dataesr/scientific-paragraphs-categorization huggingface dataset:
Step 3: Delimitation of the particle-containing area Step 3-1: Geometric conversion into the PIV mesh grid As the final mask will be applied to a PIV vector field available on a regular mesh of 66 × 49 points, we propose to convert the previous investigations into physical coordinates corresponding to the regular PIV grid mesh (see Table  1 ). As the PIV mesh discretization corresponds to a 24 × 24-pixel window (databases 1 and 2), we propose to analyze all consecutive 24 × 24-pixel windows. If one contains some white areas, the PIV mesh grid point is fixed to the white color; otherwise, it is black (see Fig. 9 ). To overcome the impact of the window's size on data treatment, and knowing that the PIV interrogation window is set to 32 × 32 pixel 2 (see Table  1 ), we performed similar treatments for all consecutive windows of 32 × 32 pixels 2.
11 ) where a black mesh grid point is set to a zero value and a white grid point is put to a value of 1. An illustration of this application is presented in Fig. 12 , in which the final interface is plotted on the PIV raw images. The whole procedure is applied to the whole PIV database available (see Tables  1  and 2 ). The algorithm is applied separately to each PIV image independently of the mask's information related to other PIV images. Recall that for PIV vector field determination, a pair of raw images is required. This algorithm has been tested successively for the two images of the latter and a similar water-air interface has been retrieved. For each considered database, the free interface detection algorithm is only applied to the first image of the pair, and the resulted interface is imposed to the computed PIV vector field.
An illustration of the application of the free surface detection algorithm is presented in Fig. 13  during a phase where a bubble cloud is generated (database '2c'). Note that the free surface is determined in the PIV mesh grid with a pixel discretization of 24, in each direction (see Table  1 ), leading to a local error of ±24 pixels in each direction. Thus, when plotting this surface on the PIV raw images discretized in pixels, possible but very small bias can be locally observed.
Free surface properties When looking at PIV database '1' (independently of the flow conditions), we observe that the wave surface is sometimes above the region of interest. In this sense, it is not possible to properly analyze the water-air interface signal, as it is not continuous in time. The results presented in the following concern exclusively those deduced from database '2', for which the water-air interface can always be detected. First, Fig. 14  represents the time evolution of the fluctuating surface signal extracted at a fixed upstream location (x = 76 mm). It is observed that the amplitude of the free surface is of ±50 mm, which is in a great agreement with previous free surface measurements  [2]  performed in similar inlet regular wave conditions with an imposed wave amplitude of 33 mm.
Furthermore, to ensure that the periodicity of the incoming wave is recovered, a Fourier analysis of the free surface signal is performed. Fig. 15  displays the spectral representation of the free surface signals obtained in each database for which the imposed frequency ( f 0 = 0. 5 Hz) at the inlet is always recovered. While for databases '2a' and '2b' (without hull motion), some low-frequency peaks are present, we observe that for the other databases ('2c' and '2d', with forced hull oscillations), a peak appears at f = 2 f 0 = 1. 7 Hz. This can be directly related to the interaction of hull motions, also generated at the frequency f 0 , and of the incoming wave. Moreover, to examine the effect of hull oscillations on the bow wave, the Root Mean Square of the free surface signals is computed.
It is then observed that the level of the RMS remains globally the same when far away from the hull (for databases '2a' and '2c' in the symmetry plane). However, the levels of the RMS when close to the hull increase notably by about 9% for database '2a' (without hull motions) and more than 15% for database '2c'. This result shows the interaction between the hull motions and the free surface. This leads to increase the free surface variabilities and then the frequency of occurrence of bubble clouds.
An illustration is given in Fig. 16  for database '2c'. This figure displays two instantaneous w components computed from the two raw PIV images labelled (i) and (f) in Fig. 13. They are represented before and after the application of the mask. Moreover, the corresponding instantaneous streamlines are also given in this figure. These two figures are retained because they are representative of the mask application in the presence or the absence of bubbles in the raw PIV images (i) and (f) respectively. We then observe the downward flow motion (graphs (f)) related to the downward motion of the hull and also to the wave beginning to get off. Conversely, the vertical velocity component associated with the other instant (graphs (i))  exhibit an upward motion of the flow just behind the hull. Note that these instantaneous pictures also emphasize (especially the (f) image containing a lot of bubbles) that it would be necessary to take into account the presence of bubbles in the PIV algorithm.
