dataesr/scientific-paragraphs-categorization huggingface dataset:
COVID-19 examples We now confront the eRG framework to data from the COVID-19 pandemic collected from www. world omete rs. info and adjourned to the 4th of May, 2020. Although we are well aware of the pitfalls stemming from comparing data provided by different countries due to the inhomogeneous way infectious cases were tested and reported, it is still possible to extract from these reliable time behaviour and structure. Of course, when coupling two regions of the world, part of the initial uncertainty also affects the epidemic transmission probability without affecting the overall picture. Nevertheless, we will now see that the eRG formalism can be used simultaneously to quantitatively project the spreading dynamics across different regions of the world, or as an a-posteriori way to learn how this spreading came to be.
Empirical strategy The empirical analysis involves two steps. First, we use panel data on firm-to-firm export growth to recover the shocks hitting firms in international markets. In practice, this amounts to estimating a high-dimensional fixed effects equation in the cross-section of growth rates, for each available year. Second, the recovered time-series of shocks are aggregated within and across firms to compute measures of volatility "in the small" and "in the large", and their components. The underlying model is described in Section 2. 1. The strategy to recover the model's shocks from the data is detailed in Section 2. 2. The aggregation procedure can be found in Section 2. 3.
Data The empirical analysis is conducted using detailed export data covering the universe of French firms. The data are provided by the French Customs. The full data set covers all transactions that involve a French exporter and an importing firm located in the European Union, over the 1995-2007 period. For each transaction, the data set records the identity of the exporting firm (its SIREN identifier), the identification number of the importer (an anonymized version of its VAT code), the date of the transaction (month and year), the product category (at the 8-digit level of the combined nomenclature) and the value of the shipment. 17 In the analysis, data are aggregated across transactions within a year, for each exporter-importer pair. 18 tatistics on the dimensionality of the Trade Network, as observed in 2007, are presented in Table  1.
for their insightful suggestions. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  714597 ). Our work is also supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( Idex Grant  Agreement No. ANR-11-IDEX-0003-02  /  Labex ECODEC  No. ANR-11-LABEX-0047 ). Martin thanks  FRQSC  for financial support (grant  2015-NP-182781 ).
14  Using external data from the INSEE-Ficus database of firms' balance-sheet, we are indeed able to recover the share of each foreign buyer in French sellers' overall sales, on top of its share in the seller's European exports which we observe directly in our data. In the sub-sample for which the information is available, the average share of a buyer in total sales is as low as 1. 3%. The reason is that firms on average sell most of their production on their domestic market (Eaton et al. , 2011; di Giovanni et al. , 2019).
Acknowledgements This work was performed within the  LABEX Plas@par project  and received financial state aid managed by the  Agence Nationale de la Recherche  under the reference  ANR-11-IDEX-0004-02. ES is also funded by the ' Chaire Energie Durable' at Ecole Polytechnique  under the reference EXXI with the financial aid from EDF foundation. AS would like to thank the  ANR  for the "invited interantional expert" grant, hosted at Ecole Polyechnique.
They confirmed that the fillers enhanced the hardness of the char layer. With a view to ensure efficient thermal protection, while providing high mechanical strength, all these phenomena have significant impact on the thermophysical properties of the TPS. In fact, the addition of fibres and fillers can complicate the thermal behaviour of the material. The determination of their thermophysical properties at high temperatures can be very challenging  [25]. Furthermore, the creation of a complete properties database represents a large issue, especially for TPS materials in aerospace applications. The thermophysical properties can be identified by means of two distinct approaches  [26] : direct (experimental measurements) and indirect (numerical models). For the first approach, the experimental procedure consists of using dedicated experimental apparatus to directly measure, from ambient temperature up to 1000 °C or more, each thermophysical property.
