dataesr/scientific-paragraphs-categorization huggingface dataset:
Approach Let T s be a tractography dataset of an individual subject consisting of a collection of fibers or streamlines, where each fiber is formed by 21 points in IR 3. The streamlines on a dataset are loaded into the main memory following the order of points calculated during the tracking. Hence, two orientations are possible: direct, or reverse (flipped), which must be considered in the streamline analysis. In this work, the similarity between fibers is defined by the maximum Euclidean distance between corresponding points (d ME )  (Guevara et al. , 2011b (Guevara et al. , , 2012 (Guevara et al. , , 2017;; Rom an et al. , 2017). It is a restrictive distance, since any relevant local difference between the fibers, on the fiber extremities and along all their shape, will be successfully captured.
STEP 1 Building point clusters This step aims at reducing the dimensionality of the input data by applying a partition clustering on a subset of streamline points. Applying the clustering locally on a subset of fiber points reduces the number of dimensions of the input elements and then the number of pairwise distance computations needed to form clusters. Distance computations are performed on three dimensions fiber points instead of fibers formed by 21 points, where each point has three dimensions. The method uses a subset of five points, including the two ending points (1, 21), the central point (  11 ) and two intermediate points  (4, 18). The decision of using these points is given because it has been shown that this sampling strategy is efficient and significant to estimate the maximum distance between fibers  (Labra et al.
The method provides high quality clusters, but it has about 13 configuration parameters and it is time consuming. QuickBundles  (Garyfallidis et al. , 2012)  builds clusters using a greedy approach, and it uses a distance threshold to define whether a new fiber is assigned to the closest cluster or will start a new cluster. It is based on the Minimum average Direct-Flip (MDF) fiber distance, although other fiber distance measures can be used. QuickBundlesX  (Garyfallidis et al. , 2016)  optimizes the QuickBundles algorithm using a tree data structure. More details in Supplementary file. FFClust method was implemented in Python version 3. 6 and in C language using compiler gþþ version 7. 4. 0. The method supports sequential and parallel execution using OpenMP. All experiments were executed in a machine consisting of a 12-core Intel Core i7-8700K CPU with 3.
70 GHz,680, and 32 GB of RAM, using Ubuntu 18. 04. 2 LTS with kernel 4. 15. 0-51 (64 bits). The experiments were performed using deterministic tractography datasets from the ARCHI database  (Schmitt et al. , 2012) , with one million of streamlines. To compare execution times, we use tractography datasets with resampled subjects from 330,000 to 2,729,000 streamlines.
Finding the number of fiber point clusters First, the number of clusters for applying MK in STEP 1 is determined using the Elbow method  (Kodinariya and Makwana, 2013). To do this, the MK algorithm is executed for each of the five streamline points of a subject of one million fibers of a subject of the ARCHI database with 50, 150, 200, 250, 300, 350, 400 and 450 clusters. Fig. 2  displays the total intra-cluster variation or total within-cluster sum of squares (WCSS) for different number of clusters for the five streamline points. The elbow method shows the total WCSS as a function of the number of clusters. As the number of clusters increases the WCSS decreases, which indicates that clusters get more compact. The idea of the elbow method is to choose a number of clusters where the WCSS does not decrease much when using more clusters.
, 2011a) , which uses a maximum Euclidean distance of 10 mm. The first evaluation, showed in Fig. 3 , considers the quality of the clustering approaches, by computing the intra-cluster, inter-cluster maximum Euclidean distances and the cluster sizes obtained by all the alternatives. The comparison includes the error bars using 50 subjects, with tractography datasets of 1,045,676 fibers in average. Fig. 3-(top)  shows the number of clusters with corresponding intracluster distance, Fig. 3-(middle)  shows the number of clusters with inter-cluster distance, and Fig. 3-(bottom)  shows the number of clusters with cluster sizes. As observed, FFClust provides clusters with small intracluster distance, where all clusters have distances below 60 mm. QuickBundles with MDF of 6 mm also provides clusters with small intracluster distance, but the number of clusters with intra-cluster distance over 30 mm is greater than the number of clusters FFClust generates.
