in (1) in a higher fraction of correct estimates in the right place and (2) better knowing when this may not be the case, i. e. , having a more realistic, adapted uncertainty: With a CANYON-B uncertainty that is in general smaller than for CANYON, the fraction of validation data that are inside these bounds is at least as high as for CANYON, but mostly larger (Figures  3, 4 ). This is a clear performance gain of CANYON-B. The difference of CONTENT with respect to CANYON-B is that it regards the entire carbonate system, not just one of its variables.
The pH estimate thus has a major influence on CONTENT pCO 2. A bias in pH or systematic issues with the carbonate system equilibrium constants would impact CONTENT pCO 2 , for which we see a small offset to CANYON-B pCO 2 (order 5 µatm). CANYON-B pCO 2 is most consistent with its training data, i. e. , pCO 2 calculated from A T and C T , while CONTENT pCO 2 is consistent with pCO 2 calculated from all carbonate system variables (Figure  3 ). These small inconsistencies as well as the alignment of pH the different types of observations are a pressing issue, which is hopefully resolved soon. pCO 2 Profiling Float Data The depth-time evolution of pCO 2 float sensor data  (Fiedler et al. , 2013)  and CANYON-B or CONTENT pCO 2 (Figure  5 ) give a similar picture as for the bottle data: profile structure (e.
They can thus extend observations and provide proxies for seasonal variability, even if their CANYON-B/CONTENT pCO 2 estimate itself may be of lower accuracy than direct observations, and thus help to fill spatial gaps in SOCAT data (e. g. , in particular in the Southern hemisphere; Figure  9 ). The CANYON-B/CONTENT surface pCO 2 estimation presented here thus represents an actual extension of the surface pCO 2 database. This is relevant for global CO 2 flux estimation efforts such as the Surface Ocean CO 2 Mapping intercomparison (SOCOM;  Rödenbeck et al. , 2015). However, only variability observed by WOCE/CLIVAR/GO-SHIP can be reproduced by CANYON-B/CONTENT at present. With large gaps in surface pCO 2 observations in the Southern Ocean and during winter, the prospect to fill these gaps through mapping methods such as CONTENT pCO 2 is promising, but is still associated with quite large uncertainties.
ACKNOWLEDGMENTS We are extremely grateful to the enormous efforts that went into GLODAPv2 (Key et al. , 2015;Olsen et al. , 2016), as well as the underlying cruises and would like to thank all the scientists and crew involved. Argo data were collected and made freely available by the  International Argo Program  and the national programs that contribute to it. The Argo Program is part of the Global Ocean Observing System. We would like to acknowledge Williams et al. (2017)'s work in particular that was performed within  U. S. NSF's Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM)  project under the  NSF  Award  PLR-1425989  with additional support from  NOAA  and  NASA. The  Surface Ocean CO 2 Atlas (SOCAT)  is an international effort, endorsed by the  International Ocean Carbon Coordination Project (IOCCP) , the  Surface Ocean Lower Atmosphere Study (SOLAS)  and the  Integrated Marine Biogeochemistry and Ecosystem Research program  (IMBER), to deliver a uniformly qualitycontrolled surface ocean CO 2 database.
Photogrammetry and GIS approaches can be used to characterize reproductive units within red coral populations by studying family relationships by combining spatially explicit positions with genetic analysis  [24]. Contrary to traditional methods used in marine biology, which can be destructive and difficult to be implemented, photogrammetry is non-invasive and can be performed in situ  [25]. Moreover, the emergence of powerful photogrammetry processing software, like PhotoScan  [26] , has changed the way to handle these studies. This approach is now more user-friendly for non-specialist researchers  [19]. Finally, photogrammetry is being perceived as a useful and affordable approach for an increasing number of research teams. See, for example, Refs. [27] [28] [29]  or the study of structural complexity of underwater seafloor  [30] [31] [32].
