dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017) , and a combination of satellite imagery (e. g. ,  Töyra et al. , 2002 , Bwangoy et al. , 2010; Bioresita et al. , 2019). The estimation of other important hydrological variables such as water level, discharge, water volume and their variations can represent a more difficult challenge  (Grippa et al. , 2019). This challenge has been addressed by exploiting information acquired by radar and laser altimetry (e. g. ,  Maheu et al. , 2003; Crétaux and Birkett, 2006). For example, Santos Da  Silva et al. (2012)  used ENVISAT Radar Altimeter 2 (RA-2) data to characterize water storage in rivers, floodplains, wetlands and lakes in the Amazon basin. Internationally, estimations of changes in lake and reservoir volume have been attempted by exploiting different satellite altimetry databases including both laser (Ice, Cloud, and Land Elevation Satellite; ICEsat-1) and radar data (Topex/Poseidon, Jason-1, Jason-2, ENVISAT; e.
Data A number of extensive ground-based and remotely sensed derived datasets are required to setup a hydrodynamic model and to perform the SWOT simulations. As part of the SWOT-C TH effort  (Pietroniro et al. , 2019) , two fieldwork campaigns were carried out in the PAD during the summer 2017 by Environment and Climate Change Canada and the University of Sherbrooke with support from Wood Buffalo National Parks. In addition to the long term water level hydrometric network stations on the Prairie River and Mamawi Lake outflow, project specific measurements of water level and streamflow were also taken in the main channels around Mamawi Lake (see Fig. 2 ). The bathymetry of the lake and channel cross-sections were acquired by a combination of weighted line depth and echo-sounding measurements.
These data were used to define the boundary conditions and develop the hydrodynamic model (see Methodology section below). The extent of Mamawi Lake is difficult to estimate precisely, both from remote sensing and in situ observations, due to the heavy vegetation and soft soil (mud) surrounding it, as well as the seasonal variability in the water level causing the lake to sometimes merge with neighbouring lakes to form a single water body in extreme cases. Nonetheless, a water mask that discriminated the water from the land pixels and used by the SWOT-HR simulator was determined from the combination of a Sentinel-2 image acquired on July 9 th 2017 and a Sentinel-1 image (Fig. 3a ) acquired on July 8 th 2017. This mask was also used to limit the physical boundaries of the lake in the hydrodynamic model.
Hydrodynamic simulations of Mamawi Lake The 2D-H2D2 hydrodynamic model was used to analyze water motions beneath Mamawi Lake's surface. This finite element hydrodynamic software platform, developed at the "Institut National de la Recherche Scientifique, Centre Eau Terre Environnement" (INRS-ETE; (http://www. gre-ehn. ete. inrs. ca/H2D2; Secretan 2013), consists of multiple modules (e. g. , sediment transport, water quality). H2D2 estimates the 2-D flow velocities, discharge, and water levels by solving the differential shallow water equations, also known as the Saint-Venant equations. The triangular finite element mesh of the topobathymetry used in the hydrodynamic model was generated using the Surface Water Modelling System (SMS). SMS, developed by Aquaveo, was designed for 2D costal and riverine modelling.
A total of 301,900 elements were used to model Mamawi Lake. Each of these elements has an elevation, which was obtained from the topobathymetric map generated from the field measurements, and a Manning roughness coefficient. The Manning coefficients were estimated in a two-step process. First, a supervised classification was performed using Sentinel-2 data (Fig. 3b ), which was validated using pictures taken from the ground, boat and helicopter. The classes include open water, shallow or flooded areas, algae, moss, sedge, live deciduous (mainly willows), denuded or dead willows (likely caused by successive tent caterpillar outbreaks;  Gleeson, 2017)  and evergreen vegetation. Since the classification matched relatively well our general knowledge of the lake configuration and the experiment is ultimately synthetic, it was not felt necessary to use a robust validation method traditionally used in remote sensing.