Indeed, the presence of bubbles may modify locally the instantaneous flow field. Such investigations are ongoing. Furthermore, Fig. 17  presents the time evolution (over more than ten periods of the hull oscillations) of the instantaneous vertical velocity components (w) extracted at a similar point located just above the bulb of the hull, in databases '2a' and '2c'. Note that when dealing with database '2a', this point remains at a fixed position. As for database '2c', in order to consider a similar location, a moving point is extracted at each instant. An illustration of the location is provided in Fig. 16  with a black circle. The vertical velocity component signal is superimposed onto the fluctuating wavy surface deduced from the masking operation and extracted at the similar streamwise position.
It is first observed that the amplitude of the wavy surface for database '2c' is globally higher than the one of database '2a'. This is due to the hull motion, which leads to increase the wavy amplitude. Besides, the representations of the instantaneous w component underline the rule of the hull motion onto the hydrodynamics. Indeed, the present analyses seem to demonstrate that the hull motion increases the magnitude of the velocity w. More analyses have to be performed to confirm these preliminary analyses. For instance, the phase average procedure based on a Proper Orthogonal Decomposition and previously implemented  [5, 26]  would allow a better investigation of the influence of the ship's motions on the intensity of the vertical velocity component, which is directly related to bow wave generation.
Acknowledgements D. Dussol  would like to thank  IFREMER  for the financial support of his internship. The authors would like to thank the  DGA (French Government Agency for Defence)  and the  Nord-Pas-de-Calais Regional Council  for the financial support of the Ph. D. theses of both  S. Delacroix  and of B. Mallat. The authors are also grateful to  T. Bacchetti ,  J. -V. Facq  and  B. Gaurier  for their involvement in the PIV measurements.
Methods Zebrafish strains and maintenance. A list of zebrafish lines used in this work is provided in the Supplementary Table  7. To prevent the pigmentation in embryos older than 24 hpf, 0. 003% in Supplementary Method 2 and imaged as z-stacks of whole embryos using a Z. 1 light-sheet microscope (Zeiss) controlled by ZEN software (Zeiss) followed by image processing using Imaris software (Bitplane, Switzerland; Fig. 3 ; Supplementary Fig. 7 ). A list of the probes and the primers used to amplify them is provided in Supplementary Tables  8 9 10. Transplantation experiments. Donor and host embryos were engineered to be depleted for or to express specific proteins as indicated in the figures. Cells from a 4 hpf donor embryo were transplanted into a 4 hpf host using an Eppendorf CellTram Vario, and cell behaviour was immediately followed by time-lapse movies using an Epifluorescence microscope (Axioplan2, Zeiss).
The sgRNAs were synthesized using MEGAscript Sp6 transcription kit (Ambion). The experimental and control sgRNAs sets were mixed with Cas9 protein and injected into the cell of one-cell stage embryos. At 28 hpf live embryos were imaged (Supplementary Figs  4a  and 5a ) or fixed for analysis using the RNAscope procedure before imaging (Fig. 3a ). Z-stacks of embryos were imaged on a Z. 1 light-sheet microscope (Zeiss), analysed and processed employing median filter, background subtraction and the 'surface' module of the Imaris software to further reduce background originating in the notochord (Bitplane, Switzerland). For molecular detection of the mutations, one lpp sgRNA was coinjected with Cas9 protein, with embryos injected with Cas9 protein and a single tyr sgRNA (G416 in Supplementary Table  3 ) serving as controls.
The reads were filtered and trimmed for low-quality reads and then mapped to the transcriptome using SeqMap (Bowtie for the last two samples) and generated expression value using rSeq. Differential expression was detected using DEseq package of Bioconductor. None of the transcripts were excluded. The expression levels of LPA and S1P receptors, sox32 RNA and cell adhesion molecules are presented in Supplementary Tables 2,5 and 6. Histological sectioning. Following the standard chromogenic in situ hybridization protocols (Fig. 2b ) or fixation of embryos expressing fluorescent proteins (Fig. 5b ), the samples were embedded in 4% low-melting agarose and sectioned into 100 mm slices using a vibratome (Leica, Germany). Overnight incubation in Hoechst 33342 (Life Technologies) was performed at 4 °C (1:10,000; Fig.
