dataesr/scientific-paragraphs-categorization huggingface dataset:
Distantly supervised neural approaches In Table  3 , we illustrate the results obtained with the neural models pre-trained on the SQuAD-T dataset and deployed on the target dataset without fine-tuning. We refer to this approach as distantly supervised. We show the concat architecture coupled with the point-wise training, and the siamese architecture coupled with the pair-wise training as they prove to be the best performing combinations. We also compare how the representation (w2v or USE) affects the performance. Our first observation is that the USE representation outperforms the w2v representation, except on the TrecQA dataset with the pair-wise/siamese approach. Comparing the pair-wise/siamese and point-wise/concat approaches, we observe that the first one performs better on the CQA dataset, while the second performs better on the QA (WikiQA and TrecQA) datasets.
The difference is however smaller on the QA datasets, with respectively -0. 8 and -0. 01 points for WikiQA and TrecQA, compared to the pointwise/concat. As a consequence, it is safe to recommend the point-wise/concat architecture when no assumption is made on the target dataset. Overall, the obtained performance is superior to the fully unsupervised approaches on WikiQA and Semeval, while the Hungarian method is better for TrecQA and Yahoo.
Few labels in the target dataset In Figure  1  and Figure  2 , we show the learning curve of our methods when we increase the amount of training data, from 4 questions to the full training dataset. The line represents the mean MAP over 10 runs, while the shaded area represents the standard deviation. In Figure  1 , we illustrate the performance of two architectures, siamese and concat, combined with the two training approaches, point-wise and pair-wise, on the WikiQA dataset. We observe that the concat architecture, both with point-wise and pair-wise training, gives a low performance with few questions and the curve steeply rises. The siamese architecture starts at a rather high performace level, especially with the pair-wise learning scheme. The curve is rather flat compared to the one obtained with the concat architecture.
This is most likely due to a lower number of parameters compared to the concat architecture and a fixed similarity function. Overall, the pair-wise training gives better performance than the point-wise one. We note that with the full training set the pair-wise/siamese and point-wise/concat give similar performance. We observe the same trends on the other datasets, except on Semeval. For the latter, the concat architecture performs better, and all the approaches or architectures are above the unsupervised baseline for any number of questions. In Figure  2 , we show the effect of the pre-training on the performance on the WikiQA, Semeval, and Yahoo datasets. We do not display the results on the TrecQA dataset for a matter of clarity, but we observe a behaviour that is similar to that on the WikiQA dataset.
All labels in the target dataset In Table  4 , we report the results obtained with the full training set. We show the concat architecture coupled with the point-wise training and the siamese architecture coupled with the pair-wise approach as they prove to be the best performing combinations. [9]  and  [10]  for WikiQA,  [23]  and  [9]  for Semeval,  [24]  and  [25]  for TrecQA, and  [26]  and  [6]  for Yahoo. Overall, the best results are obtained with the pointwise/concat method, as the pair-wise/siamese approach only performs best on the WikiQA dataet, and with a small margin (0. 3 points).
Discussion In this paper, we explored several approaches for the answer selection task on four datasets and with a varying amount of labeled examples. We provided guidelines on the method to choose depending on the availability of annotations and external resources. We designed a new unsupervised approach (Hungarian) that remains the safest choice when no annotation or external dataset are available. When an external dataset is available, the distantly supervised approach is better than the Hungarian method on two datasets out of four. Notably, the distantly supervised approach provides state-ofthe-art results on the WikiQA dataset, which is a remarkable result, given that no annotation were used on the target dataset. Importantly, we found that the pair-wise/siamese method allows for low-shot learning of sentence representations that outperforms the unsupervised baseline with as little as four questions.
