dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of the two periods Even if we only consider the tsunamis reported in the NOAA NGDC catalogue, it corresponds to 11 out of 854 events for the 100-year period from 1 January 1900 to 29 September 2009 (included) and 14 out of 113 events for the 10year period from 30 September 2009 to 10 January 2019, i. e. , 1. 29 % and 12. 39 %, so an increase by a factor of 10. This is likely due to the lack of tide gauge data before 2009, highlighting the importance of sea-level data. Indeed, to compare the two periods exactly, we should consider the same number of days, i. e. , 9 years, 3 months and 10 d. Thus for the period from 19 June 2000 to 29 September 2009 the decision table extracted 111 events potentially able to trigger tsunamis able to reach New Caledonia (this number is very close to the 113 events extracted after 29 September 2009 and to the 96 events extracted from 9 March 1991 to 19 June 2000).
Limitations of the methodology This methodology using an extraction decision table is based only on the reported or recorded tsunamis generated by earthquakes and, thus, does not consider tsunamis triggered by landslides or, less frequently, by volcanic eruptions, representing about 7 % and 5 %, respectively, of the reported tsunamis of the NOAA tsunami database according to  Harbitz et al. (2014). In the available data, there is no evidence of tsunamis related to active volcanism or landslides. Any-way, active submarine volcanoes exist in the neighborhood of New Caledonia, especially off the Loyalty Islands (Gemini seamounts, 200 km east of Maré and south of Aneytum, Vanuatu) on the Vanuatu volcanic arc. In addition, numerous submarine landslides have been mapped along the margins of Grande Terre and the Loyalty Islands.
e. , not always in sheltered areas but rather in places facing main tsunami pathways. It also brings to light the necessity of installing more sensors in exposed areas like on the east coast of the Isle of Pines. Data availability. This paper does not have underlying research data. All the datasets used herein, such as the USGS, the NOAA or the Russian catalogues are indicated in the paper, as well as the marigraphic data from ReefTEMPS or REFMAR projects. The new data provided by this paper are given in the New Caledonia tsunami catalogue (presented in the paper) and the wave amplitudes indicated in Tables  2  and 3. Author contributions. JR, BP and JA conceived this study. JR collected and analyzed all data and prepared the figures. JR and BP wrote the paper with assistance and input from JA.
Acknowledgements. This study is part of a national project of tsunami hazard assessment (TSUCAL) funded by the local government of New Caledonia. The authors would like to thank the  SHOM  for providing tide gauge data (http://doi. org/10. 17183/REFMAR, last access: January 2019) and the ReefTEMPS coastal sea waters' observation network for providing pressure gauge data (http://doi. org/10. 17882/55128 ,last access: January 2019). They would also like to acknowledge the constructive comments of  Viacheslav K. Gusiakov , two anonymous referees and the editor  Maria Ana Baptista. Review statement. This paper was edited by  Maria Ana Baptista  and reviewed by two anonymous referees.
244 6. 8 11. 0 Vanuatu (southern) usp000j1a8 -19. 125 6. 7 16. 0 Vanuatu (southern) usp000jfzj -8. 899 6. 8 12. 6 Solomon us10003zcp -21. 638 6. 3 14. 0 Vanuatu (southern) us2000brbk -21. 503 6. 6 13. 0 Vanuatu (southern) us2000brgk -21. 743 6. 5 17. 0 Vanuatu (southern) us1000hclz Longitude ( • ) 142. 373 183. 660 168. 143 183. 762 227. 899 165. 114 289. 231 162. 148 162. 051 288. 326 161. 327 161. 132 153. 522 266. 101 169. 148 168. 858 168. 671 170. 126 169. 418 168. 226 169. 613 158. 422 168. 673 168. 598 169. 522 Earthquake time NOAA 11 Mar 2011, 05:46:24. 120 Z NGDC/WDS 6 Jul 2011, 19:03:18. 260 Z Global 20 Aug 2011, 16:55:02. 810 Z Historical 21 Oct 2011, 17:57:16. 100 Z Tsunami 28 Oct 2012, 03:04:08. 820 Z Database 6 Feb 2013, 01:12:25. 830 Z 1 Apr 2014, 23:46:47. 260 Z 12 Apr 2014, 20:14:39.
