dataesr/scientific-paragraphs-categorization huggingface dataset:
Dinehart and Burau (2005)  proposed a two steps method to isolate secondary currents: first, a bend-crossing plane of velocity vectors from ADCP data are derived, and second, elements of the backscatter intensity planes are used to guide an interactive alignment of the averaged velocity grids previously obtained. They found the bendcrossing plane through a section-straightening procedure, where the velocity ensembles are spatially translated to a straight line defined by a mean crossing line fitted along multiple transects. The procedure adopted here for post-processing the field data involve two steps: the use of proprietary software in the first place and then in-house routines to get the transverse velocity field. In other words, filtered 3D flow data were first exported into spreadsheet files written in ASCII format with the proprietary program ViewADP  (Sontek 2004)   cross-sections with the DEM, in turn generated with the routines described in  Vionnet (2010).
Nevertheless, if the 3D structure of the loaded velocity field is correctly handled by the inhouse code, these new flow discharge estimates should not be far from the values reported by the proprietary software. It suffices to note that whereas the river discharge measured on 4-5 May 2010 (Table  2 ), estimated with the software WinRiver II (TRDI 2007) was 1085 m 3 /s on average with a fractional standard deviation of about 5. 8%, the computation with read_aDcp yielded a mean value of 1100 m 3 /s with a standard deviation of 6. 1%. Sixteen values of discharges measured along cross-sections XS1, XS3, XS4, and XS5 were selected (see Figure  3 (b) for reference locations). The values measured on crosssection XS2 were ruled out because this transect is affected by recirculating flow nearby the left riverbank.
Both results, the estimated with the proprietary software and the approximated with the in-house code have similar frequency distributions, with a median of 1100 m 3 /s (see appendix). It is seen that the river discharge values obtained with the in-house code bears favourable technical comparison with the values produced by the proprietary ADCP software. The comparison can be favourable only if the 3D velocity field is correctly uploaded by the code. In brief, the in-house code handles the field data in a manner consistent with the proprietary software and, therefore, it can be deemed appropriated for further processing, for example, the isolation of secondary currents.
It seems then that the curved flow along the right branch should be prone to the formation of secondary flow. This distinctive flow feature should be embedded in both the numerical results and the field data, where the former depends on the ADCP proprietary software, and to a lesser extent, on the used in-house code. The proprietary RiverSurveyor software  (Sontek 2004 ) handles depth-averaged data across the measured transect without further processing. Thus, after conversion of geographical positions to TM (Gauss-Krüger) coordinates, the information can be readily exported onto georeferenced satellite images of the study site for further analysis. Few transects with the 2DH velocity field measured during the 2004 and 2006 field works (see Table  2 ) are depicted in Figures  5(a ) and 6(a).
The 3D flow structure The right branch of the diffluence exhibits a strong asymmetry of the bed topography. The flow along this branch Downloaded by [Universidad Nacional del Litoral], [Mariana Morell] at 09:31 17 December 2014 Note: Q l and Q r stand for Q left and Q right , respectively. Figure  7 (a) depicts the 3D velocity field along the edges of the computational domain nearby the outflow boundary representing the inlet to the access channel. The numerical solution captures the area of stagnant water behind the island, the vertical axis vortex at the curve apex of the inner bank, and the detached flow downstream of it. The contour map embedded in Figure  7 (a) reflects the module of the 3D velocity field, in m/s. The size and location of the vortex shown in Figure  7 (a) are in fairly well concordance with the drawn float paths and with the ADCP measurements (Figures  4(a) , 5(a), and 6(a), respectively).
Conclusions Float paths produced on the study site, and numerical results obtained with Telemac, confirm the presence of a central core of convectively accelerated flow bounded by curved streamlines along both branches of the diffluence. The flow along the left branch is smooth, weakly curved, whereas the flow along the right branch exhibit regions of flow stagnation, separation and reattachment. Field data captured with the Sontek ADCP, and processed with an in-house code show that the right branch contains a well-defined central cell of secondary currents. Although absent here, TRDI ADCP data show the same secondary motion. The helicoidal flow formed along the right branch keeps a scour hole at the access channel mouth. There, the channel sharp turn provokes the separation of the incoming boundary layer and, therefore, the formation of a vertical axis vortex at the curve apex.