Pre-training the models on an external dataset and performing fine-tuning on the target dataset only provides good results when the latter does not present a domain shift, or when the number of training samples used for fine-tuning is low. We believe that performing a cross-domain regularization could greatly improve our results, using for instance an adversarial approach such as in  [27]. We note that the siamese architecture would be of particular interest in the context of large-scale semantic information retrieval because the question and candidate answers are independently encoded. It results that a large answers database could be encoded o ine and performing the search would just consist of very fast and efficient cosine operations  [28]. Finally, we found that when a larger amount of data is available, our models compete with much more complex approaches typically relying on convolutional and/or recurrent models with attention mechanisms  [2, 29].
Pre-training is highly effective on the WikiQA, Semeval (and TrecQA) datasets when training is performed on a few questions. The MAP on WikiQA remains constant up to a hundred questions, and then slowly increases. In the case of Semeval, pre-training gives a significant improvement of the performance for a small training set (up to 64). The gain in performance then gradually decreases, and it is close to zero when all the training set is used. For the Yahoo dataset, pre-training is only effective for a low number of training question (up to 16) and is detrimental when more questions are used for training. These findings confirm the added value of domain adaptation when the domain shift is not too important, i. e. , for WikiQA and TrecQA. It is also efficient for the other datasets, but only when the number of training samples is very low, i.
[10]he WikiQA dataset, we obtain very good results, with a MAP close to the state-of-the-art which is an attentionbased RNN architecture also pre-trained on SQuAD-T[9]. Without pre-training, we obtain a MAP of 76. 36, which is superior to the state of the art without pre-training (74. 50 MAP[10]). On the Semeval dataset, the pre-training has a small, yet significant, effect on the performance, and our results are over the current state of the art. Results on the TrecQA dataset exhibit a different pattern, as the w2v representation is better than the USE one. However, the performance reached by our approach is 10 points below the state of the art. The MAP is slightly, yet significantly, higher than with the unsupervised Hungarian method. On the Yahoo dataset, we reach good performance with the USE representation, only 2 points below the state of the art.
For this dataset, the pre-training is significantly detrimental (-1. 4 points p > 0. 05). WikiQA TrecQA SemEval Yahoo MAP MAP MAP P@1 point/concat -w2v 60. 11 73. 23 72. 10 27. 50 point/concat -w2v  † 66. 14 76. 60* 73. 43 27. 30 point/concat -USE 76. 36 65. 37 80. 57* 36. 65* point/concat -USE  † 78. 30 72. 52 80. 45 34. 87 pair/siamese -w2v 62. 38 69. 12 71. 75 24. 92 pair/siamese -w2v  † 67. 94 70. 27 71. 72 25. 76 pair/siamese -USE 75. 61 67. 96 75. 32 34. 13 pair/siamese -USE  † 78. 67* 71. 65 75. 94 33. 79 SOTA 1 79. 90 † 86. 57 80. 36 38. 74 SOTA 2 74. 50 82. 20 78. 37 † 37. 17.
The Operational Program on the Exchange of Weather Radar Information (OPERA) has co-ordinated radar co-operation among national weather services in Europe for more than 20 years. It has introduced its own, manufacturer-independent data model, runs its own data center, and produces Pan-European radar composites. The applications using this data vary from data assimilation to flood warnings and the monitoring of animal migration. It has used several approaches to provide a homogeneous combination of disparate raw data and to indicate the reliability of its products. In particular, if a pixel shows no precipitation, it is important to know if that pixel is dry or if the measurement was missing.
Since 2011, the OPERA data center (ODC; also known as "Odyssey") has created Pan-European radar composites every 15 min. This long time-series of data provides an insight to the development of central data processing methods, and also to the gradual improvement and harmonization of the incoming data from over 20 different national weather radar networks. OPERA produces three different composites with a horizontal resolution of 2 km-maximum reflectivity, rainrate, and hourly accumulation-which are updated every quarter of an hour (on the hour, and at 15, 30, and 45 min past the hour) and are issued approximately 15 min after the start of data acquisition. It also provides quality-controlled single-site radar data for the numerical weather prediction (NWP) consortia for assimilation. This paper is organized as follows.