6% for 'Healthy with agricultural technology C', Healthy-C, and -54 million ha or -1% for 'Metropolization with ultra-processed products', Metropolization_Ultrap). Only the Metropolization_Ultrap and Healthy_C scenarios are able to produce sufficient food for the expected growing population up to 2050 without a significant expansion in the world agricultural land area and potential further major deforestation at the world scale. Under all the other scenarios, ensuring food availability in 2050 would likely result in large areas of deforestation all over the world. In some cases, the expansion in agricultural area is so huge that the scenarios concerned may be considered to be clearly unsustainable in 2050: Communities_Collapse and Metropolization_Animp scenarios, for example. However, one could also question the feasibility of the Regionalization scenario, which induces a significant expansion in the agricultural area: +249 million ha and up to +691 million ha in the A and B GlobAgri-AgT simulation results and SSP database  [49] [50]  (https://tntcat.
iiasa. ac. at/SspDb/dsd?Action=htmlpage&page=20). Agrimonde-Terra scenarios include the impacts of differentiated pathways of climate change (Table  1 ). They are thus compared to SSP/RCP (Radiative Concentration Pathway) combinations of similar mitigation levels (respectively, RCP2. 6, RCP4. 5 and RCP6. 0). The RCP8. 5 option would fit better for comparison of SSP5 with Metropolization, however results for SSP5-RCP8. 5 do not exist in the SSP database; Regarding SSP-RCP results, those reported are REMIND-MAGPIE-SSP5-RCP6. 0, MESSAGE-GLOBIOM-SSP2-RCP4. 5, IMAGE-SSP1-RCP2. 6, GCAM4-SSP4-RCP4. 5, AIM/CGE-SSP3-RCP6. 0. https://doi. org/10. 1371/journal. pone. 0235597. g003 variants respectively. Although more limited, the expansion in the world agricultural area induced under the Healthy_D scenario (+269 million ha) puts into question the coherence of this scenario given that it involves strong mitigation objectives designed to stabilize climate change.
Acknowledgments The authors warmly thank all those who contributed to the  Agrimonde-Terra foresight study , the 80 experts involved in thematic workshops, the scientific coordinators of these workshops, the members of the scenario advisory committee and the members of the steering committee. The project team was composed of:  Marie de Lattre-Gasquet  (CIRAD, coordinator),  Chantal Le Moue  ¨l (INRA, coordinator),  Olivier Mora  (INRA, organizer for scenario building),  Catherine Donnars (INRA), Patrice Dumas (CIRAD)  and  Olivier Rechauchère (INRA) , in collaboration with  Marco Barzman (INRA) ,  Thierry Brunelle (CIRAD) ,  Agneta Forslund (INRA) ,  Elodie Marajo-Petitzon  ( INRA ),  Stephane Manceron (INRA), Pauline Marty (INRA) and Clemence Moreau (CIRAD). The scenario advisory committee was composed of:  Agnes Andersson Djurfeldt  ( Lund University, Sweden ),  Leïth Ben Becher  ( Synagri, Tunisia), Mohamed Elloumi (INRAT, Tunisia ),  Adama Faye  ( IPAR, Senegal ),  Richard Guissou-Yves  and  Gerard Bazie  ( Ministry of Agriculture and Food Security, Burkina Faso ),  Holger Kray  ( World Bank, USA) ,  John Lewis  ( Terra Global Capital, USA ),  Patrick Meyfroidt  ( Louvain University, Belgium ),  Marc Mueller  ( FAO, Italy ),  Siwa Msangui  ( IFPRI, USA ),  Antonio Onorati  ( International Planning Committee on Food Sovereignty, Italy), Stephane Parmentier (Oxfam, Belgium), Alain Retière (Cap 2100, France ),  Roberta Sonnino  ( Cardiff University, UK ),  Sebastien Treyer (IDDRI, France) ,  Dominique van der Mensbrugghe  ( AgMIP, Purdue University, USA ),  Joost Vervoort  ( CCAFS and Oxford University, UK ) and  Hisham Zehni  ( IFAD, Italy ).
Underexplored accessory minerals such as titanite and apatite have the potential to give insights into the nature and the petrogenesis of their host rock. Their trace element and REE-rich compositions carry a record of crystallisation history and chemical characteristics of their source. Moreover, titanite and, to a certain extent, apatite are resistant to erosion during sedimentary cycles which makes them ideal to reconstruct the history of long-eroded continental landmasses. Here we report new trace element data on apatite and titanite from granitoids of different Archean cratons and comparative granitoids from the Phanerozoic. Trace element signatures of both minerals reveal systematic chemical trends in Y, LREE and Sr contents related to the nature of their host magma, which are used to construct discrimination diagrams delineating Archean TTGs from sanukitoids, and modern adakites from S/I-type granites.
