dataesr/scientific-paragraphs-categorization huggingface dataset:
Compound of {{m|en|door}} and {{m|en|bell}} Whenever we find a match to a compound pattern, we ignore everything after the match, as there is no standard for the etymology of compound words. While the selected patterns generally correctly reflect real patterns (as Etymology sections use very well defined standards 9 ), some etymologies are written in non-standard ways, which implies that the corresponding extraction is incorrect (or partially incorrect). We are trying to interact with the community of editors of English Wiktionary to better understand the standards they use and to encourage the use of more standards that would allow the community to have a lower amount of data loss and a lower rate of incorrectly extracted etymological relationships. One example of non-standard Etymology sections uses links instead of templates to represent words that are etymologically related (e.
THE DATABASE The database is installed on the Wikimedia Labs, the Wikimedia Foundation's cloud computing environment. It is managed by Virtuoso version 07. 20. 3217 on Linux. The extracted database consists of 6 million distinct entries (6023380) in 3365 languages, with Latin having the highest number of entries (around 13% or 806999 entries), followed by English (9% or 547506), Italian (8. 5% or 515059), Spanish (7% or 419889), Russian (5. 5% or 331798), French (5% or 305973), Portuguese (4% or 244784), German (3% or 185520). With appropriate queries to the SPARQL endpoint we can ask interesting questions. For example, we can ask which languages English words derive from. The extracted English words derive mostly from other English words (2702), Middle English words (1152), Latin words (1116), French words (832), Old French words (715).
CONCLUSIONS We have presented etytree, a tool to visualize etymological relationships between words in the form of a connected graph. The tool is currently under development but a first working release is available at http://tools. wmflabs. org/ etytree/etymology/resources/html/index. html. This tool can be a valuable resource for people that are interested in the history of words or words in general as they can discover new words in other languages that are etymologically related to the searched words, as well as for etymology enthusiasts, as they can explore etymological relationships in a completely new way. Also we believe that the database can be a valuable resource for linguists as they can study etymologies on a much larger scale. Because of its nature, we believe this work will attract new users to Wiktionary and will improve as well as increase its content.
We also believe it will encourage Wiktionary editors to use more standard rules to format etymologies. We hope that this project will help to turn the whole Wiktionary into a machine readable resource with the minimum possible loss of information. Because of the complexity of the original data contained in Wiktionary (especially the complexity of Etymology sections) the extracted database contains some incorrect entries. We hope that users will contribute to Wiktionary to spot those inconsistencies. We would like to work together with them to improve even more Wiktionary Etymology sections and to improve etytree simultaneously. The project has the potential to grow in both content and quality as it is open source and relies on data coming from a collaborative and multilingual resource as Wiktionary.
FUTURE DEVELOPMENTS As the structure of etymological trees (or graphs) is language independent, this project could be extended to use etymological relationships described in other language versions of Wiktionary, although Etymology sections seem rather incomplete/informal in other languages (Russian might be the next target language). In addition, as the textual part of the tree (definition of words, language tags, etc. ) can be exported from different language versions of Wiktionary, this tool can easily become available in different languages, thus considerably extending its scope. Last, this tool can be integrated into Wikidata when the Wikidata-for-Wiktionary 10 proposal turns into production.
ACKNOWLEDGMENTS This work is supported by the Wikimedia Foundation through an IEG grant 11 to Ester Pantaleo. We would like to thank the Wiktionary and the Wikidata communities for their help and for their precious work. 10 https://www. wikidata. org/wiki/Wikidata: Wiktionary/Development/Proposals/2015-05 11 https://meta. wikimedia. org/wiki/Grants:IEG/A_ graphical_and_interactive_etymology_dictionary_ based_on_Wiktionary.