Volume analysis In order to remain true to the goal of the study to evaluate expected SWOT performance over lakes and not rivers, which have their own set of challenges, a mask was created to exclude the channels going in and out of the lake. Henceforth, WSE and water volumes refer to the area within the mask (Fig. 2 ) unless stated otherwise. The water balance of Mamawi Lake was performed for two types of data: SWOT imagery and in situ gauges The first type of data is from simulated SWOT imagery. WSE over the entire lake was retrieved in two ways. The first is computed using the mean elevation of SWOT pixels, assuming the WSE to be uniform over the lake. The second is computed by passing a linear plane through available SWOT pixel cloud data, such that the square of the normal distance between the plane and the pixel cloud is minimized.
Acknowledgement We thank  Environment and Climate Change Canada and Parks Canada  for their collaboration in this study. In particular, we thank  David Campbell ,  Ronnie Campbell ,  Tom Carter ,  Mark Russell ,  Sébastien Langlois ,  Jessica Lankshear  and  Nicolas Simard  for help collecting data during the 2017 field campaign. We are also thankful for the tips granted by  Pascal Matte  and https://doi. org/10. 5194/hess-2020-162Preprint. Discussion started: 17 June 2020 c Author(s)  2020. CC BY 4. 0 License.
In addition, stratification of healthy individuals (selecting high-risk groups of "potential patients") is also required to implement precision medicine  11  and preemptive therapy, which will be described in the next section. Furthermore, the utilization of artificial intelligence (AI) in "big data" analysis and standardizing phenotypes in model organisms need to be promoted. A large-scale birth cohort study under the Japan Environment and Children's Study  12  and a third-generation cohort study by the Tohoku Medical Megabank Organization 13 are currently being conducted in Japan. Stratification of patients based on phenotypes is being attempted by bringing together such clinical information databases. However, these approaches pose many problems regarding differences in information categories, terminology, diagnostic definition, or the completeness of the stored information.
Thus, in collaboration with Action III-4 described later, it is indispensable to make full use of good practices in the rare disease realm, where national and international databases have been harmonized in a globally standardized format,  14  resulting in the confirmed diagnosis, treatment and medical care. 15, 16 With the standardization of clinical information and the deep phenotyping of patients with allergic and immunological diseases, multi-omics analysis of their samples from the peripheral blood, skin, and airway lesions, etc. would be promoted greatly. On the other hand, it is impossible to perform such multi-omics analyses in a single research laboratory or institution. Thus, the use of preexisting programs and closer cooperation among laboratories/institutions are essential.
I-4. Interdisciplinary R&D for multi-organ system network Despite having many aspects in common regarding immunological mechanisms, neurological findings (for symptoms such as itching),  30  and barrier functions,  31  various symptoms in different organs have prevented interdisciplinary R&D regarding the multiorgan system network beyond the medical department boundaries in Japan. Several countries set up their registration systems for individual diseases at the national level, and many epidemiological study results that utilized such databases have been reported. 32 urthermore, it is important to collect objective data that may be useful within different clinical fields and to standardize the data for furthering research that goes beyond the borders of individual organs. For example, asthma is often diagnosed subjectively via auscultation among doctors, although the findings must be transformed into objective data.