Here we present new geochemical data on titanite and apatite from granitoids selected from this overall temporal progression; (i) Archean TTG from three different cratons (Slave Province, Karelia and Kaapvaal), (ii) sanukitoids from the Karelia and Kaapvaal cratons, (iii) Neoproterozoic calc-alkaline granitoids from Guernsey (UK Channel Islands) in the Armorican terrane. Studied Archean localities have been affected by metamorphic conditions up to amphibolite facies. In addition to these, we have included likely Phanerozoic equivalents to test the impact of metamorphism on their older counterparts: TTG-like (adakites from Antarctica;  Pankhrust et al. , 1998)  and sanukitoid-like rocks (high Ba-Sr granites from Caledonian Scotland;  Fowler and Rollinson, 2012). Detailed sample descriptions, geological settings and whole rock compositions can be found in the Supplementary Information.
, 1992)  and monazite  (Montel, 1986)  solubilities, which suggest that apatite solubility increases with ASI and (to a lesser extent) with SiO 2 in metaluminous magmas. Conversely,  Montel (1986)  has shown that monazite solubility decreases as ASI increases. This is also consistent with the presence of "remaining" primary monazite surrounded by secondary allanite and apatite identified on the studied Acasta samples (Supplementary Information). It should be noted that monazite solubity can also be influenced by other factors (e. g. , P content in the melt; Skora and  Blundy, 2012). In summary, our dataset demonstrates that in peraluminous compositions early monazite will strongly partition LREE with the remaining melt relatively enriched in HREE and Y, which will be incorporated by later apatite and titanite.
As a direct consequence of residual mineralogy, HP-TTG have low HREE, Nb and Ta and high Sr whereas LP-TTG show the opposite signature for comparable bulk compositions (e. g. ,  Moyen and Martin, 2012). Previous authors  (Belousova et al. , 2001; Jennings et al. , 2011; Bruand et al. , 2014)  have shown a strong correlation between Sr in apatite and in the corresponding whole rock. Our data sit on this previously reported correlation. Accordingly, in our apatite dataset, two TTG and our adakite samples that have typical HP-TTG signatures have systematically higher Sr concentrations than the others that have LP-TTG signatures (Fig. 4c ). Figure  4c  also shows that the HP-TTG apatite analyses have lower Y (and by analogy, HREE). All these observations confirm that apatite Sr content can allow discrimination between HP and LP-TTG.
Therefore detailed investigation of the apatite and titanite mineral chemical archive could tightly constrain the change in geodynamics at the end of the Archean. Finally, our data reveal an efficient distinction of parental magma types based on REE-Sr-Y alone which further encourages application to the detrital mineral record. Voluminous data exist from zircons, but the results described above promise much closer constraints on parent rock identity, thus providing vital access to the primary history of eroded terranes and helping to reconstruct the historical evolution of continental crust from the early Earth to the present day.
Acknowledgements We are grateful to  Stuart Kearns  and  J. -L. Devidal  for microanalytical analyses, to  Geoff Long  for amazing technical support. This work was supported by the  Natural Environment Research Council  (grant  NE/I025573/1 ) and by the  French Government Laboratory of Excellence initiative  n°  ANR-10-LABX-0006. This is Laboratory of Excellence ClerVolc contribution number  387. We deeply thank the editor  H. Marschall  and two anonymous reviewers for their input. Editor:  Horst R. Marschall.
All data were processed with Gatan Digital Micrograph GMS3. Cross-sectional lamella samples, nominally 20-25 nm thick, were prepared using focused-ion beam (FEI Helios Nanolab 650). Sample surface morphologies were characterized by atomic force microscopy (AFM), using a Dimension 3100 SPM system with a Nanoscope IVa controller (Veeco Instruments Inc. , USA). Multiple sample areas ~1. 5x1. 5 µm 2 in size were scanned. Surface morphology and elemental composition was also examined using scanning electron microscopy (SEM, FEI HELIOS Nanolab 650) equipped with an energy dispersive X-ray spectrometer (EDX) from Oxford Instruments. Photoluminescence measurements were carried out using a DPSS laser (532 nm) as an excitation source, with the intensity kept at ~ 5 kW/cm 2 , a 0. 4 m monochromator SPM-2, and a thermoelectrically-cooled InGaAs photodetector.