5 Paleographic skills can often be acquired only through intensive training and prolonged exposure to rare artifacts that can be difficult to access. Much like expert-based methods in the field of art authentication, paleographic knowledge can be difficult to formalize, share, and evaluate. Therefore, paleographers are increasingly interested in digital techniques to support and enhance the traditional practice in the field. 6 Additionally, computer-assisted methodologies for paleographers are now more urgently needed than ever, given the fact that digital libraries such as Gallica, Manuscripta Mediaevalia, BVMM, and (more recently) the Vatican's DigiVatLib are amassing electronic reproductions of medieval manuscripts, sometimes with scarce metadata that are incomplete or out of date. 7 Hiring and training experts to manually enrich or correct the metadata for these collections is expensive and time-consuming.
Methods In this paper, we introduce two complementary methods that have been submitted to the CLaMM competition, each of which ranked first in one of the competition's tasks. One, the DeepScript approach, relies on the use of deep convolutional neural networks, which recently attracted much interest in the computer vision community; and the other one, the FAU submission, uses a more established computer-vision approach, which is known as "Bag of (visual) Words. " In this section, we introduce both methods in nontechnical language that should be accessible to the broad readership of the journal.
3 : these keypoints indicate areas in the images that seem of particular relevance to the model and function as the salient "words" in the BoW model. Note, for instance, how the flourishing of decorated initials invites the detection of many more keypoints than do the page's margins. On the basis of these "visual words," we now have to create a global descriptor for the entire image. The simplest approach would be to take the average of all local descriptors, but more sophisticated methods have shown better performance in the past. 21 These encoding methods typically rely on a background model that needs to be computed from the training data in advance. When presenting an incoming image to the system, a global image descriptor is determined by aggregating statistics drawn from the background model of the local descriptors of this image.
The output layer in the DeepScript network consists of twelve neurons, one for each script class involved in the CLaMM competition. Images are categorized into one of the script types involved according to which output neuron receives the highest activation. Their layered nature sets neural networks apart from other learning techniques that, conventionally, do not have all these intermediary stages between input and output. It has been noted that in these layers different levels of abstraction are captured. In the task of face recognition, for instance, where the system's task is to identify a specific individual, we see that very primitive features are being detected  26  Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, "Deep Learning," Nature 521 (2015): 436-44. 27 The code for applying the FAU system is available online, https://github.
Artificial Paleography Speculum 92/S1 (October 2017) cally in the range of hundreds of thousands of images. The data set of the CLaMM competition, which was already difficult to create in the first place, is rather small from this perspective. Moreover, because of their powerful modeling capacities, the danger exists that networks naively start "memorizing" the training examples: this will result in the undesirable situation that the network produces perfect predictions for the training data-simply because it has learned to memorize the example images-in a manner that does not generalize or scale well to new images that have to be classified. This learning artifact is commonly known as "overfitting. " To combat overfitting, the DeepScript approach proceeded as follows. 31 First, the resolution of the original training images was downsized by a factor of two.
Comparison It is interesting to compare the FAU and DeepScript systems. Both systems share the characteristic that they start from local-feature descriptions in the image, which are subsequently aggregated in a more complete representation of the full image. This attention to low-level information reflects the fact that the manuscripts have been categorized by the annotators based on local, morphological features, such as Derolez', which are exclusively situated at the level of individual characters, instead of, for example, page-level layout information. The exact manner in which 32 Our augmentation derives from that of Sander Dieleman, described here: http://benanne. github. io /2015/03/17/plankton. html.
Artificial Paleography Speculum 92/S1 (October 2017) visual features are detected is nevertheless clearly different. FAU extracts SIFT keypoints using an established generic feature detector, which is known to work well across many problems in computer vision but which cannot be fine-tuned in the light of a specific data set. In other words, the keypoint detection algorithm is fixed and does not get adapted to the particularities of the script-type classification task. DeepScript's neural network approach is, in principle, able to learn more taskspecific filters, but here the limited size of the training data might pose problems for the feasibility of this approach. Note that both approaches explicitly try to reach scaling invariance, which is an important quality of a computer vision system: the detection of a given script type should not break down for scribes who wrote relatively larger or smaller letters, or for manuscripts that were photographed at a different zoom level.