95 NA) and simultaneous excitation at 980 nm (Mai Tai Spectra Physics) and 1,030 nm (t-pulse 20 Amplitude Systems)  69. The spatial resolution was 1. 37 mm 3 and the temporal resolution 2 min. Supplementary Movie 2 was generated using the Amira software (Visage Imaging, CA). Germ cell tracking was performed using ImageJ (NIH). Epifluorescence movies were captured on an Axioplan2 epifluorescence microscope (Zeiss). Image analysis was performed using ImageJ (NIH) and Metamorph (Visitron Systems) software packages. Light-sheet recordings of live zebrafish embryos (Supplementary Movies 1 and 5-8) were performed using SiMView microscopy  27. The embryos were embedded at 24 hpf in 1% low-melting temperature agarose (Sigma-Aldrich, A4018) and recorded using either a 16 Â objective and 1. 5-3 min time intervals, or a 40 Â objective and 30 s time intervals followed by analysis using Imaris software (Bitplane, Switzerland; Fig.
6d-f ). When necessary, time-dependent intensity levels and the embryo position were corrected computationally using the Fiji distribution of ImageJ (plugins 'Bleach Correction' and 'StackReg', respectively) and custom software written in Matlab (The MathWorks)  70. Statistical analysis. All statistical analysis was performed using PRISM (Graphpad Software, Inc. ) and Microscoft Excel. Cluster formation analysis and simulations. To determine the distribution of PGCs at the gonad region, epifluorescence z-stack images were captured at 24, 24. 5 and 25 hpf (Fig. 7c ). Following z-projection, the positions of PGCs, the outline of the embryos and the gut were manually marked and measured using ImageJ and normalized using Microsoft Excel. For PGC behaviour analysis at the gonad region SiMView z-projected time-lapse movies were analysed (Fig.
Acknowledgements This work was funded by the  European Research Council (ERC) ,  Human Frontier Science Program (HFSP) , the  Deutsche Forschungsgemeinschaft (DFG) , Cells in Motion cluster of excellence (EXC 1003-CIM), the  Max Planck Society  and the  University of Muenster, Germany. We thank  Nina Knubel  for artwork, Michal Reichman-Fried for comments on the manuscript,  Ines Sandbote ,  Esther-Maria Messerschmidt ,  Ursula Jordan  and  Anne Albers  for technical help. N. S. G. and N. R. acknowledge very useful support from  Jens Elgeti  in setting up the simulations used in this project.
8 to 1. 5 mm on bloodenriched Colombia agar (bioMérieux) after 72 hours' incubation. Individual cells exhibited a diameter of 0. 7 to 1. 2 μm and a length of 1. 5 to 2. 5 μm measured by electron microscopy using a DM1000 photonic microscope (Leica, Wetzlar, Germany). Our systematic matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (MALDI-TOF MS) screening using a MicroFlex spectrometer (Bruker Daltonics, Bremen, Germany)  [2]  was not be able to identify this strain. The complete 16S rRNA gene was sequenced using a 3130-XL sequencer (Applied Biosciences, Saint Aubin, France) using the universal primers FD1 and RP2 (Eurogentec, Angers, France) as previously described  [3]. The sequence of strain Marseille-P2831 (GenBank accession number LT576394) showed a similarity of 90% with Alloprevotella rava strain F0323 T (GenBank accession number GU470887), the phylogenetically closest species with standing in nomenclature (Fig.
Introduction In recent years, work on the Semantic Web has led to the publication of large scale datasets in the socalled Linked Data framework such as for instance DBPedia or Yago. However, as shown in  (Rector et al. , 2004) , the basic standards (e. g. , RDF, OWL) established by the Semantic Web community for representing data and ontologies are difficult for human beings to use and understand. With the development of the semantic web and the rapid increase of Linked Data, there is consequently a growing need in the semantic web community for technologies that give humans easy access to the machine-oriented Web of data. Because it maps data to text, Natural Language Generation (NLG) provides a natural means for presenting this data in an organized, coherent and accessible way. It can be used to display the content of linked data or of knowledge bases to lay users; to generate explanations, descriptions and summaries from DBPedia or from knowledge bases; to guide the user in formulating knowledge base queries; and to provide ways for cultural heritage institutions such as museums and libraries to present information about their holdings in multiple textual forms.