ACKNOWLEDGMENTS The authors would like to thank  Xiaoyan Li  for her assistance with STEM sample characterization. The authors are also grateful to  Arnas Naujokaitis  for STEM sample preparation. This work was supported by  European Regional Development Fund  (project No  01. 2. 2-LMT-K-718-02-0020 ) under grant agreement with the  Research Council of Lithuania (LMTLT). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  823717 -ESTEEM3. This study was also supported by the  Operational Program Research and Innovation  for the project ITMS2014+ code:  313010T598  co-financed by the  European Regional Development Fund.
What do we know about metallic implants and theirs oncologic clinical consequences? Methods to overcome metal artefacts in CT imaging have been investigated and developed for nearly 40 years. The first study on the CT MAR methods was published in 1995, i. e. 20 years after CT introduction. Nowadays, several hundred papers enrich the physics and dosimetric literature. This contrasts with the lack of data about direct effects of metallic hardware on local tumour control and normal tissue side effect rates in cancer patients with a metallic implant. In a prostate cancer patient with bilateral hip implants, MVCT-image-based treatment planning was used for a helical tomotherapy unit (HiArt)  [3]. The authors pointed out the sufficient morphological information of the pelvic anatomy for CTV delineation and radical prostate treatment planning.
They showed that the presence of metallic surgical stabilisation prognosticates for worsened local outcomes (46% versus 75% at 5 years, P = 0. 01). Similarly, from Massachusetts General Hospital Cancer Centre in Boston, Delaney et al. noted slightly more local failure in patients with hardware stabilisation (35% versus 22% at 8 years) but the difference was not statistically significant (P = 0. 277)  [5]. Their proposed explanation included artefacts in CT and MRI datasets, with difficulties to plan and deliver radiation treatment, including target definition and dose inhomogeneities. In a theoretical model based on the EUD algorithm, Tomé and Fowler showed a dose deficit to 1% volume of the target that is larger than 20% of the prescription dose may lead to serious loss of tumour control probability (TCP) with intensity modulated radiation therapy (IMRT).
Mega-voltage CT Simulation images acquired with kVCT system can be associated to MVCT images that are not as susceptible as metal artefacts. These methods could be very useful in case of serious imaging artefacts due to bilateral hip protheses or dental implants in the CTV area. Aubin et al. used MVCT to complement the planning kVCT  [30] , as well as De Marzi et al. in proton therapy to evaluate relative proton stopping power in various high density implants  [31]. Both sets of images were acquired and co-registered. Non-viewable data on the kVCT were shown on the MVCT because metallic implants have little impact on MVCT image quality. Indeed, they showed a change of only 3% in the HU between MVCT images with and without metal. Son et al. noticed that MVCT allowed calculation error reduction compared to manual density assignment  [32].
Dedicated metal artefact reduction algorithms Commercial metal artefact reduction algorithms are available for CT imaging of patients in radiation therapy. To eliminate these artefacts, many MAR algorithms modify the process by which the image is reconstructed. The widespread approach is to directly correct or replace the corrupt projection data in the sinogram by synthesizing new projection data. This is achievable by interpolating replacement values, either from neighbouring projections or from a mathematical model. Another method to complete the sinogram is to incorporate prior information and back-project it to generate projection data for sinogram completion. Iterative methods can reduce the impact of artefacts by ignoring or statistically down-weighting data affected by the metallic object and use an iterative procedure to arrive at an image considering the more reliable features.
Dual energy CT Another approach to reduce the impact of artefacts in radiation therapy workflow is the use of Dual Energy CT (DECT) scanner and particularly a post-processing technique allowing calculation of virtual mono-energetic images. This technology is nowadays widely used in imaging departments. Indeed, in DECT, a highenergy and a low-energy dataset are acquired using poly-energetic X-ray spectra. A linear combination of these two datasets is calculated in order to create an effective image as it were measured using a mono-energetic beam. These virtual mono-energetic images reflect the attenuation as if it was only caused by the respective linear attenuation coefficient for the specific energy. Because of the higher attenuation of soft X-rays, low-energy virtual monoenergetic images result in higher low-contrast resolution.