GIS software is also coupled with photogrammetry in  [33]  for a deep coral survey. Some stereo tools, diver-operated or embedded in an AUV, have been developed since some years ago, in the field of underwater robotics in order to easily survey a site using photogrammetry with specific methods to manage the stereo systems and local bundle adjustment. These developments were intended to support inclusion of bathymetry in an underwater archeology context as well as in marine biology (see  [32, [34] [35] [36] ). Similarly, we have also developed our own low-cost system, which is easier to use in complex environments  [37]. The system is based on embedded and distributed low-power mini computers (Raspberry Pi). A set of software programs developed by our team ensures a general good precision with local bundle adjustment and real-time odometry.
Setting Up the Quadrat The quadrat is a homemade 3D printed plastic frame that can be easily made by anyone that has the 3D mesh file. This mesh file is designed to comply with the standard used by biologists. The frame's internal dimensions were fixed to 200 × 200 mm. The external dimensions were chosen to ensure a good visibility of the targets: 230 × 270 × 15 mm. It incorporates two kinds of coded targets. First, 18 classical circular targets used by Photoscan and Photomodeler software-second, 18 square targets from the TagRuler system, which we have developed for our own photogrammetric platform ARPENTEUR  [7]. The presence of these two kinds of targets enables processing the photos both in the commercial software and in our own photogrammetry modules. The 18 targets of each type are evenly distributed on the frame.
Photogrammetric Acquisition The classical photogrammetric approach is achieved by shooting hundreds of photos, with a high overlap. As said previously, the density of large colonies multiplies hidden areas. A photogrammetric acquisition relies on the redundancy of data, ensured by the redundancy of the areas' shots. The more complex the colony, the more photos we should take and the less missing data we get in the reconstruction process. Moreover, red coral colonies grow in dark and narrow areas, like cave ceilings, which present a complex topography. This makes the photogrammetric acquisition process challenging and time-consuming. In a practical way, it is generally performed by a scuba diver. We tested both HD (Olympus TG2 and Canon G1) and reflex cameras (Nikon D700 and Nikon D300). The whole process enables computation of the 3D point cloud, and, next, the mesh based on the dense cloud.
• Depth map computation: A brute force stereo points matching is performed among neighboring micro-lenses. The neighborhood is an input parameter that defines the maximum distance on the MLA between two neighboring micro-lenses. This process can be easily run in parallel. This is achieved using RxLive software that relies totally on the GPU. The total processing time does not exceed 400 milliseconds per image. Next, using the computed calibration parameters, each matched point is triangulated in 3D in order to obtain a sparse point cloud.
e. , revert Henneberg steps of type 1), and compensate by multiplying the final Laman number by 2 for each removed vertex. Another optimization consists in identifying the largest tetrahedral subgraph, i. e. , the largest subgraph that can be constructed by Henneberg steps of type 1, starting from a triangle. This subgraph is considered when fixing some vertices of the graph, in order to deal with rotations and translations. Then we call the fast FGb  [11]  implementation of Gröbner bases in Maple, for determining the number of solutions of the constructed polynomial system. Executing this program once for all 747065 graphs took about 162 days of CPU time, using Xeon E5-2630v3 Haswell 2,4Ghz CPUs. However, the computations were run in parallel so that the result was obtained after a few days.
Acknowledgments G. Grasegger  and  C. Koutschan  were partially supported by the  Austrian Science Fund (FWF) :  W1214-N15 , project  DK9. C. Koutschan  was also supported by the  Austrian Science Fund (FWF) :  F5011-N15. E. Tsigaridas is partially supported by  ANR  JCJC  GALOP  ( ANR-17-CE40-0009 ).