When comparing the confusion matrices for both the FAU and DeepScript, we find it interesting that a number of similar patterns can be gleaned from them. Of particular relevance is the observation that the majority of confusions that arose in both systems make sense from a historical perspective and correspond to paleographical expert knowledge. The various subtypes of textualis script types, for instance, are regularly confused (textualis, Southern textualis, and semitextualis), including at the upper chronological border some examples of Praegothica. The most obvious misclassifications, however, occur between the various forms of Gothic cursive handwritings, such as the (semi)hybrida and cursiva, including the category of the semitextualis script type. The humanistic cursive letter is rarely misclassified, a result showing that what causes the confusion in Gothic scripts is not solely the cursive appearance, but may be some other feature or features specific to a given script family.
To what extent should such misclassifications be considered "errors"? Followup discussions about the competition's results among paleographers showed that the ground-truth annotations provided by the organizers, while certainly defendable, were not always free of controversy. 34 The categorization system used, while trying to discard the geographic component (except for northern and southern textualis), contradicts the classification systems developed by different paleographers in different parts of the world. It merges graphic phenomena that may be disconnected (loopless mercantesca, loopless Dutch hybrida) and separates script types that have a clear historical connection (cancelleresca as cursiva or as semihybrida). 35 Conversely, some geographically defined classifications lack some of the script categories in the CLaMM data altogether and cannot be applied consistently.
Arguably, the competition therefore yielded the valuable insight that our computer systems, although trained on a consistently annotated data set, nevertheless experienced great difficulties in attempting to project this classification model onto new, unseen images. Epistemologically, this sheds an interesting light on the concept of "ground truth" in humanities research. The classification systems would have reached much higher accuracy scores if specific class pairs (such as the southern textualis and the northern textualis for DeepScript, or semihybrida and cursiva for both systems), would have been merged in the ground truth. Therefore the computational modeling of script types raises interesting questions as to the feasibility-or even desirability-of distinguishing between specific 34 These discussions took place at the center of an international seminar "Paléographie numérique: Du défi technique au défi épistémologique / Digital Paleography: From Technical to Epistemological Challenge" at the Fondation des Treilles: see http://www.
Model Inspection and Model Criticism Unsupervised Learning As mentioned above, the FAU system depends on the extraction of SIFT keypoints from images, which are subsequently aggregated and used to train a classification system. The SIFT algorithm has the disadvantage that these generic features are extracted using a fixed algorithm that is hard to optimize in the light of a specific task, such as script-type identification. However, because these features can automatically be extracted from manuscript images, even if we have no other metadata concerning these manuscripts' provenance this result opens up interesting possibilities for "unsupervised" methods that do not depend on the availability of labeled data. An interesting question is therefore how a computational model might categorize the available manuscript images if the model was unaware of the preexisting classification available in the ground truth.
One visualization method is t-Distributed Stochastic Neighbor Embedding (t-SNE). 36 It enables a visualization of high-dimensional data in, for instance, a two-dimensional map that preserves the local structure of the data as much as possible-that is, images close to each other in the high-dimensional space will also lie close to each other in the result. Fig. 11  visualizes the global descriptors of the FAU system. The colors are arbitrarily chosen, but the dimensionality reduction preserves the clusters and their relative positions. With very few outliers, all script types form clearly separated groups, except for the Gothic cursive family (hybrida, cursiva, and semihybrida). While these results might have been expected on the basis of the confusion matrices, such visualization provides new insights.