Acknowledgments We thank all the group members, all those who participated as observers, and all those who worked on Strategy 2030. We are grateful to all the presidents of the relevant academic societies for their cooperation. Our special gratitude goes to  Prof. Warwick Anderson  ( Secretary-General ,  International Human Frontier Science Program Organization)  and to  Prof. Christopher Mueller  (Director,  CNRS UPR 3572 ,  Institut de Biologie Mol eculaire et Cellulaire (IBMC) ,  Universit e de Strasbourg)  for their valuable suggestions regarding the manuscript. This study was supported by the scientific research fund of the  Ministry of Health, Labour and Welfare, Japan  ( 201812006A ). Advisory role:  SF, GlaxoSmithKline, Kyowa Kirin, Sanofi. Honoraria: KA, Astellas Pharma, AstraZeneca ,  Nippon Boehringer Ingelheim ,  GlaxoSmithKline Consumer Healthcare Japan ,  Novartis Pharma ;  EU, Santen Pharmaceutical , Senju Pharmaceutical; MO,  Mitsubishi Tanabe Pharma, Taiho Pharmaceutical ; HS,  Mitsubishi Tanabe Pharma ,  Taiho Pharmacetutical ,  Novartis Pharma ,  Maruho ,  Torii Pharmaceutical ,  Sanofi ,  Kyowa Kirin ,  Eli Lilly Japan ,  Otsuka Pharmaceutical ,  AbbVie ,  Celgene ; MN,  Novartis Pharma ;  NH ,  GlaxoSmithKline ,  AstraZeneca ,  Nippon Boehringer Ingelheim ,  Novartis Pharma ; AF,  Santen Pharmaceutical ,  Senju Pharmaceutical ,  Novartis Pharma ; SF,  Kyorin Pharmaceutical, Taiho Pharmaceutical ,  Mitsubishi Tanabe Pharma ,  Torii Pharmaceutical ,  Sanofi ,  MSD.
To show this we present a new measure called platform surface interior angle (PSIA) formed by the point of percussion and the extent of the platform width (Fig  1A ). We predict that this angle will be constant given that it directly follows from the constant of the Hertzian cone angle. If our prediction is correct, platform width (PW) can be incorporated into the EPA-PD model and grounded in fracture mechanics via PSIA. This model also has behavioral implications in that it may explain how the manipulation of the platform impacts flake morphology. To test this model, we examine several sets of flakes, including flakes produced in the Dibble glass experiments and flakes from replication experiments, using several methods to measure the PSIA. We find that the mean angle in all datasets, regardless of how it is measured, is the same (approximately 136 degrees) and quite consistent with above mentioned values for Hertzian cone formation  [2].
There is some variability in the PSIA, and it is clear that this variability cannot be solely attributed to measurement error. In the Dibble glass experiments, where key variables are controlled, there is some indication that the PSIA responds to the angle of blow. Our finding is consistent with all of the empirical results of the Dibble experiments. More importantly, PSIA explains some of the patterns in those data that previously were unaccounted for. Once PSIA can be combined with the existing EPA-PD model, we may have a model for flake formation that can explain a larger portion of the variability we see in stone tool assemblages and that may allow for a closer link to predictions coming from fracture mechanics.
Materials and methods We examine the platform surface interior angle in three different datasets. First, we examine glass flakes (n = 142) and cores coming from the Dibble controlled experiments in flake formation  [9, 19, 22, 24, 25]. This dataset has the advantage that a number of potentially important variables are either controlled for or were measured. These include the exterior platform angle, the angle of blow, the hammer type, raw material, and metrics such as platform thickness, platform width, flake length, width and thickness, and flake weight. Hereafter this dataset is referred to as the Dibble glass data. Second, we attempt to replicate the findings from the Dibble glass data by measuring the PSIA in a large (n = 568) set of complete, unretouched flakes coming from 45 discrete reduction sequences produced in replicative experiments by three knappers who were naïve to the goals of this study.
These flakes were made with the intent of replicating various Middle and Upper Paleolithic core reduction strategies from the initial decortification of the core through to flake production and core maintenance  [42] [43] [44] [45]. These replicative experiments used nodules of high-quality Bergerac and Senonien flint weighing from ~480-4100 g and coming from the southwest region of France  [46]. For each of the flakes coming from these reduction series, the technology and the type of hammer (hard hammer, soft hammer and indirect percussion) are known. Here we are interested in the hard hammer flakes because these are comparable to the Dibble hard hammer flakes, but we have included the soft hammer and indirect percussion flakes in the results presentation for discussion purposes. Today these flakes are stored in Campagne, France, and hereafter this dataset is referred to as the Campagne data (see  [47]  for additional details on the structure of this dataset).