Acknowledgments: We are grateful to  JAXA  for providing PALSAR-2 images in the framework of the  RA4  project # 1026. We also thank  ESA  for fieldwork support within the  BIOMASS DesertSAR project , and the  National Commission on Research Science & Technology of Namibia  for the approval of our research permit. We deeply thank  Gillian Maggs-Kölling , director of the Gobabeb Research & Training Center, for her help and continuous support,  Charles Bristow  for providing samples of the Warsaw sand dune, and  Troy Sternberg  together with an anonymous reviewer for their advices to improve earlier versions of this paper. Acknowledgments: We are grateful to  JAXA  for providing PALSAR-2 images in the framework of the  RA4  project # 1026. We also thank  ESA  for fieldwork support within the  BIOMASS DesertSAR project , and the  National Commission on Research Science & Technology of Namibia  for the approval of our research permit.
Approaches based on neural networks challenge the variational formulation of motion estimation, as shown on video image sequences with the FlowNet2 convolutional neural network (CNN) architecture (  12 ) that focuses on optical flow. Similar approaches have been applied to cardiac imaging  (13, 14) , but raise several methodological questions. First, the generalization ability of the trained networks to estimate a wide range of deformations at multiple scales still needs to be verified. This is critical for specific disease traits of lower prevalence. Furthermore, robustness to a variety of routine clinical imaging conditions (different image qualities, fields of view, devices, etc. ) needs to be established. Second, supervised CNN-based motion estimators such as FlowNet2 do not embed any regularization, and are therefore sensitive to imaging noise if it differs from the training database.
Database Size and Heterogeneity Traditionally, difficulties in accessing and reliably annotating databases of medical images have led to smaller databases in medical imaging compared to computer vision applications. Recent initiatives such as the UK Biobank project 2  (77) now provide large-scale annotated imaging databases, fuelling a rise in more data-intensive methods such as deep learning. Figure  2  illustrates this high increase over recent years for the studies reported in this paper. The impact of these large databases is high: reporting reference ranges for cardiac functional biomarkers is now possible with much greater confidence  (78, 79) , in addition to detecting effects otherwise hidden with smaller databases, as shown for genome data  (77). Data heterogeneity is also crucial when choosing or curating a database for a specific task, i.
Validation and Standardization Initiatives As analyzing the tracking output is sensitive to processing errors, in particular for multi-centric data, tracking algorithms should be benchmarked to prevent bias due to different manufacturers or settings/practices. To ensure reproducibility of clinical decisionmaking from these data, standardization initiatives arose from academic, clinical, and industrial actors of cardiac imaging. Strain estimation was compared across vendors for synthetic and real images  (81). Outputs were consistent regarding the differentiation between pathological and healthy regions, and the identification of ambiguous zones. However, statistically significant differences among vendors were reported, including differences around 15% for the biggest scars. These differences call for benchmarks on more realistic datasets (both regarding geometry and image quality), obtained e.
CONCLUSION Machine learning offers wide possibilities to automate processing, and notably extract and analyze myocardial motion and deformation. Driven by advances in cardiac segmentation and large databases collection, there is potential for substantially improving the characterization of the cardiac function and impacting clinical practice. Changes cover the automation of time-consuming and user-dependent tasks such as feature extraction, higher performance on supervised problems such as (earlier) diagnosis, prognosis, and risk stratification, and new unsupervised data representations for knowledge discovery such as clustering or phenotyping. Nonetheless, motion and deformation are rich but complex high-dimensional data. Efforts need to be continued to reduce uncertainties, approximations, and crucial misinterpretations along the analysis pipeline, from careful problem definition, compliance with the mathematical and physiological data properties, algorithms benchmarking/validation/testing, and health actors' education.
ACKNOWLEDGMENTS The articles discussed in this review were selected by querying PubMed over the last 10 years with the terms (myocardial [OR] cardiac) [AND] learning [AND] (motion [OR] deformation), complemented by the authors' knowledge, and examining the publication profile of the authors of the already selected articles. Papers not using spatial or temporal motion or deformation patterns but single measurements such as peak values or timings, and papers addressing cardiac respiratory motion were removed from this selection, although we acknowledge their importance for the more complete analysis of cardiac function.