L = 1/2, the simulations are performed between t 0 = 0 and t 1 = 1 for different values of the time step δt. Fig. 1 , page 9, shows the solution at t = t 0 and t = t 1. The code is implemented in Fortran 90 and it is run under a 64-bit Linux operating system on a 8-core processor Intel R Core TM i7-7820HQ at a frequency of 2. 9GHz and with 32 GB of RAM. The sparse matrices resulting from the finite element approximation are inverted using a solver provided by the library MUMPS  [51] ,  [52]. According to Fig. 2 , page 10, all the three temporal schemes provide results with approximately the same level of accuracy with a spatial convergence rate of 2 computed with the global L 2 -Biomath 7 (2018), 1812037, http://dx. doi. org/10. 11145/j. biomath. 2018. 12. 037 Fig. 2. Convergence of the solution with respect to the mesh size: plot of the total L 2 -error computed between t = 0 and t = 1 with and without the splitting technique for the explicit Euler scheme (θ = 0), the implicit Euler scheme (θ = 1) and the Crank-Nicolson scheme (θ = 1/2) for δt = 5 × 10 -5.
VI. CONCLUSION In this work, a discontinuous Galerkin approximation method based on unstructured mesh combined with operator splitting has been described, implemented and tested, to solve an advectiondiffusion-reaction equation used to model the growth of root systems. The code has been validated in a simple test case for which an analytic expression of the solution is known. The computations showed that the method convergences with a convergence rate of two in space with P 1 -finite elements. A convergence rate of one and two in time were obtained for respectively the implicit Euler scheme and the Crank-Nicolson scheme both with and without the splitting technique. The computations of those convergence rates required the use of fine mesh grids. For the explicit Euler scheme, such fine mesh computations were not performed since they require really small time steps to fulfill the CFL condition, resulting in huge additional computational cost.
The final consensus coverage was 95. 6% of the reference (minus a 1,000 bp fragment of the control region) with a mean coverage depth of 124x (Accession Number MH010393). In order to verify our MITObim consensus sequence, we also attempted to build a consensus using an iterative BWA mapping approach. Collapsed reads were mapped to the Episoriculus fumidus (NC_003040) mitochondrial genome using the BWA options mentioned above, and a 75% majority consensus was generated using Geneious where the reference sequence was used for sites with coverage depth < 3x. The newly generated consensus was then used as reference for a subsequent round of mapping, and this process was iterated until no additional reads were mapped. After 27 iterations, we were able to map 20,926 unique Nesiotites reads (86. 3% of reference covered, mean coverage depth of 119x), and a final consensus sequence was generated as above (75% majority, calling nucleotides with read-depth ≥ 3x).
Results The MrBayes analysis of our molecular dataset (Fig. S2 ) yielded a tree that was generally consistent with previously published phylogenies based exclusively on mitochondrial data (e. g. ,  He et al. , 2010; Ohdachi et al. , 2006  suggests the genus Episoriculus is polyphyletic, as already observed by other authors  (He et al. , 2010; Ohdachi et al. , 2006; Willows-Munro and Matthee, 2011). The addition of morphological data to our MrBayes analysis did not greatly alter the topology and support values compared to the molecular analysis alone (Fig. 2 ). However, we did recapitulate a monophyletic Neomys (PP=0. 96), and confirm that N. and A. gibberodon (PP=0. 93) are sister-taxa, with S. nigrescens as their immediate outgroup (PP=0. 93). Our BEAST analysis (Fig. 3.
http://www. bioinformatics. babraham. ac. uk/projects/fastqc). Using Sabre v. 1. 0 (http://github. com/najoshi/sabre) we filtered for samples containing the correct P5 5mer barcode sequence (allowing one mismatch). Adapter sequences were trimmed with AdapterRemoval v. 2. 1. 7 (Schubert et al. , 2016) using the following parameter values: mismatch rate 0. 1, minimum Phred quality 4, quality base 33, trim ambiguous bases (N), and trim bases with qualities equal or smaller than the given minimum quality.