As was done with the projection of such classes in the Graphem project, 37 we can try to analyze the repartition in evolutional terms. Uncial, Half Uncial, Caroline, and Praegothica are located in the upper left part. Unsurprisingly, Humanistic script lies close to the Praegothica from which it has been imitated, whereas Humanistic cursive seems slightly closer to pure Caroline script. Both humanistic script types can be found, surprisingly, in the middle of the graph. The Gothic evolutions may be read in the trajectory from Praegothica to Textualis and Southern Textualis and a sharp bend towards the Cursiva and (Semi)hybrida types. Semitextualis is in the middle, corresponding to the main threads of the Semitextualis types (the Southern-specific scripts, the simplified scripts used in universities, and the simplified formal Textualis used in late manuscripts in the Low Countries and Germany).
Conclusion In paleography, as is much the case in any other field in the humanities that involves a considerable hermeneutical effort, meaning is under constant negotiation: an impression of "truth" can only emerge when the opinions of the various stakeholders in a field temporarily align and reach an intersubjective equilibrium. In this paper, we discussed a competition in which intelligent machines attempted to repro-Fig. 13. Explanatory filter visualization for some of the neurons in a higher-level layer in the DeepScript network. These patches have been artificially generated in order to maximally excite a particular neuron. duce such a "truth" in the form of a categorization of images of medieval manuscripts in twelve script types. To this end, these machines had access to a reference data set of hand-labeled images as a so-called ground truth.
Our implementation (and a trained model) are publicly available: see https://github. com/mike kestemont/DeepScript. Our code depends on the popular Keras library (https://keras. io/), which serves as an interface to Theano: see Rami Al-Rfou et al. (the Theano Development Team), "Theano: A Python Framework for Fast Computation of Mathematical Expressions," ArXiv 2016, http://arxiv. org/abs/1605. 02688. We gratefully acknowledge the support of NVIDIA Corporation for the donation of the TITAN X used for this research. A special word of thanks goes out to Dr. Sander Dieleman (Google DeepMind, London) for his valuable feedback and help in the development process.
Global Submarine Canyon Protection Status A recent review of global seafloor geomorphic features, based on the analysis and interpretation of a modified version of the SRTM30_PLUS global bathymetry grid  (Becker et al. , 2009) , included a new assessment of submarine canyons and associated geomorphic statistics  (Harris et al. , 2014). The database that underpins the analysis of  Harris et al. (2014)  provides the information needed to estimate the area of canyons within each country's EEZ as well as the area of canyon currently protected within MPAs. Here, we provide an analysis using ArcGIS ESRI tools on the proportion of canyons included in MPAs at the global scale (Figure  6 ; Table  1 ). Statistics included the area estimates for 9477 individual canyons covering a total area of 4,393,650 km 2 (i.
e. , 1. 2% of the total ocean area). In order to estimate the area of canyons within EEZs (i. e. , canyon area that can be protected within the jurisdiction of individual countries, as opposed to those located in the high seas or areas beyond national jurisdiction), global EEZ boundaries downloaded from the VLIZ database were used. In addition, a summary of global MPA boundaries was downloaded from the IUCN and UNEP-WCMC database (IUCN and UNEP-WCMC, 2016) and used to calculate the areas of canyons within MPAs. Results showed that of the overall 4,037,764. 35 km 2 of seafloor found in submarine canyons in EEZs (91. 9% of canyon area), 13. 6% of canyon areas are protected (with at least 10% of their area) within an MPA. Only 10. 3% of all canyons are completely protected (100% of their area) within an MPA (Table  2 , Annex I).
Indonesia and the Philippines had the highest number of canyons within their EEZs (576 and 265 from which 20 and nine were protected respectively, Annex 1). Notably, these results only reflect those canyons protected by management provided under MPA authority. However, there are other mechanisms in place that offer other protective measures (i. e. , world heritage area marine park and fisheries management areas). Yet, no standardized world database exists that includes all protected canyon areas. Therefore, some level of worldwide synthesis, although difficult to achieve, is needed to assess the conservation actions undertaken for canyon protection. The data (see detailed Figure  6  of the Mediterranean Sea) also reveal that most of the marine conservation measurements have focused on shelf ecosystems, while the vast majority of continental slope areas around the world (where submarine canyons are found) are still without any regulations to prevent them from pollution and human exploitation.