All these features are well captured by the 2D and 3D numerical solutions, including the observed free-surface deflection along the diffluence head whose "piecewise" shape is in tune with the expected flow behaviour. River discharge data processed with the in-house software is consistent with the proprietary ADCP software, a required validation step before inferring the presence of secondary currents. Unfortunately, a unified algorithm to isolate cross-flow from a skewed discharge produced with either numerical or field data are still lacking. In particular, no unbiased algorithm is yet available to project the 3D computed velocity field onto selected cross-sections. The mentioned limitation hampers the users to judge the capability of Telemac-3d to capture secondary currents along river bends.
The group is developing the user interface to extract the numerical data from Telemac-3d to project them onto any cross-section with the same algorithm embedded in the software developed to treat field data. According to the produced field measurements, the hydrodynamics of the studied bifurcation has remained essentially unaltered during the observation period. Initially, it was possible to spot zones of stagnant water around the sandbar driving the flow along circular streamlines, a role later occupied by the emerged mid-channel island. However, and despite the recent changes seen during the study period, the reconstructed morphology of the site along a century-long record establishes the stability of the riverbanks surrounding the bifurcation node at the access channel mouth. The node initially conformed to a Y-shaped bifurcation, later evolved to a T-shaped, and recently underwent a transitional stage towards a Y-shaped node with a bifurcation angle γ close to the stability range reported by  Federici and Paola (2003).
Moreover, the use of Equation (A3) in Equation (A2) implicitly assumes that the upper layer of unmeasured water (due to blanking distance and transducer draft) and the bottom layer moves with the mean velocity U = (U, V) ( It must be clear that the procedures implemented in the inhouse code to approximate the surface-nearest and bed-nearest velocity values within an ADCP profile, and the discharge values near the river banks as well, differ greatly from the one-sixth power-law and the ratio interpolation methods embedded in the.
Acknowledgements Part of this work was supported by the  CONICET  under Grant  11220100100384. Financial support from  EDF  and  UNL  is also gratefully acknowledged. Special thanks are given to  Jean-Michel Hervouet  for his support, to  Carlos Ramonell  who kindly provided the historical charts used for reconstructing the morphological evolution of the study site, and to two anonymous reviewers for their useful criticisms.
Microsatellite typing and determination of population structure S. cerevisiae microsatellite loci were amplified as described previously  [22]. Genomic DNA was isolated by phenol/chloroform extraction, after cell grinding with glass beads, and isopropanol precipitation as described previously  [23]. Allelic variation at 12 microsatellite loci was examined in 142 strains as described previously  [22]. The chord distance Dc  [24]  matrix was calculated for each couple of strains with a laboratory-made program. The tree was obtained from the distance matrices with the Neighbor program of the Phylip 3. 67 package, and drawn with MEGA5. 22  [25]. The tree was rooted by the midpoint method. To assess the assignment of flor strains to a particular origin, InStruct  [26]  was used to evaluate the number of populations in the set of strains and a graphical display was obtained with R software version 2.
Analysis of FLO11 polymorphisms and cell hydrophobicity The polymorphism of the length of Flo11p was measured from the amplification of FLO11 alleles with a pair of primers located 2 53 bp in the 59 of FLO11 (Flo11IntFw CTCCCTCATCA-TGTTGTGGTTC), and +3126 bp in the terminal part of FLO11 (Flo11IntRv AACGACGGTGGTTGAGACAA). ExTaq DNA polymerase (TaKaRa) was used to amplify this long DNA fragment. The PCR temperature program was 95uC for 5 min, followed by 30 cycles with an initial denaturation step of 95uC for 30 sec, annealing at 61uC for 30 sec, and elongation at 72uC for 6 min. The presence of the 111 bp deletion in ICR1 ncRNA was examined by the amplification of this region with the primer pair Flo11promFw CAGCCCCAGAGTATGTTCTCACAG and Flo11promRv AATCACCTTCTAAACGCTCGGA. This PCR was performed with regular MBI Fermentas Taq DNA polymerase.