European Radar Network In January 2019, OPERA had 30 members. Its metadata database listed 200 operational radars from these members, and 164 of these (from 25 countries) were exchanged regularly. A total of 71 radars were from the 20th century; furthermore, 163 are C-band, 29 are S-band, and 8 are X-band (see Figure  1 ). To understand both the variability and common factors in this grouping and to share experiences between members, OPERA has executed several surveys and studies. Results from a survey about maintenance showed large differences in national maintenance policies, and revealed that the primary causes of missing data were not related to the radar itself, but were related to issues with the electricity supplies or telecommunications  [2]. A survey about application priorities showed that the most important uses of radar data were for aviation and severe weather warnings.
Only a limited number of OPERA members are testing dual-pol attenuation correction, and even less have started using this operationally. Many of the new dual-polarization-based quality control algorithms are implemented in the radar signal processor, and are part of the proprietary software. So, they must be applied at the radar site during the time of measurement. Solar monitoring is a method used to monitor antenna direction. The direction from where microwaves are received from the Sun are compared to the nominal pointing of the antenna and the power from a solar observatory  [3, 4]. The solar hit detection software in ODC is also able to calculate solar ZDR values  [5]. At present (April 2019), polarimetric data (ZDR or TV) is provided by two members (16 radars, in total). In the OPERA data-hub, the solar signals are analyzed in the polar volume data, which helps the members to notice changes in the radar direction.
Central Processing and Quality Control In the early years, OPERA was focused on supporting bi-lateral data exchanges. In 2006, OPERA took a first step towards an operational service with the OPERA Pilot Data Hub, which received single-site Cartesian products and national composites from members to create a 4 km resolution pan-European composite. Variability in the quality of the input was surprisingly large, and the re-projection of each of the Cartesian product into the pan-European one led to the loss of resolution and discontinuities. In order to create a better product, it was decided that a data center was needed to receive all the Plan Position Indicator (PPI) scans produced by the European radars, instead of national composites, and, in 2011, the Opera Data Centre (ODC) (later called "Odyssey") was created.
The first maps show the frequency of nodata for the years 2012 and 2018 (Figure  2 ). This comparison shows a substantial geographical extension of the network. In 2018, most of Europe is covered, with a data availability exceeding 98%. Figure  3  shows the total rainfall accumulation for 2012 and 2018. Such maps allow for the identification of various artifacts and unrealistic spatial variations. In most areas, an increased homogeneity of the composite was obtained in 2018. Assessment of rainrate composites shows that the increase in homogeneity of composites is due to national improvements, both of the radars themselves and of the data sent to OPERA. Furthermore, the beam blocking correction and satellite-based clutter removal methods, implemented in December 2015, also improved the quality of the composite.
To quantitatively assess the quality of the OPERA products, an external reference is needed. Hence, the rainrate composites were compared to the accumulated precipitation using the mesoscale analysis system MESAN  [13]. Figure  6  (left) shows the number of days in December 2017 where the precipitation intensity in any of the composites is greater than 0. 0 mm/h. This data set was, subsequently, used to normalize the precipitation intensity difference (OPERA-MESAN). Figure  6  (right) shows the corresponding average daily bias for December 2017. In general, weather radars underestimated precipitation at the borders of the network due to beam overshooting. This was especially evident at Northern latitudes, but also in the South during winter. On the other hand, precipitation close to a radar is sometimes overestimated, due to remaining clutter.
Interferences (e. g. , Spain) and potential overcompensation effects of the central processing software (e. g. , East Iceland) are clearly visible in Figure  6  (right). Figure  7  shows the monthly average precipitation bias (OPERA-MESAN) for 2015-2017. Typically, OPERA underestimates precipitation by 1-3 mm/day. This bias was smaller in summer and larger in winter, as beam overshooting is more common in winter. The zero bias in March 2016 looks good, but for the wrong reason. In fact, a single malfunctioning radar (failing noise reduction over two hours) compensated for the general underestimation of precipitation in the OPERA composite domain. A clutter analysis, based on composite pixels with precipitation intensities greater than 0. 0 mm/h, confirmed that the monthly average clutter fraction decreased at the end of 2015, which coincides with the implementation of the new clutter filters in the central processing software.