In this paper, we focus on an important subtask of generation from RDF data namely lexicalisation of RDF properties. Given a property, our goal is to map this property to a set of possible lexicalisations. For instance, given the property HASWONPRIZE, our goal is to automatically infer lexicalisations such as was honored with and received. Our approach is based on learning lowdimensional vector embeddings of words and of KB triples so that representations of triples and their corresponding lexicalisations end up being similar in the embedding space. Using these embeddings, we can then assess the similarity between a property and a set of candidate lexicalisations by simply applying the dot product to their vector embeddings. One difficulty when lexicalising RDF properties is that, while in some cases, there is a direct and simple relation between the name of a property and its verbalisation (e.
Embedding-based Approaches The model we propose is inspired by  (Bordes et al. , 2014). In  (Bordes et al. , 2014) , low dimensional embedding of words and KB symbols are learned so that representations of questions and their corresponding answers end up being similar in the embedding space. The embeddings are learned using automatically generated questions from KB triples and a dataset of questions marked as paraphrases (WikiAnswers,  (Fader et al. , 2011) ). We adapt this model to the lexicalisation task by generating noisy lexicalisations of KB triples using a simple generation approach and by exploiting different paraphrase resources (c. f. Section3). Our approach further differs from  (Bordes et al. , 2014)  in that we combine this embedding based framework with a pre-selection of candidate lexicalisations which reflects knowledge about the property extension and the property name.
Learning Words and KB symbols Embeddings Similar to the work of  (Bordes et al. , 2014) , we use distant supervision and multitask training to learn embeddings of words and KB symbols. Training Set Generation We train on two datasets, one aligning KB triples with automatically generated verbalisations of these triples and the other, aligning paraphrases. The first dataset (T ) is used to learn a similarity function between KB symbols and words, the second (P) to account for the many ways in which a given property may be verbalised. Triples and Sentences (T ) We build a training corpus of KB triples and Natural Language sentences by combining the pattern based lexicalisation approach of (Trevisan, 2010) (c. f. Section 2) with a simple grammar based generation step. We apply this approach to map KB property names to syntactic constructions and then use a simple grammar to generate sentences from KB triples.
For instance, the triple in (1a) will yield the sentences in (1b-g): (1) a. On average, each property is associated with 5. 9 sentences. Given a training pair (t, s) such that t = (s k , p k , o k ), we generate negative examples by corrupting the triple i. e. , by producing pairs of the form (t ′ , s) such that t ′ = (s k , p ′ k , o k ) and (s k , o k ) / ∈ p ′ k. Paraphrases (P). To learn embeddings and a similarity function that takes into account the various ways in which a property can be lexicalised, we supplement our training data with pairs of paraphrases contained in the PPDB paraphrase database, in the WikiAnswers dataset and in DBPedia (DBPP). Positive examples (p i , p j ) are taken from these datasets and negative examples are produced by creating corrupted pairs (p i , p l ) such that p i is not in the paraphrase dataset of p l and vice versa.
The PPDB database was extracted from bilingual parallel corpora following  (Bannard and Callison-Burch, 2005) 's bilingual pivoting method 1. PPDB comes pre-packaged in 6 sizes: S to XXXL. The smaller packages contain only better-scoring, high-precision paraphrases, while the larger ones aim for high coverage. Additionally PPDB is broken down into lexical paraphrases (i. e. one word to one word), phrasal paraphrases (i. e. multi-word phrases), as well as syntactic paraphrases which contain non-terminals. We use PPDB version 2. 0 M size lexical and phrasal sets which contain overall 3525057 paraphrase pairs. We choose to use medium size sets to incorporate some variability while still favouring higher quality paraphrases. As for the type of paraphrases, we took only the lexical and phrasal ones given that our goal is geared to acquiring alternative lexicalisations in terms of wording rather than syntactic variation.