Analysis of RAD51B tumor-derived mutations The RAD51 paralog mutant cell lines provide a means to rapidly analyze the impact of patientderived mutations by functional complementation. As a proof of concept, we identified five highly conserved residues in RAD51B that are mutated in tumors from the MSK-IMPACT database, three of which are located in the Walker motifs  (Fig 7A and S7 Fig). We overexpressed these mutant RAD51B proteins in RAD51B knockout HEK293 cells and analyzed them in HR assays. Three of the five, RAD51B G108D, V207A, and G341V, were unable to complement the HR defect of RAD51B knockout cells, whereas the other two, RAD51B K114Q and R159H, were able to restore HR to wild-type levels (Fig  7B ). We further performed yeast 2-hybrid analysis to determine if the interaction with RAD51C was affected by these mutations.
Doubling time Doubling times were measured using the CellTiter-Glo Luminescent Cell Viability Assay (Promega) using manufacturer recommendation. Briefly, 5000 cells were seeded in 96-well plates and measurements were performed initially and 24, 48, 72 and 96 h. Cell doubling time was calculated using GrahPad Prism software by nonlinear regression (exponential growth equation) analysis.
DR-GFP homologous recombination assays Post-Cre MCF10A cells were infected with I-Sce-I expressing lentivirus, cells were washed 24 h later and HR was measured 48 h later by quantifying the percentage of GFP+ cells by FACS (Becton Dickinson FACScan). U2OS and HEK293 cells were seeded into 10 cm plates 24 h prior to transfection with 2. 5 μg of the I-SceI expression vector pCBA-SceI (Addgene plasmid # 26477,  [105]  or an empty vector in combination with 0. 5 μg of pcDNA-RFP using polyethylenimine (PEI) at a ratio of 2:1 of PEI:total DNA. Cells were collected 72 h post-transfection and multicolor FACS analysis was performed with a LSRII apparatus (Becton Dickinson). Data were analyzed with FlowJo software (Tree Star, Ashland). RFP signal was used as transfection efficiency control and double positive cells (GFP+/RFP+) were counted as positive for HR events.
Results were represented as a ratio of double-positive cells to the total number of RFP-positive cells. For transient complementation assay, 2 μg of pCMV plasmids expressing RAD51 paralogs, RAD51, RAD52 or BRCA2 were transfected in combination with 2. 5 μg of pCBA-SceI and 0. 5 μg of pcDNA-RFP. For stable complementation assays, cells transfected with retroviruses expressing RAD51 paralogs were used. For RAD51B cancer-associated mutant analysis, RAD51B -/-HEK293 cells were transfected with 1 μg of the I-SceI expression vector pCBA-SceI and 1 μg of the RAD51B expression vector. Cells were collected 48 h post-transfection and FACS analysis was performed by flow cytometry (BD FACScan), and data were analyzed using FlowJo software.
Authentication of cell lines and confirmation of targeted gene disruptions For authentication of cell lines, genomic DNA was isolated using the High Pure PCR Template Preparation Kit (Roche Life Science). STR DNA genotyping at DSMZ was carried out as described previously using a nonaplex PCR reaction of eight highly polymorphic STR loci plus Amelogenin-based gender determination  [111]. Generated STR profiles have been compared with the international STR Reference Database of DSMZ  [112]. All derivatives of MCF-10A, U2OS and HEK293 showed full authenticity. DSMZ performed an independent confirmation of gene disruptions in U2OS SCR#18 and HEK293-DR-GFP derivatives specific for RAD51B, RAD51C, RAD51D, XRCC2, and XRCC3 disruptions as well as their reconstituted counterparts. Using primers presented in S6 Table, targeted DNA regions were amplified and respective amplicons cloned into pGEM-T (Promega) and the products subjected to Sanger sequencing.