Common Data Format During the first phase of OPERA, starting in 1999, the community adopted and promoted use of the BUFR format defined by World Meteorological Organization (WMO). Before that, each radar owner used the native formats of the software manufacturers, or nationally developed versions of these. Introducing a common format was a revolution, which allowed manufacturer-independent joint development, and made the international exchange of radar data much easier. The second phase (2004-2006) formulated a second-generation information model for use with weather radar data and the Hierarchical Data Format version 5 (HDF5) file format. It was implemented in 2009, and has been updated several times. Version 2. 3 was published in 2019. During the 20 years of OPERA, all major weather radar software manufacturers have adopted the OPERA data format and provided conversion software for it.
Additionally, the numerical weather prediction model consortia are currently developing tools to read the ODIM format. Development of a common data model started with a focus on Cartesian radar products (such as CAPPI or MAX); however, since 2013, bi-lateral and multi-lateral exchange between OPERA members has consisted of single-site data in the original polar coordinates. The information model was designed from the point of view of trying to harmonize all relevant information, independently of radar manufacturer and the organization from which the data originates. While the information model is intended to enable the representation of the data and products presently agreed upon within the framework of EUMETNET OPERA, we also look ahead to future needs and have tried to ensure that they will be appropriately met with this information model.
Data for Users OPERA produces three different composites  [15] , which are updated every quarter of an hour (on the hour, and at 15, 30, and 45 min past the hour), issued approximately 15 min after the start of data acquisition. The OPERA composites, covering large parts of Europe with a spatial resolution of 2 km × 2 km (Lambert Azimuthal Equal Area projection), are distributed in the ODIM HDF5 format. The maximum reflectivity product is also shown as an animation on the EUMETNET website. Odyssey was developed with four user groups in mind: Forecasting and nowcasting, numerical weather prediction (assimilation and verification), civil and military aviation, and hydrology and water management. For the weather forecasters of national weather services, the products from their national radar network are always the priority.
Most of the limited area models use radar data in their original radial coordinates in order to take full benefit of the three-dimensional structure of the precipitating system. Global models have used two-dimensional composites. In the European Centre of Medium-Range Weather forecasting (ECMWF), Lopez et al. [17]  have used 6-hourly precipitation sums in 4DVAR. They have implemented the use of NCEP Stage IV data from the contiguous United States operationally. This data is a combination of radar and rain gauges. A similar product for Europe is not available, and the quality of OPERA composites alone have not yet been sufficient for such use. The COSMO consortium has tried to use the rainrate composite for Latent Heat Nudging  [18]  and experimented with the assimilation of 3-dimensional radar reflectivities with an ensemble Kalman filter  [19].
Discussion Definition of a manufacturer-independent data model has been an unprecedented success story. The OPERA data model has been used in international radar data exchange and re-analysis beyond the borders of Europe (e. g. , in Australia). The first versions were very accommodating, allowing easy conversion from all the different manufacturer-specific formats; even the identifier of a radar can be given in five ways. Now, as the use of radar data has expanded from exchange between members or organizations to assimilation, verification, and data fusion in various communities, there is more pressure to tighten the use of the model, in order for it to be more homogeneous. The OPERA program has responded to this by allocating a new work package for homogenisation of the metadata. Each of the participating institutes has defined the use of its radars primarily for its own uses, within the boundary conditions of the hardware and data transfer capacity.