Wikianswers is a corpus of 18M questionparaphrase pairs collected by  (Fader et al. , 2013) , with 2. 4M distinct questions in the corpus. Because these pairs have been labelled collaboratively, the data is highly noisy (  (Fader et al. , 2013)  estimated that only 55% of the pairs were actual paraphrases). Finally, the BDPP dataset consists of (entity, class) pairs extracted from the DBPedia ontology. They provide a bridge between the entity names appearing in the DBPedia triples and the more generic common nouns which may be used in text. Using the resources and tools just described, we create a triple/sentence corpus T consisting of 317853 triple/sentence pairs obtained from 53384 KB triples of 149 relations. The paraphrase corpus P contains 3525057 (PPDB), 220998 (WikiAnswers) and 54489 (DBPP) paraphrase pairs.
Implementation The model is implemented in Python using the Keras(Chollet, 2015) library with Theano backend. We initialise the W matrix with pre-trained vectors which already provide a rich representation for words. We use the publicly available GloVe  (Pennington et al. , 2014 ) vectors 2  of length 100. These vectors were trained on 6 billions words from Wikipedia and the English Gigaword. We set the dimension d of the K and W matrices to 100. For K we use uniform initialisation. The size of the vocabulary for the W matrix, the n w dimension, is 130970 words. This is considering all words appearing in the T and P sets. The size of the K matrix, the n k dimension, is 43797 counting both KB entities and relations. The training for both similarity functions S t/s and S p is performed with Stochastic Gradient Descent.
The learning rate is set to 0. 1 and the number of epochs to 5. Training run approximately 15 hours 3. DBPedia 4 is a crowd-sourced knowledge based extracted from Wikipedia and available on the Web in RDF format. Available as Linked Dataon the web, the DBPedia knowledge base defines Linked Data URIs for millions of concepts. It has become a de facto central hub of the web of data and is heavily used by systems that employ structured data for applications such as web-based information retrieval or search engines. Like many other large knowledge bases (e. g. , Freebase or Yago) available on the web, DBPedia lacks lexical information stating how DBPedia properties should be lexicalised. We apply our lexicalisation model to DBPedia object properties. We construct candidate lexicalisation sets in the following way.
Evaluation and Results We compare the output of our lexicalisation method with the following resources and approaches. Table  1 : Set of DBPedia object properties used in the evaluation. DBlexipedia e : a lexicon 6  automatically inferred from Wikipedia using the method described in  (Walter et al. , 2013; Walter et al. , 2014a; Walter et al. , 2014b ) (c. f. section 2). Lexical entries are inferred using either the extension of the properties (by retrieving sentences containing entities that are related by a DBPedia property and generalising over the dependency paths that connect them. ) or synonyms of the words contained in the property name. PATTY: a lexicon automatically inferred from web data using relation extraction and clustering (c. f. (Nakashole et al. , 2012) ). QUELO: a lexicon automatically derived using the method described in  (Trevisan, 2010)  (c.
This is true in particular of properties such as ROUTEEND where the correct lexicalisation is difficult to guess from the property name. DBlexipedia e at k=10 scores lower (0. 29) than the corresponding version of our approach union(k=10), R:0. 38). Interestingly, for our approach, better recall values are consistently obtained using L-LEX suggesting that many of the verbalisations found in GOLD can be extracted from text that is unrelated to the extension of DB-Pedia properties. This is a nice feature as this permits avoiding the data sparsity issue which arises when a DBPedia property has either a restricted extension or a small set WKP p of candidate lexicalisations. Indeed, we found that out of a set of 149 DBPedia properties, the MATOLL corpus did not provide any sentences for 19 of them.
DBlexipedia e has a precision score (0. 11) comparable to the corresponding version of our approach (union(k=10), P:0. 09) and PATTY has a very low precision (P:0. 0015). A manual examination of the data shows that the relation extraction approach fails to find a sufficiently large number of distinct property lexicalisations. The lexicalisations found often contain many near repetitions (e. g. , "has graduated from, graduated from, graduates") but few distinct paraphrases (e. g. , "graduate from, study at"). To better assess, the precision of our system we therefore manually examined the results of our system and annotated all outputs lexicalisations which were correct but not in the gold. Based on this updated gold, precision for union. FreqQ3Limit7-25 is in fact, 0. 289. Example Output Table  4  shows some example output of our system (for union.