Note that FFClust eliminates only small clusters containing 1 or 2 fibers. Another experimental evaluation was performed to consider QuickBundles and QuickBundlesX using maximum Euclidean distance instead of the MDF. However, both methods become time consuming, taking days to complete on a dataset of 100,000 fibers. Experiments showed the quality of clusters, considering the same measures, i. e. intracluster and inter-cluster maximum distances, and cluster sizes, are similar to FFClust's. The Supplementary file contains the results for this experiment. A third quality clustering evaluation uses the Davies-Bouldin (DB) index  (Xu and Tian, 2015). The DB index is defined as the average similarity between each cluster with its most similar cluster, where similarity refers to the ratio of intra-cluster and inter-cluster distances.
Runtime comparison This section evaluates the execution time of FFClust with state-of-theart methods. FFClust-seq denotes the sequential version of FFClust, where the complete algorithm is executed using only one thread. FFClustpar denotes the parallel implementation of FFClust, where each of the steps of the algorithm is executed using five threads in the first step and 12 in the other steps. The methods used for comparison are: QuickBundles with MDF of 6 mm (QBmdf6), with MDF of 10 mm (QBmdf10), QuickBundlesX with MDF of 6 mm and 10 mm (QBX). However, we did not include the Guevara  (Guevara et al. , 2011a)  method for this experiment because its execution times are longer than 2 h for any dataset, far exceeding the execution times of algorithms such as QB and FFClust, which were designed to be efficient.
The evaluation was performed using subjects from the ARCHI database with a number of streamlines varying from 330,000 to 2,729,000. Table  1  shows the execution times in seconds for all considered methods. Fig. 13  shows the execution times in logarithmic scale of the methods. FFClust-seq and FFClust-par provide the best execution times. It is at least an order of magnitude faster than QuickBundles. Also, we can see the trend of the QB algorithm, where the execution time increases when MDF is set to 6 mm, instead of using the QB default value of 10 mm.
Discussion and conclusions We propose FFClust, a new fast clustering algorithm for large wholebrain tractography datasets of the brain's white matter. We compare our clustering results with the state-of-the-art clustering using the Quick-BundlesX (QBX)  (Garyfallidis et al. , 2016) , QuickBundles (QB)  (Garyfallidis et al. , 2012)  and Guevara  (Guevara et al. , 2011b)  methods. After tuning the parameters of all methods, our experimental evaluation shows that FFClust identifies homogeneous clusters with a moderate maximum intra-cluster Euclidean distance and still it is able to find large clusters. Using the DB index as a metric of clustering quality, we found that QB, using MDF of 6 mm, is the best and FFClust is the second best, whereas using QB with the default distance MDF of 10 mm its DB index is less competitive.
Acknowledgements This work has received funding by the  ANID PFCHA/DOCTORADO NACIONAL/2016-21160342 ,  ANID FONDECYT 1190701 ,  ANID PIA/ Anillo de Investigaci on en Ciencia y Tecnología   ACT172121 ,  ANID-Basal  Project  FB0008  ( AC3E ),  ANID-Basal  Project  FB0001  (CeBiB), and by the  European Unions Horizon 2020  research and innovation programme under the  Marie Sklodowska-Curie  grant agreement No  690941. This work was also partially funded by the  Human Brain Project , funded from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreements  N 945539 (HBP SGA3 ), No. 785907  ( HBP SGA2 ) and No: 604102  ( HBP SGA1 ).
No readily available parsing and machine translations systems are available for such languages. Taking as an example the Arabic dialects spoken in North-Africa, mostly from Morocco to Tunisia, sometimes called Maghribi, sometimes Darija, these idioms notoriously contain various degrees of code-switching with languages of former colonial powers such as French, Spanish, and, to a much lesser extent, Italian, depending on the area of usage  (Habash, 2010; Cotterell et al. , 2014; Saadane and Habash, 2015). They share Modern Standard Arabic (MSA) as their matrix language  (Myers-Scotton, 1993) , and of course present a rich morphology. In conjunction with the resource scarcity issue, the codeswitching variability displayed by these languages challenges most standard NLP pipelines, if not all. What makes these dialects especially interesting is their widespread use in user-generated content found on social media platforms, where they are generally written using a romanized version of the Arabic script, called Arabizi, which is neither standardized nor formalized.