This work was supported by  CRCM  state subsidies from  CNRS, Inserm, Institute Paoli-Calmettes and Aix-Marseille University ; grant  ANR-II-INSB-0014  (J-P. C. , C. G. );  MSK Cancer Center Support Core Grant  ( NIH P30CA008748 );  NIH  Grants  F32GM110978  (R. P. ),  R35GM118175  (M. J. ),  R01CA185660  (M. J. ), F31ES027321 (M. R. S. ),  ES024872  (K. A. B. ); in part by the  Marie-Josee and Henry R. Kravis  Grant  129182-RSG-16-043-01-DMC (K. A. B. ) ;  Stand Up  to Cancer Grants  SU2C-AACR-IRG-02-16 (K. A. B. )  and  SU2C-AACR-DT16-15  (M. J. ); Postdoctoral fellowships from the  French National League against Cancer  and from  Aix-Marseille University Foundation  (E. B. G. ); and the  French National League against Cancer  (M. M. ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The Modern Analogue Technique (MAT) is considered the most suitable method for large-scale climate reconstructions from terrestrial and marine pollen sequences, especially when the training set encompasses a wide range of vegetation and climate zones  (Brewer et al. , 2007; Juggins and Birks, 2011). In this case, we complied with this assumption using the extensive European Modern Pollen Database  (Davis et al. , 2013). We reconstructed a range of climate parameters usually estimated from pollen data, namely mean monthly summer (JJA), winter (DJF) and annual temperature and precipitation.
Ichnological research This research was based on digital image analysis treatment  (Dorador and Rodríguez-Tovar, 2018) , on selected cores of IODP Site U1385. The technique is based on image adjustment modifications to enhance ichnoassemblage visualization and characterization. Three adjustment modifications (levels, brightness and vibrance) were applied to the highresolution images using Adobe Photoshop CS6 software ® for enhancing the visibility of biogenic structures. Ichnotaxonomic identification is mainly based in ichnological observations achieved from cores  (Knaust, 2017). In each of these images, ichnofabric attributes (i. e. , ichnoassemblage, cross-cutting relationships and degree of bioturbation) are characterized. Quantitative estimation on the percentage of bioturbation was obtained by the application of the Ichnological Digital Analysis Images Package  (Dorador and Rodríguez-Tovar, 2018).
Time series analyses We used REDFIT  (Schulz and Mudelsee, 2002)  to estimate the Fourier spectrum directly from the unevenly spaced time series of the Mediterranean forest pollen percentages, and we removed the linear trend before estimating the spectrum. One of the main advantages of REDFIT is that this method is able to separate real signals from the red noise background. To explore potential climate regime shifts contained in the paleoclimate data under analysis, we used the change point method proposed by  (Bai and Perron, 2003) , as implemented in the R package strucchange  (Zeileis A. et al. , 2002). This statistical tool identifies the age where there exists a significant structural change in the times series analysed providing the 95% CI (confidence interval) of the change-point, but this tool works only with evenly spaced ("regular") time series.
For this reason, we interpolated the unevenly spaced time series of pollen percentages through Akima method using intervals of 200 years. Furthermore, others interval lengths ("100" and "300" years) were used, but the results did not change and are not shown. To estimate the wavelet spectrum to the interpolated pollen percentages (using the same preprocessing strategy such as was described previously) via the Morlet continuous wavelet transform we used the method of  (Liu et al. , 2007) , as implemented in the R package biwavelet  (Gouhier and Grinsted, 2014). Please note that it is not necessary to remove a linear trend in the time series of pollen percentages because wavelet spectral analysis is designed to work with non-stationary time series.
ACKNOWLEDGMENTS J. M. P. M. was funded by a  Basque Government post-doctoral fellowship and MAG and TR by FCT  ( PTDC/MAR-PRO/3396/2014 ,  UID/Multi/04326/2013 ,  SFRH/BPD/96960/2013 ,  SFRH/BPD/108600/2015 ). We acknowledge  C. Morales-Molino  and  D. Oliveira  for the reading of the manuscript and  V. Hanquiez  for drawing Figures 1 and 9. We are grateful to  Ludovic Devaux  for pollen sample preparation.
2004 , Hudson et al. 2017 , Sauer et al. 2017b). Spatial and temporal gaps in the coverage of the long-term programs may be filled by aggregating data across many finer scale, independent studies, but doing so requires careful consideration to reconcile variations in survey protocols  (Matsuoka et al. 2014 , Barker et al. 2015). Different monitoring programs that have been conducted in the boreal forest are being integrated by the Boreal Avian Modelling Project (BAM; http:// www. borealbirds. ca/; Fig. 2 ). Government agencies have also continued to examine new methods that complement the coverage provided by existing programs, such as the deployment of automated recording units (ARUs) or the development of new survey methods, but the process of integrating data from existing and new programs is an open area of research  (Venier et al.