The PCR temperature program was 95uC for 5 min, followed by 30 cycles with a first denaturation step 95uC for 30 sec, annealing at 56uC for 45 sec, and elongation at 72uC for 1 min. The presence of the deletion was detected from the band size of the amplified fragment in gel electrophoresis. For 5 strains (CAV21, LRJura, CECT11758, TR05CUB, T8CUB), the amplified fragment was sequenced with the same primers. These five sequences are available in GenBank under the accession number (HG965200-HG965204). Cell hydrophobicity was evaluated following the procedure of Ishigami et al. [17] , which relies on the measure of the partition of yeast cells between a buffer solution and an organic solvent. Yeasts strains were cultivated for 48 h with shaking in Fornachon's media containing 4% ethanol, and then harvested, washed three times with water and suspended in 4 ml of McIlvaine buffer, pH 3.
CGH on array Genomic DNA was labeled and hybridized against GeneChip Yeast Genome 2. 0 Array from Affymetrix (Santa Clara, CA), which covers all S. cerevisiae S288C genes  [28]. Labeled fragments were prepared from 200 to 500 ng of genomic DNA with the BioPrime DNA Labeling System (Invitrogen). The hybridization and detection steps were performed at the IGBMC Microarray and Sequencing Platform (Illkirch, France). Two arrays were used for each strain. Intensity data of perfect match probes were obtained with apt1. 12. 0 Affymetrix software, after RMA background subtraction and quantile normalization  [29]. After filtering for probes with insufficient signal, the final number of probes used for the analysis was 38863. Signal intensities were scaled across arrays and log ratios were calculated using S288C as a reference.
The log ratios were averaged by groups of three consecutive probes, to reduce probe to probe variation and facilitate analysis with DNAcopy. The best results were obtained after RMA background subtraction and quantile normalization of array data. Array Data were analyzed with the package DNAcopy  [30]  and R software version 2. 15. 1  [27]. A custom script was used to associate the mean log ratio calculated per chromosome segment with each ORF it contained. Gene clustering was performed with Cluster 3. 0  [31] , using a filter of 0. 5 minimum difference in log ratio between all strains, and limiting missing data to six strains. Uncentered correlation and the centroid clustering were chosen as parameters, and dendrograms were drawn with TreeView. Gene ontology enrichment analysis was performed with Gene Codis 3.
Interestingly, subclusters formed inside the main group of flor strains according to geographical origin: three clusters of Jura strains, two clusters of sherry wine strains (Jerez 1 and 2), and one main cluster of Sardinian strains. In addition, Jura strains were grouped according to the cellar from which they were isolated. One Jura flor strain, MAA52, did not cluster with the other flor strains, and was thus considered as a wine strain. To confirm the global structure observed from microsatellite typing, we used the software InStruct to detect population structure and assign the various flor strains to a particular origin. InStruct  [26]  is an alternative program to Structure  [38]  that takes into account partial self-fertilization and inbreeding; therefore, it is well suited for such an analysis because a high rate of inbreeding has been inferred from Fis values for yeast populations  [22, 39, 40].
In addition, this strain has a microsatellite profile indicating that the S. cerevisiae moiety of its genome belongs to the flor yeast group. A first analysis carried out with different normalization methods dedicated to Affymetrix arrays (RMA, GCRMA, MAS5) indicated that 1606, 834, and 218 probe sets, respectively varied significantly between strains after correction for multiple tests (adj. p. value , 0. 01). Although we were able to detect the main aneuploidies of Eg8, the high gene to gene variation in hybridization necessitated the use of a sliding window smoothing over three genes to reduce noise  [33]. This explains why we used directly the signal of PM probes. We then chose to evaluate variation in copy number by detecting discontinuities of log ratios along the chromosomes with the Rpackage DNAcopy  [30].