Interestingly, the lack of Arabic support in input devices led to the rise of a romanized written form of this dialect, which makes use of alphanumeric letters as additional graphemes to represent phonemes that the Latin script does not naturally cover. Not limited to North-African dialectal Arabic, this non-standard "transliteration" concurrently emerged all over the Arabic-speaking world, and is often called Arabizi. Whether or not written in Arabizi, the inter-dialectal divergences between all Arabic dialects remain. The following list highlights some of the main properties of Arabizi compared to MSA written in the Arabic script. • Unlike in MSA written in the Arabic script, where short vowels are marked using optional diacritics, all vowels are explicitly written; • Digits are used to cope with Arabic phonemes that have no counterpart in the Latin script; for instance, the digit "3" is often used to denote the ayin consonant, because it is graphically similar to its rendition in Arabic script; • No norms exist, resulting in a high degree of variability between people writing in Arabizi.
From now on, we will call NArabizi the Algerian dialect of Arabic when written in Arabizi, thereby simultaneously referring to the language variety and to the script itself. Table  1  presents several examples of lexical variation within NArabizi. Interestingly, this variability also affects the codeswitched vocabulary, which is mostly French in the case of NArabizi. A typical example of NArabizi that also exhibits code-switching with nonstandard French spelling can be seen in Example 1. (.
Data Collection Cotterell et al. (  2014 )'s corpus was collected in 2012 from an Algerian newspaper's web forums and covers a wide range of topics (from discussion about football events to politics). We collected the 9973 raw sentences from its GitHub repository 4  and sampled about 1300 sentences. In addition, because they were available with translations in French and English, we included lyrics from a few dozen recent popular songs of various genres  (Raï, hip-hop, etc. ) , leading to an additional set of 200 sentences. These 1500 sentences form the core of our NArabizi treebank annotation project. In order to make our corpus usable by modern, resource-hungry natural language processing techniques, we also used data-driven language identification models to extract NArabizi samples among the whole collection of the Common-Crawl-based OSCAR corpora  (Ortiz Suárez et al.
In addition, we added several morphological features following the Universal Dependency annotation scheme  (Nivre et al. , 2018) , namely gender, number, tense and verbal mood. Note that instead of adding lemmas, we included French glosses for two reasons: firstly for practical reasons, as they helped manual corrections done by non-native speakers of NArabizi, and secondly because of the non-formalized nature of this language, which makes lemmatization very hard, almost akin to etymological research as in the case of garjouma/the throat which can either originate from French gorge or be of Amazigh root. Code-Switching identification Unlike other works in user-generated content for minority languages  (Lynn and Scannell, 2019) , we do not distinguish between inter-and intra-sentential code-switching and consider word-level codemixing as lexical borrowing.
Extending Our Data Set With Noisy Unlabeled Data The need for more data has never been more striking as they are needed for important tasks such as handling lexical sparseness issues via word embeddings, lexicon acquisition, domain adaptation via self-training, or fine-tuning pre-trained language models, its modern incarnation. The trouble with NArabizi is that it is a spoken language whose presence can be mostly found in informal texts such as social media. More importantly, the Arabizi transliteration process is also used by other Arabic dialects, making the data collection a needle in a haystack search task. We therefore present in this section the process we used to mine an additional set of 50k.
First method: SVM-based classifier Using keywords-based web scrapping tools, we collected a raw corpus of 4 million sentences, called CrawlWeb, that in fine contained a mixture of French, English, Spanish, MSA and Arabizi texts. Since we are only interested in NArabizi, we designed a classifier to extract proper sentences from that raw corpus. The corpus we used as gold standard is made of 9k sentences of attested NArabizi from our original corpus and 18k of French and English tweets. Using language identification  (Lui and Baldwin, 2012) , we convert each sentence from the gold-standard corpus to a feature vector containing language-identification scores and use it as input to a SVM classifier with a classical 80/10/10 split. With a precision and recall score of 94%, we filtered out 173k code-mixed sentences out of the CrawlWeb corpus.