Third, in addition, we measured a small set of flint flakes (n = 67) produced at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, in the context of teaching, replication, and experimentation. In this case, no details are known about how the flakes were produced (e. g. hammer types). This set of flakes is used here only to test a method for measuring the PSIA. Hereafter this dataset is referred to as the MPI data. The methods used to measure the PSIA varied substantially between the three datasets for practical reasons given the types of data available to us and because we wanted to try different methods to begin to have some idea of how best to measure this angle in future studies. First, for the Dibble glass data, we used the following procedure. Dibble and colleagues used several core forms, but the first and most common type is what was called the semispherical core.
We then filtered the Dibble glass data to have only flakes made from the semispherical cores by hard hammer. We include only flakes with a feather termination, and we exclude flakes coming from experiments on platform beveling  [25]  and so-called 'on-edge' core strikes  [19]  (thereby arriving at our sample of n = 142). Knowing that Dibble and colleagues tried to strike flakes from these cores at the center or peak of the core surface curvature, we use the platform depths reported for these flakes to position the point of percussion relative to the set of platform edge outline points described above. Next, we find the symmetrical pair of platform edge outline points, one to the left and one to the right of the point of percussion, that yield a platform width equal to the reported platform width for each flake.
e. , the two ends of the platform width) and the point of percussion. These three points are analogous with the three points described above for computing the PSIA. This angle, therefore, can be once again computed using the dot product of these two line segments (specifically the arc-cosine of the dot product of the normalize line segments). However, there is an important difference in that, with the Dibble glass data, all computations are with two dimensional line segments, while in the Campagne dataset the line segments are in three dimensions. In the latter case, the angle is computed in a two dimensional plane that is coincident with both line segments, but we note this difference because it could introduce a certain amount of incomparability in the two datasets. Our expectation is that these angles could average larger than the Dibble glass data because, for instance, lifting the point of percussion relative to the two platform points would result in a larger PSIA.
Results Fig  3  shows the distribution of estimated PSIA in the Dibble glass dataset. The distribution has a mean of 136. 49 ± 7. 56. Variation in this angle does not seem to be related to platform depth, exterior platform angle or weight (Fig  4 ). There is a relationship between platform width and the platform surface interior angle such that larger angles result in wider platforms, which is to be expected (see  Fig 4). There also appears to be a relationship between the angle of blow and the PSIA (Fig  5 ). While there are fewer cases with angles of blow less than 20, there is some indication in the data that lower angles of blow may be correlated with lower PSIA. However, once an angle of blow is between 10-20 degrees or higher, the angle of blow is not correlated with the PSIA. Another way of looking at the relationship between platform width and PSIA is to calculate what the platform width would be if the PSIA is a constant and compare this to the actual platform width.
We can do this by placing the point of percussion on the same platform outlines as above using the known platform depth for each of the flakes in the Dibble glass data set. We then use the average PSIA computed above to extend two vectors from this point of percussion to the platform edge. Where these vectors intersect the platform edge defines the left and right limits of the platform width. This estimated value for platform width is then plotted against the actual, measured platform widths (Fig  6 ). In the Campagne data, PSIA does not covary with platform width, platform depth or the shape of the platform (as measured by the ratio of platform width to platform depth) (  Fig 10). Though sample size is potentially a problem, there is perhaps an indication that for larger platform depths, there is less variability in the PSIA.
Discussion Each of the datasets and measurement methods used to determine the PSIA yielded very similar results, and these results are consistent with the prediction based on the already known and constant angle of Hertzian cones in soda lime glass (136˚). While these results directly incorporate platform width into the EPA-PD model of flake formation, they also suggest that the existing model requires additional nuance. All other things being equal, the relationship between PSIA and platform width indicates that what determines the size of the flake is how far into the core it is struck. Specifically, because of the constant PSIA, the deeper into the core the flake is struck, the greater the platform width will become (and the thicker the flake will become as well). However, and this is the nuance, while platform depth is normally a good proxy for how far into the core the flake is struck, it is not exactly the same thing.