Interestingly, a flor yeast population was recently characterized in Georgian aged wines produced by the ''Kakhetian'' method  [49]. Nonetheless, it is still possible that all flor strains have a Mesopotamian origin because wine making is an ancient process in Georgia and this country is close to origin of vine domestication. However, the comparison of a larger number of strains is necessary. The position   [18]. The first sequences were obtained from Genbank and correspond to Spanish and an Italian flor strains  [18, 48]  that carry this deletion. The Spanish strain CECT11758, the Hungarian strain T8CUB and the Jura strain LRJura share the same deletion. The alleles of the Jura strain CAV21 and the Hungarian strain TR05CUB are similar to that of S288C. doi:10. 1371/journal. pone. 0108089.
Acknowledgments The authors are extremely grateful to  Prof M. Budroni ,  University of Sassari, Italy ,  Prof A. Maraz ,  Corvinus University of Budapest, Hungary ,  Dr. Maria Esther Rodrı ´guez Jimenez ,  Universidad de Cadiz, Spain ,  Prof J. Jimenez   Universidad Pablo de Olavide, Sevilla ,  Prof JC Garcia Mauricio ,.
Relative measures are also useful to compare the efficiency of prediction algorithm across studies, as in most cases the popularity values have widely different ranges (e. g. , the number of views on YouTube is several orders of magnitude greater than the number of comments on a news web site). Special attention should be paid when using these error measures for zero-inflated variables as the relative error is undefined when the actual value is zero. Another way of expressing the prediction error is through the Relative Squared Error (RSE -Equation  6 ), Root Relative Squared Error (RRSE -Equation  7 ), and Relative Absolute Error (RAE -Equation  8 ). The error in this case is expressed relative to the performance of a simple predictor, the average of the actual values (computed on the training data set).
rely on this approach to predict the popularity of YouTube videos  [33]. Using a sampling rate of one day the authors use a multivariate linear regression expressed as N c (t i , t r ) = (t i , t r )X c (t i )• ( 14 ) The parameters of the model, (t i , t r ) = [θ 1 , θ 2 ,. , θ i ] are computed to minimize MRSE under the new definition of estimated popularity. Using a collection of YouTube videos this model shows a significant improvement compared to the constant scaling model. For instance, predicting the popularity of a video one-month after its publication using data from the first week shows an average improvement of 14% over the constant scaling model. The main drawback of this algorithm, as mentioned by the authors, is that in order for the prediction methods to be effective, additional exploration is needed to decide on the optimal history length and the sampling rate.
The representative members for each interval are computed using Affinity Propagation clustering algorithm  [80]. To calculate the similarity between items, the authors derive two features from X c (t i ): one that compares if two items receive the same proportion of users' attention and another one that measures if the two items experience a similar popularity growth. Once the clusters of popularity are identified, they are grouped into a probabilistic framework used to describe the evolution of content popularity between clusters over time. Thus, by knowing to which cluster an individual item is most likely to belong at time t i , one can predict its popularity at a future moment of time t r. The performance of the model was tested on three data sets (YouTube, Vimeo, and Digg) and shows a significant improvement over the log-linear model.
Cross domain The second major category of methods is used to predict web content popularity using information from multiple web domains: extract data from one domain (e. g. , social media) and transform it into knowledge to predict web content popularity in another domain (e. g. , the site where content was published). Currently, only methods that predict content popularity after publication based on the aggregate behavior have been proposed. Oghina et al. use data from Twitter and YouTube to predict movie ratings on IMDb  [36]. By training a linear regression model on several textual features extracted from Twitter and various statistics from YouTube (likes, dislikes, and comments) the authors show that they can accurately predict movie ratings on IMDb. The authors indicate that the best performance is obtained by combining the ratio of likes over dislikes from YouTube activity with the subjective terms (positive and negative unigrams about the movies) extracted from Twitter.