Acknowledgements Thanks are due to  Violaine Nicolas  and the  Vertebrate Collections of the Muséum National d'Histoire Naturelle of Paris (France)  for allowing the morphological character analysis of specimens of Anurosorex squamipes, Soriculus nigrescens,  Nectogale elegans , and Chimarrogale himalayica. This work was supported by a  Marie Curie International Outgoing Fellowship  within the  7th European Community Framework Programme  (project  MEDITADNA ,  PIOF-GA-2011-300854 ,  FP7-PEOPLE)  ( Pere Bover ) and a  Marie Curie Fellowship  ( MCA-IEF PIEF-GA-2013-629604 ) ( Juan Rofes ). Gloria Cuenca  and  Juan Rofes  are members of the  Atapuerca  Project  CGL2015-65387-C3-2-P (MINECO/FEDER ). The research is also included in Research Project  CGL2016-79795 , [" Cambios holocénicos en la biodiversidad animal de las islas de la Macaronesia y de Baleares.
( http://www. bioinformatics. babraham. ac. uk/projects/fastqc). Using Sabre v. 1. 0 (http://github. com/najoshi/sabre) we filtered for samples containing the correct P5 5mer barcode sequence (allowing one mismatch). Adapter sequences were trimmed with AdapterRemoval v. 2. 1. 7 (Schubert et al. , 2016) using the following parameter values: mismatch rate 0. 1, minimum Phred quality 4, quality base 33, trim ambiguous bases (N), and trim bases with qualities equal or smaller than the given minimum quality.
, 2018) ; • GxG -Cross-Genre Gender Prediction. This task addresses the problem of gender prediction across different textual genres. Specifically, given a collection of texts from a specific genre, the gender of the author has to be predicted as either female or male. A dataset from different genres is distributed to the participants and gender prediction has to be done either (i) using a model which has been trained on the same genre, or (ii) using a model which has been trained on anything but that genre  (Dell'Orletta and Nissim, 2018).
Hate Speech • AMI -Automatic Misogyny Identification. This task focuses on the automatic identification of misogynous content both in English and in Italian languages in Twitter. More specifically, it is a two-fold task. It includes: (i) a Misogyny Identification subtask consisting in a binary classification of tweets as being either misogynous or not; (ii) a Misogynistic Behaviour and Target Classification subtask aimed at classifying tweets according to different finer-grained types of misogynistic behaviour detected, such as sexual harassment or discredit, and the target of the message (individuals or group of people). (Fersini et al. , 2018a) ; • HaSpeeDe -Hate Speech Detection. This task is organized into three sub-tasks, concerning: (i) the identification of hate speech on Facebook (HaSpeeDe-FB), (ii) the identification of hate speech on Twitter (HaSpeeDe-TW), and (iii) the cross-dataset setting concerning the assessment of the performance of the hate speech recognition system developed, i.
Fostering Reproducibility and Cross-community Engagement Open access to resources and research artifacts, such as data, tools, and dictionaries, is deemed crucial for the advancement of the state of the art in scientific research. Accessibility of resources and experimental protocols enable both full and partial replication of studies in order to further validate their findings, towards building of new knowledge based on solid empirical evidence. To foster reproducibility and encourage follow-up studies leveraging the resources built within EVALITA 2018, we introduced two novelties this year. First of all, we intend to distribute all datasets used as benchmark for the tasks of this edition. To this aim, we have set up a repository on Github 5 , in line with the good practices already applied by the organizers of the previous edition 6.
Also, the datasets for all the tasks will be hosted and distributed by the European Language and Resources Association (ELRA). In addition, we decided to further encourage the sharing of resources by making availability of the systems an eligibility requirement for the best system award (see Section 4). In the same spirit, we encouraged cross-community involvement in both task organization and participation. We welcomed the initiative of the organizers of AMI, the Automatic Misogyny Identification task  (Fersini et al. , 2018a) , focusing on both English and Italian tweets. This task has been proposed first at IberEval 2018 for Spanish and English  (Fersini et al. , 2018b) , and then re-proposed at Evalita for Italian, and again for English with a new dataset for training and testing. The ITAmoji shared task was also a re-proposal for the Italian language of the Multilingual Emoji Prediction Task at International Workshop on Semantic Evaluation (SemEVAL 2018)  (Barbieri et al.