Second method: Neural-based classification The objectives of this method are twofold: (i) selecting data from CommonCrawl using a neural classifier and (ii) using this data set to intersect the data collected with the previous method. The idea is to ensure the quality of the final resulting unlabeled corpus. Given the large number of noisy data in Com-monCrawl, a "noise" class is added to the language classification model and is built according to several heuristics. 6 That "noisy" class corpus is made of 40k sentences randomly selected among the result of the application of these rules to a short, 10M-sentence sample of CommonCrawl. We then trained a classifier using Fasttext  (Joulin et al. , 2016)  on 102 languages, 40k sentences each, extracted from the CommonCrawl-based, languageclassifed OSCAR corpus, to which we added the 9k sentences of the NArabizi original corpus and the "noise" class.
The final dataset is composed of 4,090,432 sentences and is split into 80% train, 10% development and 10% test sets. The classifier consists in a linear classifier (here logistic regression) fed with the average of the n-gram embeddings. n-grams are useful in this case as they enables the model to capture specific sequences of NArabizi characters such as lah, llah, 3a, 9a, etc. We choose to embed 2-to 5-grams. These parameters lead to precision and recall scores of 97% on the NArabizi test set. After an intensive post-processing step (cf. Appendix A. 2), this process results in a dataset of 13,667 sentences extracted from half the Common-Crawl corpus. 7 To evaluate the quality of the resulting data set, we randomly picked 3 times 100 sentences, and genuine NArabizi sentences were manually identified, which allowed us to assess the accuracy of our corpus as reaching 97%.
Arabic letters are either consonant sounds or long vowels, each one may have several different transliterations in NArabizi, depending on the writer's age, accent, education and first learned Western language. For example, the letter ‫ث‬ 10 can be transliterated as "t" or "th". A probability must be assigned for each possibility, and to make it as close as possible to what is produced by NArabizi speakers, a small parallel corpus of PADT sentences and their transliteration 9 Other diacritization systems have better performances  (Belinkov and Glass, 2015)  but are either not maintained with the proper python packages, or come with a fee. 10 Theh, U+062B. by ten NArabizi speakers was assembled, and then each letter aligned with all its possible matches to get probability distributions.
Discussion How interleaved are French and NArabizi? As stated before, NArabizi takes its root in Classical Arabic and in multiple sources of integration of French, MSA and Berber, the Amazigh language. As the NArabizi treebank contains more than 36% of French words, it is of interest to use recent methods of visualization to see how interleaved it is with some of its source languages. To this end, we extract words embeddings using fastText  (Joulin et al. , 2016)  from a corpus made of the "translitterated" PADT described in Section 5, the French UD GSD and NArabizi original corpus  (Cotterell et al. , 2014). Two-dimensional representations of the resulting embeddings space for 300 selected words are shown in Figure  2  for embeddings of size 50 and 100. We notice that the overall shapes of both representations are very similar, apart from a non significant x-axis reversal.
On the first components, increasing the embedding size does not provide more information. We also see that French and transliterated Arabic words are clearly separated into two clusters of low standard deviation, while NArabizi words are very spread out. Some fall within the French cluster, they correspond to French words present in this Algerian dialect. Others are in the middle of the Arabic cluster, these are the purely Arabic words of the dialect. Between the two, there are Amazigh words (rak, mech), arabized French words (tomobile < French automobile), Arabic words whose Berber pronunciation has resulted in an unexpected NArabizi rendering (nta instead of expected enta 'you', mchit instead of expected machayt 'to go-2SING'). What is the Impact of Code-Switching in POS-tagging performance? Given the large degree of interleaving between French and NArabizi, it is interesting to assess the impact of the French vocabulary on the performance of a POS-tagger trained on French data only.
For these experiments, we use the StanfordNLP neural tagger  (Qi et al. , 2019) , which ranked 1st in POS tagging at the 2018 UD shared task, trained on the UD French ParTUT treebank, using French fastText vectors  (Mikolov et al. , 2018). In order to perform a meaningful evaluation, we split the NArabizi training set into 4 buckets of approximately 25% of it size in tokens, with a increasing proportion of identified NArabizi tokens. Results in Table  7  show a clear drop of performance between the sentences that contain more code-switching (59. 55% of UPOS accuracy) and those with none (16. 84%). This suggests that low-resource languages with a highlevel of code-switching such as NArabizi can benefit from NLP models trained on the secondary language. The level of performance to expect from these cross-language approaches is yet to be determined.