Polygon color corresponds to biotopes present, which were mapped using video ground-truthing data and prediction using General Additive Models in the software R. is an area in which more research is needed-with a focus on comparing canyons with different physical environmental regimes in order to clarify the role of potential physical drivers in influencing differences in faunal communities and ecosystem function. While top-down classifications can help design and facilitate broad-scale assessments of MPA networks, the degree to which these coarse classification systems represent finer-scale distribution of biological assemblages is still questionable  (Williams et al. , 2009). In addition, they do not allow the identification of areas where assemblages and species recognized as VMEs or in need of special protection measures are located.
ACKNOWLEDGMENTS This paper has been prepared in the framework of the  INCISE network. UF was funded by the  Balearic Government postdoctoral  grant  2016 , co-financed by European Social Plan; VH was supported by the  CODEMAP  project ( ERC Starting Grant  no  258482 ) and the  NERC  MAREMAP programme; PP has been funded by the  ABIDES  project ( CTM2015-65142-R ); AR was supported by  NIWA  research project " Impact of resource use on vulnerable deep-sea communities " (MBIE contract  CO1X0906 ). IV was funded by  Ifremer and Région Bretagne. ALA is supported by  Science Foundation Ireland / Marine Institute IvP  award  15/IA/3100.
During the 1 st millennium before the Common Era (BCE), nomadic tribes associated with the Iron Age Scythian culture spread over the Eurasian Steppe, covering a territory of more than 3,500 km in breadth. To understand the demographic processes behind the spread of the Scythian culture, we analysed genomic data from eight individuals and a mitochondrial dataset of 96 individuals originating in eastern and western parts of the Eurasian Steppe. Genomic inference reveals that Scythians in the east and the west of the steppe zone can best be described as a mixture of Yamnaya-related ancestry and an East Asian component. Demographic modelling suggests independent origins for eastern and western groups with ongoing gene-flow between them, plausibly explaining the striking uniformity of their material culture.
Most genetic studies on the later Iron Age nomads, however, have been limited by small sample size, restricted to a single cultural group, or based on the analysis of mtDNA alone  [18] [19] [20] [21] [22]. In this study, mtDNA data from 96 individuals associated with the Scythian culture in different geographical regions and time periods have been sequenced and analysed; additionally, genomic data from eight of these individuals was obtained and analysed (Supplementary Table  1 ). From the western part of the Eurasian Steppe, samples discovered in the North Caucasus dating to the initial Scythian period (eighth to sixth century BCE), classical Scythians from the Don-Volga region (third century BCE), and Early Sarmatians from Pokrovka, southwest of the Ural (fifth to second century BCE), were included.
Results Samples and sequence data. We generated genome-wide capture data on a target set of 1,233,553 SNPs  26, 27  for six individuals: two Early Sarmatians from the southern Ural region (PR9, PR3, group #3 in Fig. 2 ; fifth to second century BCE), two individuals from Berel' in East Kazakhstan (Be9, Be11, #6) dating to the Pazyryk period (fourth to third century BCE), and two individuals found in kurgan Arzhan 2 (A10, A17, #5) assigned to the Aldy Bel culture in Tuva (seventh to sixth century BCE). For Be9 and two additional individuals from east Kazakhstan (Is2 and Ze6, #4) dating to the Zevakino-Chilikta phase (ninth to seventh century BCE), we generated low coverage (o0. 3x) whole genome datasets by shotgun NGS (Table  1 , Supplementary Tables  20  and 21 ). We additionally obtained unambiguous and reproducible mtDNA sequences of the hypervariable region 1 (HVR1; bp 16,013-16,410) for 96 samples (out of 110 samples for which the analysis was attempted) (Supplementary Table  5 ).