, 2018) , which focused on English and Spanish. Here the re-proposal of the task at Evalita was driven by twofold aim to widen the setting for cross-language comparisons for emoji prediction in Twitter and to experiment with novel metrics to better assess the quality of the automatic predictions, also proposing a comparison with human performances on the same task. In the 2016 edition task organisers were encouraged to collaborate on the creation of a shared test set across tasks  (Basile et al. , 2017). We were happy to observe that also this year this practice was maintained. In particular, a portion of the dataset of IronITA  (Cignarella et al. , 2018) , the task on irony detection in Twitter, partially overlaps with the dataset of the hate speech detection task (HaSpeeDe)  (Bosco et al.
Aspect-based Sentiment Analysis (ABSA) is an evolution of Sentiment Analysis that aims at capturing the aspect-level opinions expressed in natural language texts  (Liu, 2007). At the international level, ABSA was introduced as a shared task at SemEval, the most prominent evaluation campaign in the Natural Language Processing field, in 2014 (SE-ABSA14), providing a benchmark dataset of reviews in English  (Pontiki et al. , 2014). Datasets of computer laptops and restaurant reviews were annotated with aspect terms (both fine-grained, e. g. "hard disk", "pizza", and coarse-grained, e. g. , "food") and their polarity (positive or negative). The task was repeated in SemEval 2015 (SE-ABSA15) and 2016 (SE-ABSA16), aiming to facilitate more in-depth research by providing a new ABSA framework to investigate the relations between the identified constituents of the expressed opinions and growing up to include languages other than English and different domains  (Pontiki et al.
, 2015; Pontiki et al. , 2016). ABSITA (Aspect-based Sentiment Analysis on Italian) aims at providing a similar evaluation with respect to texts in Italian. In a nutshell, partic-ipants are asked to detect within sentences (expressing opinions about accommodation services) some of the aspects considered by the writer. These aspects belongs to a close set ranging from the cleanliness of the room to the price of the accommodation. Moreover, for each detected aspect, participants are asked to detect a specific polarity class, expressing appreciation or criticism towards it. During the organization of the task, we collected a dataset composed of more than 9,000 sentences and we annotated them with aspects and polarity labels. During the task, 20 runs were submitted by 7 teams comprising 11 individual participants.
Dataset The data source chosen for creating the ABSITA datasets is the popular website booking. com  3. The platform allows users to share their opinions about hotels visited through a positive/negative textual review and a fine-grain rating system that can be used for assigning a score to each different aspect: cleanliness, comfort, facilities, staff, value for money, free/paid WiFi, location. Therefore, the website provides a large number of reviews in many languages. We extracted the textual reviews in Italian, labeled on the website with one of the eighth considered aspects. The dataset contains reviews left by users for hotels situated in several main Italian cities such as Rome, Milan, Naples, Turin, Bari, and more. We split the reviews into groups of sentences which describe the positive and the negative characteristics of the selected hotel.
The reviews have been collected between the 16th and  the 17th of April 2018 using Scrapy 4 , a Python web crawler. We collect in total 4,121 distinct reviews in Italian language. The reviews have been manually checked to verify the annotation of the aspects provided by booking. com, and to add missing links between sentences and aspects. We started by annotating a small portion of the whole dataset split by sentences (250 randomly chosen sentences) using four annotators (the task organizers) in order to check the agreement of the annotation. For the ACD task, we asked the annotators to answer straightforward questions in the form of "Is aspect X mentioned in the sentence Y ?" (Tab. 1). The set of italian aspects is the direct translation of those booking. com: PULIZIA (cleanliness), COMFORT, SERVIZI (amenities), STAFF, QUALITA-PREZZO (value), WIFI (wireless Internet connection) and POSIZIONE (location).