What platform depth really measures is how far into the existing platform of the core the flake is struck. Beveled flakes illustrate this point. Beveled flakes are ones where material is removed behind the platform prior to striking the core. Dibble and colleagues recognized that beveling altered the EPA-PD model of flake formation such that the interaction of platform depth and exterior platform angle no longer predicted flake size  [25]. Beveled flakes have too thin a platform for their size. However, PSIA helps explain this discrepancy and thereby pulls beveled flakes back into an expanded EPA-PD model. To illustrate this point, we examine some beveled flakes in the Dibble glass dataset. In the Dibble glass dataset there are 11 flakes with flat or concave beveling, coming from semispherical cores, and otherwise conforming to our selection criteria.
These are plotted in Fig  12  along with the non-beveled flakes. It is clear that beveling changes the relationship between platform depth and platform width. For a given platform width, the beveled flakes have much shallower platforms (smaller PD) than expected. As a result, the EPA-PD model underestimates their weight (see  Fig 12)  as well. To illustrate the power of PSIA to model these flakes, we build a linear model to estimate platform depth from the PSIA and platform width in the non-beveled flakes in the Dibble glass dataset. We then use this model to predict a platform depth for the beveled flakes. However, given that the linear model requires PSIA to predict platform depth and the actual PSIA is not known for these flakes, we substitute in its place the average PSIA, as reported above for the non-beveled flakes.
Finally, we use the predicted platform depth for the beveled flakes, as modeled above, to predict flake weight again using the non-beveled flake model. In this case, the flake weights plot in among the rest of the non-beveled flakes (blue points in  Fig 12). Thus the beveled flakes are the expected size when we think of PD in the EPA-PD model not as a measure of platform depth but rather as a measure of how far into the core the flake is struck, which then determines the flake width via the PSIA given the shape of the platform edge. Beveling does not change the expected size of these flakes when flake formation is viewed this way. There is some indication in the Dibble glass data that the angle of blow may impact the PSIA. As mentioend earlier, at low angles of blow (with 0 degrees representing an angle of blow perpendicular to the platform, see Fig  2  in  [19] ), the PSIA is below average, and it appears to increase until the angle of blow reaches between 10 and 20 degrees (from perpendicular) after which the PSIA remains essentially unchanged (see  Fig 5).
In our Campagne data set, we see a difference between flakes made from direct hard-hammer percussion and those made with a punch technique. The latter cluster at the low range of PSIA. This difference could be interpreted as reflecting a difference in the angle of blow in that punch flakes are more likely to be struck perpendicularly to the platform surface (an angle of blow of 0). More work needs to be done, in particular in analyzing the Dibble glass data set where angle of blow is well controlled, but we suggest that increasing the angle of blow has the effect of tipping the direction of the Hertzian cone such that it intersects the core surface not as a circle but rather as an ellipse. This is a phenomenon that has been repeatedly documented in fracture mechanics  [36, 38, 40]. Although the angle of the cone itself remains unchanged, its intersection with the surface broadens and results in higher PSIA.
Acknowledgments Michel Brenet  and  Laurence Bourguignon  produced the Campagne data set, and we thank them for allowing us access to this important collection. Claudio Tennie  provided feedback on the ideas and comments on the manuscript. SPM, MW, WA, ZR and TD thank  Jean-Jacques Hublin  for his continued support of our research agenda. Sadly Harold Dibble  died before the main findings of this paper were discovered. Without his vision and his efforts to build a data set of flakes made under controlled conditions, this paper would not have been possible. We dedicate this paper to him.
In "A Multilingual Evaluation for Online Hate Speech Detection"  [5] , the authors address the task of automatically detecting hate speech in social networks by addressing the multilinguality Introduction to the Special Section on Computational Modeling 8:3 challenge. One of the main contributions of the article consists of identifying a deep learning architecture suitable for the task in a cross-lingual environment, as well as selecting the best features (e. g. , word and emoji embeddings, social network specific features, or emotion and hate lexicons  [2] ) that could be exploited to develop better hate speech detection systems. The proposed architectures have been evaluated on freely available datasets in English, German, and Italian, and the impact of various parameters on hate speech detection in different languages is discussed.