The popularity of a video on Twitter, expressed by the popularity of its topic, is then compared to its popularity on YouTube (represented by number of views) and, if the difference is significant, the video is considered susceptible to receive a sudden burst of attention. Using data from YouTube and Twitter, and by training a support vector machine classifier, the algorithm shows that it can predict with 70% accuracy which videos will experience a significant increase in popularity on a daily basis. This strategy shows an improvement of almost 60% compared to a model that uses only the information available on YouTube. Castillo et al. propose a prediction method that collects information about the early attention that news articles receive on social networks to predict the total number of http://www.
Social media signals. As we saw in Section 7. 2, social media conveys valuable information about web content popularity. Castillo et al. show that the attention that news articles generate across social networks (number of Facebook shares, number of tweets and retweets, the language of the Twitter messages) is effective in predicting the popularity of articles on a news site  [14]. Oghina et al. successfully use information from Twitter and YouTube to predict movie ratings on IMDb. Another example of the predictive power of social media has been reported by  Roy et al. who  show that the popularity of a topic on Twitter provides a good indication that a YouTube video will experience a sudden burst in popularity  [35]. Social sharing viewing behavior. Yahoo! Zync is an application that allows users to share and jointly manipulate video content in real time.
com/content/5/1/8 mechanism for information spreading and by learning the social structures created by users one can understand the patterns of information diffusion  [7, 104]  which further gives one the ability to predict when a user will be interested in a certain content. This type of approach has been proposed by  Malandrino et al. that  show that, by predicting information cascades, mobile users' requests can be treated in advance which can lead to a reduction of up to 50% of the cellular data traffic during periods of high data traffic loads  [105]. Major Web search engines such as Google, Yahoo!, and Bing are always looking for new ranking factors to improve the relevancy of their search results. Over the years, search algorithms have become extremely sophisticated including hundreds of ranking factors based on the content of a web page or its importance in the Web graph.
Principle « Morphing » is the word used to say: « image transformation ». If at the beginning, the morphing was just a simple interpolation of colors, with the source's image colors which progressively fade away to let (also progressively) appear the destination's image colors, we shall see that nowadays the morphing is slightly more complicated. In fact, morphing is now based on a double interpolation, both on shapes and on colors, between two images. There are different ways to do this, and we have chosen to look into only the most wide-spread methods. To illustrate our discussion, we have used a free software called: "Sqirlz Morph"  [7].
How to use the morphing tool First of all, you need two images of computed results which mustn't be totally different: one need consider images with some similarities i. e. two faces, two invisibility cloaks and so forth. The overall size and shape of images matters. For instance, if one would like to get morphing results between invisibility cloaks and human faces as is done in Fig. 2  and 4 , the first thing to check is to equalize the size of source and destination images. Then, you need of course a morphing software in order to process the morphing transformation. We opted for the freeware Sqirlz Morph  [7]. With this software, one just has to place the control points in one image, making sure that each control point in the first image is correctly placed in the final image. Note that one can move each control point in one image independently from its corresponding control point in the other image.
Because the data collected by each actor of the domain have become substantial, it is important to design and implement a large, common and consistent database to aggregate these complementary data. In order to meet this issue, the French ANR 1 Fresqueau project 2 has begun in 2011. This project aims at collecting and unifying databases that are linked to the quality of water bodies. They include biological, physico-chemical and also hydro-morphological data. The result is a consistent spatio-temporal database that brings together information related to north-east and south-east French watersheds. It concerns 11,329 sampling sites spread over 161,100 km 2 that represent 29. 45% of metropolitan France. These watersheds are grouped into two major hydrographic areas which are Rhin-Meuse (north-east), denoted as RM and Rhône Méditerranée Corse (south-east), denoted as RMC.