2012a , Rempel et al. 2013 , Pacifici et al. 2017 , Pankratz et al. 2017 , Thompson et al. 2017 , Vold et al. 2017). The emergence of citizen data collection programs such as eBird, a citizen science data repository for bird sightings (http://www. ebird. org; Fig. 5 ), and iNaturalist (https://www. inaturalist. org/) can provide monitoring data from across broad spatial scales. However, their reliance on volunteers leads to strong spatial bias favoring densely populated locations  (Fletcher et al. 2019) , and these programs are thus of limited value in remote areas such as the boreal forest. Given the growing gap between the extent of current field studies in the boreal forest and the extent of emerging conservation issues because of resource extraction and climate change, there is an urgent need to develop more effective monitoring in the boreal forest  (Cumming et al.
Abundance surveys The tools used to analyze the trends and trajectories of a bird population will be dependent on the types of data being collected. The most straightforward approaches involve estimating bird abundance, density (abundance per unit area), or indices of relative abundance through a count-based survey. The measurements are generally extrapolated to estimate the overall population size for a given survey area. Depending on the goals of the survey and how the data were collected spatially, abundance data collected over a time period can be analyzed either as a single population or as multiple populations in a multivariate framework. For single-population models, either each sampling site is analyzed independently or all of the sampling sites are assumed to share a common set of parameters governing abundance.
2 ). Because BAM aggregates point-count and point-transect data, the aggregated data set is also phylogenetically biased against species that are not well surveyed by these methods, e. g. , birds with large home ranges or limited vocalizations. Many of the data sets represented in the BAM database also have limited temporal replication  (Barker et al. 2015) , although some constituent programs have been specifically designed with broad spatial and temporal replication (e. g. , Alaska Landbird Monitoring Survey;  Handel and Sauer 2017). Efforts to increase spatial coverage and temporal replication across the boreal forest within programs contributing to the BAM database would improve our ability to draw inferences about boreal passerine population trends. Citizen science programs provide useful if incomplete data for understanding boreal bird population status and trends.
2019 ). However, they are restricted by the paucity of historical data in the northern boreal forest. As of the last comprehensive assessment of the BBS survey in Canada  (Dunn et al. 2005) , there were at least 66 species of landbirds (∼21% of the species) for which trends could not be adequately estimated in large part because of the lack of coverage of the boreal forest. Dunn also pointed out that the trends estimated for some species, such as the Blackpoll Warbler (Setophaga striata), were probably unreliable because the BBS sampled the population only at the fringe of its breeding distribution. Little has changed in the intervening years to ameliorate that situation. In the absence of repeated counts over a long period of time, few options remain for examining historical variation in avian abundance and correlated drivers.
Improve data availability Survey data are increasingly being used not only to monitor basic population trajectories but also to address key ecological questions that will inform conservation of boreal forest birds. The growing complexity of questions being asked also tends to outpace the amount of explanatory data available to fit the model. Although amassing and standardizing large data sets such as those integrated in BAM will help answer many questions, there are undoubtedly additional, as yet unassimilated data that could partially fill spatial and temporal gaps in sampling. Data sets with survey data and explanatory variables that are not publicly available or that do not even have publicly available metadata to highlight their existence could be invaluable for such efforts. A key step toward facilitating greater use of existing data would be the creation of a publicly accessible archive of metadata, particularly for survey data, and publicizing this accessibility.
2010 , Barker et al. 2015). For explanatory variables, such as fire regime, land cover, or insect abundance and distribution, addressing the issue of ownership of the different data sets and consolidating the different regional products into an integrated database could also greatly enhance population modeling in the boreal forest. The ongoing effort, initiated by various governmental agencies, to move some of the climatic and landscape variables to open source has been invaluable. There is still, however, a lot of work to be done not only to make the data sets available but also to publicize their availability and maintain and update them once they have been published on an open data portal.
Spatially balanced sampling designs with rotating panels could alleviate the cost of implementing these new surveys while also providing spatial and temporal coverage sufficient to detect significant population declines. Second, the best use of the data collected through monitoring would be achieved through the development of mechanistic population models for migratory birds. However, these models will require the appropriate spatiotemporal environmental covariates. Improving the accessibility of databases holding the explanatory variables will play a key role in our ability to develop a sound understanding of the population dynamics of migratory birds in the boreal forest. Given the magnitude and complexity of this challenge, it will be important to develop multidisciplinary working groups to coordinate the development of new monitoring strategies and the consolidation of monitoring data and explanatory covariates.