Creation of an international composite has resulted in a natural pressure to homogenise the radar network. Sharing best practises in expert meetings has led to the same direction. The level of homogeneity can be considered a great achievement, in view of the heterogeneity of the hardware and measuring circumstances. The different brands and ages of the radars can be compared to those of cars in Europe: If we were to dictate that everyone must run at the same speed, we should force the Ferraris to slow down to Trabant speed, or agree that Trabants can never reach the Ferrari standard. It is important to note that these changes have happened gradually, and that the OPERA archives contain data from the early years, when the number of non-precipitating echoes was larger and the number of participating radars smaller.
Figure3shows the total rainfall accumulation for 2012 and 2018. Such maps allow for the identification of various artifacts and unrealistic spatial variations. In most areas, an increased homogeneity of the composite was obtained in 2018. Assessment of rainrate composites shows that the increase in homogeneity of composites is due to national improvements, both of the radars themselves and of the data sent to OPERA. Furthermore, the beam blocking correction and satellite-based clutter removal methods, implemented in December 2015, also improved the quality of the composite. This is illustrated in Figure4, which shows the frequency of undetect values in 2015 and 2016. The undetect value can be interpreted as a "dry pixel". The pixels which are never dry (i. e. , blue pixels in the figure) are very likely contaminated by non-meteorological signals, even if these signals are very low in intensity.
X-ray diffraction and thermal analyses were used to determine the mineralogical composition of the solid phases. Thermogravimetric analyses (TGA) were carried out under N2 atmosphere at a heating rate of 10 °C min -1 up to 1000 °C. The so-called "bound water content" (i. e. , the water constitution of all hydrates precipitated at that time) was deduced and corresponds to the weight loss between 50 and 550 °C. The apparatus used was a SETARAM TG-DTA 92-16. X-ray powder diffraction (XRD) measurements were performed using Cu-Kα radiation on a PANalytical X'Pert Pro diffractometer in a θ-2θ configuration with an angular scan of 5° -65° (2θ), the step size of 0. 016° / 60 s, and an X'Celerator detector. Rietveld analysis was performed with the HighScore Plus (version 3. 0d) PANalytical software.
The anodic curve related to the sample with an initial 1. 0% Cl, in turn, is strongly shifted towards lower Ecorr values and icorr up to 1. 44 µA/cm² (one order of magnitude when compared to reinforced samples without chloride), indicating that, in these conditions, the reinforcing steel is undergoing sustained corrosive attacks. It is important to stress that, if the increase of corrosion current with chloride contamination is an expected behaviour, results presented here bring a systematic and robust insight pointing out the good corrosion resistance of BYF samples exposed to endogenous chloride contents up to 0. 4%. This trend has been confirmed by the EIS analysis discussed below. EIS data at 1, 28, 90 and 365 days are represented by Nyquist and Bode diagrams in Fig.
Acknowledgments: The authors wish to thank the team of  La Rochelle Marina  and in particular  Adeline Thomassin , that provided technical support for the deployment of observing systems. The developing team of TELEMAC-MASCARET system is also acknowledged for making its code available. We also thank  CNRS, Région Nouvelle-Aquitaine , and  La Rochelle Université  for their support and  Météo France  for providing us with meteorological data. The authors finally want to sincerely thank editor and reviewers chosen by  Water Editorial Office , for their very constructive reviews that improved this paper greatly.
Entity linking can also be modeled as a ranking task, where a list of candidate matches of an entity is ordered from highest to lowest confidence level. The input data consists of a graph, where nodes represent associations between named entities and candidate matches obtained from the KB. The edges represent links between concepts found in an external database. The objective of this approach is to select the set of candidate matches that maximizes the global coherence between entities. In biomedicine, ontologies are commonly used to organize knowledge about a specific domain, providing a formal representation of concepts and their relations according to the domain. As such, they can be used as reference KBs for text mining tasks such as entity linking  [9, 10]. For example, an ontology enables us to calculate the semantic similarity between two concepts and compare which concepts have more in common.