Related Work Research on Arabic dialects is quite extensive. Space is lacking to describe it exhaustively. In relation to our work regarding North-African dialect, we refer to the work of  (Samih, 2017)  who along his PhD covered an large range of topics regarding the dialect spoken specifically in Morocco and generally regarding language identification  (Samih et al. , 2016)  in code-switching scenario for various Arabic dialects  (Attia et al. , 2019). Unlike NArabizi dialects, the resource situation for Arabic dialects in canonical written form can hardly be qualified as scarce given the amount of resources produced by the Linguistic Data Consortium regarding these languages, see  (Diab et al. , 2013)  for details on those corpora. These data have been extensively covered in various NLP aspects by the former members of the Columbia Arabic NLP team, among which Mona Diab, Nizar Habash, and Owen Rambow, in their respective subsequent lines of works.
Conclusion We introduced the first treebank for an Arabic dialect spoken in North-Africa and written in romanized form, NArabizi. More over, being made of user-generated content, this treebank covers a large variety of language variation among native speakers and displays a high level of codeswitching. Annotated with 4 standard morphosyntactic layers, two of them following the Universal Dependency annotation scheme, and provided with translation to French as well as glosses and word language identification, we believe that this corpus will be useful for the community at large, both for linguistic purposes and as training data for resource-scarce NLP in a high-variability scenario. In addition to the annotated data, we provide around 1 million tokens (over 46k sentences) of unlabeled NArabizi content, resulting in the largest dataset available for this dialect.
The best scores were obtained with a model trained on a mix 30% of pseudo-Arabizi and 70% of French, which we call the MIX treebank, totaling 5,955 training sentences. We split this treebank into training, development and test sets, called MIX train/dev/test , following a 80/10/10 split. We used a very small manually annotated NArabizi development dataset of 200 NArabizi sentences, called Arabizi dev , to evaluate our parser. As shown in Table 6 (line "Mix"), despite good results on MIX's development and training sets, MIX dev and MIX test respectively (see Table.
Acknowledgments The work was partially funded by the  French Research Agency projects ParSiTi  ( ANR-16-CE33-0021 ),  SoSweet  ( ANR15-CE38-0011-01 ) and by the  French Ministry of Industry and Ministry of Foreign Affairs  via the  PHC Maimonide France-Israel cooperation programme , as well as by the Sagot's chair in the PRAIRIE institute funded by the  French national agency ANR  as part of the  "Investissements d'avenir" programme  under the reference  ANR-19-P3IA-0001.
Finally, we will discuss the main interactions between SOM, soil structure and soil biota concerning soil aggregation, fate of earthworm casts, structural effects of soil engineers and the important interactions between fine roots, mycorrhizal fungi and SOM. This review was conducted by including leading authors from the different disciplines. Web of Science Core Collection (www. webofknowledge. com), Google Scholar (scholar. google. com), ScienceDirect (www. sciencedirect. com) and ResearchGate (www. researchgate. net) were used to search for manuscripts covering soil models, soil functioning and SOM stabilization/formation but also each of the different soil biota as well as reviews on soil hydrology and soil aggregation.
Cloning of full length cDNA and sequence analysis The full length mRNA encoding the sghC1q was cloned from RT-PCR performed on cDNA from whole European sea bass larvae using primers designed on the EST sequence (forward primer: ATGGTCTACACGGTGATTCTC, reverse primer: CTACGACGGGAAGATGAGGA) (Figure  1 ). After cloning, the sequencing was performed by GENEWIZ service (South Plainfield, USA). The protein sequence deduced from cDNA was obtained using the Expasy translate tool (https://web. expasy. org/translate/). cDNA and deduced protein sequence are available in Genbank nucleotide databasis (MT176417). The amino acid sequences of domains were analysed on SMART (Simple Modular Architecture Research Tool) web resource (http://smart. embl-heidelberg. de/) [57]. A microsynteny analysis consisting in identifying the orthologs of sghC1q gene in other species was performed by determining the flanking genes from European sea bass genome available in Genbank (https://www.