Acknowledgments: We thank  S. Cumming ,  E. Elizondo ,  P. Flint ,  J. Grand ,  M. Kéry ,  S. Matsuoka ,  G. McNeil ,  T. Meehan ,  J. Pearce ,  O. Robinson ,  V. Ruiz ,  W. Thogmartin ,  C. Wilsey ,  S. Wilson ,  J. Wu , and an anonymous reviewer for useful discussions and feedback on the manuscript. We also thank the organizers of the  Conservation of Boreal Birds Workshop at the 2016 North American Congress for Conservation Biology in Wisconsin  for giving us the opportunity to present and discuss these thoughts. This work was supported in part by funding from the  Wildlife Program of the U. S. Geological Survey's Ecosystem Mission Area. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. government.
Section 2 discusses related work on link prediction. Section 3 contains preliminaries about knowledge graphs and queries. Section 4 recalls the definition of CNNs, and their efficient computation. Section 5 presents our method to perform link prediction, using CNNs and Dempster-Shafer theory. Section 6 reports positive experimental results on benchmark FB15k-237 and two other datasets. Finally, Section 7 concludes and sketches future work.
The existing methods vary by how they learn the embeddings, and how they combine them. Those methods are based on a range of techniques including: matrix factorization, tensor factorization, neural networks, and gradient descent. For example, one of the first method for KGs, TransE  [2] , models a relation as a translation in the embedding space of entities, and scores a candidate triple according to the distance between the translated head and the tail. Bordes et al also introduced two datasets, FB15k and WN18, respectively derived from Freebase and Wordnet, which became references in the evaluation of link prediction methods. Toutanova and Chen  [19]  however showed that a very simple method was able to outperform previous methods because of a flaw in the datasets: many test triples have their inverse among the training triples.
Methodology Datasets. We use three datasets to evaluate our approach. Table  1  provides statistics about them (numbers of entities, relations, train edges, validation edges (if any), and test edges). The main dataset is FB15k-237, introduced by Toutanova and Chen  [19]  as a challenging subset of dataset FB15k, which was formerly introduced Task. We follow the same protocol as introduced in [2], and followed by subsequent work. The task is to infer, for each test triple, the tail entity from the head and the relation, and also the head entity from the tail and the relation. We call test entity the known entity, and missing entity the entity to be infered. We evaluate the performance of our approach by using the same four measures as in  [18] : MRR and Hits@{1,3,10}. Like in previous work, we use filtered versions of those measures to reflect the fact that, for instance, there may be several correct tail entities for a 1-N relation (e.
g. , the relation from awards to nominees). For example, if the correct entity is at rank 7 but 2 out of the first 6 entities form triples that belong to the dataset (and are therefore considered as valid), then it is considered to be at rank 5. Method. Because our approach has no training phase we can use both train and validation datasets as examples for our instance-based inference. Our approach has only two (hyper-)parameters (and no parameter to learn) for the computation of CNNs: the depth of the description of the test entity, and the timeout (i. e. the allocated computation time). We study the sensitivity to those parameters. For the inference of a ranking of entities, we set α 0 =0. 95 and use all computed CNNs (no selection of the k-nearest CNNs). The implementation of our approach has been integrated to SEWELIS as an improvement of previous work on the guided edition of RDF graphs  [9].
A standalone program for link prediction is available from the companion page. We ran our experiments on Fedora 25, with CPU Intel(R) Core(TM) i7-6600U @ 2. 60GHz, and 16GB DDR4 memory. So far, our implementation is simple and uses a single core, although our algorithm lends itself to parallelization. We have observed that in all our experiments the memory footprint remains under 1. 5%, i. e. about 240Mb. Table  2. Results on FB15k-237 for Freq, latent-based approaches (TransE, DistMult, HolE, ComplEx, R-GCN, ConvE), a rule-based approach (AMIE+), and our approach (CNN) with three timeouts (0. 01s, 0. 1s, 1s): *-results are from  [18] , **-results are from  [4].
We also report better results on FB15k-237 from  [14] , although we were not able to reproduce them. We add yet another baseline Freq that simply consists in ranking entities e j according to their decreasing frequency of usage in r k over the train+valid dataset, as defined by freq j =|ans(x←(x,r k ,y),y =e j )|. It is independent of the test entity, and therefore acts as a default ranking.