dataesr/scientific-paragraphs-categorization huggingface dataset:
Scaffolding of the Ectocarpus sp. chromosomes with instaGRAAL To test and validate instaGRAAL, we generated an improved assembly of the genome of the model brown alga Ectocarpus sp. A reference genome consisting of 1,561 scaffolds generated from Sanger sequence data is available  [20]. A Hi-C library was generated from a clonal culture of a haploid partheno-sporophyte carrying the male sex chromosome using a GC-neutral restriction enzyme (DpnII). The library was paired-end sequenced (2x75 bpthe first ten bases were used as a tag and to remove PCR duplicates) on a NextSeq apparatus (Illumina). Of the resulting 80,521,968 paired-end reads, 41,288,678 read pairs were aligned unambiguously along the reference genome using bowtie2 (quality scores below 30 were discarded), resulting in 2,554,639 links bridging 1,806,386 restriction fragments (Fig  1a ) (see Methods for details on the experimental and computational steps).
Comparisons with previous Ectocarpus sp. genome assemblies and validation of the instaGRAAL assembly We compared the polished instaGRAAL assembly (subsequently referred to as the polished assembly) with three earlier assemblies of the Ectocarpus sp. genome (Table1 and Table  S2 ): 1) the reference assembly generated using Sanger sequencing data  [20] , mentioned above, which was assumed to be highly-accurate but highly fragmented (1,561 scaffolds); 2) an assembly generated by combining genetic recombination data and the Sanger assembly  [19, 24]  (pseudochromosome assembly) and 3) an assembly generated by running the original GRAAL program on the reference genome (GRAAL assembly). We aligned the instaGRAAL, pseudochromosomal and GRAAL assemblies to the reference assembly to detect misassemblies and determine whether the genome annotations (362,919 features) were conserved.
Comparisons with existing methods To date, only a limited number of Hi-C based scaffolding programs are publicly available, and, as far as we can tell, no detailed comparison has been performed between the existing programs to assess their respective qualities and drawbacks. In an attempt to benchmark instaGRAAL, we ran SALSA2  [31]  and 3D-DNA on the same Ectocarpus sp. and Desmarestia herbacea reference genome and Hi-C reads. 3D-DNA is a scaffolder that was hallmarked with the assembly of Aedes aegypti and SALSA2 is a recent program with a promising approach that directly integrates Hi-C weights into the assembly graph. For Ectocarpus sp. , SALSA2 ran for nine iterations and yielded 1,042 scaffolds, with an N50 of 6,552,506 (L50 = 11). Its BUSCO-completeness was 77. 6%, a level identical to that obtained with instaGRAAL.
Benchmarking In order to test our tool against existing programs, we ran two scaffolders available online (SALSA2 and 3D-DNA) on our two genomic datasets. In all instances, instaGRAAL proved more successful at scaffolding both genomes. However, we have not extensively tested all the combinations of parameters of both programs, and acknowledge the difficulty in designing and implementing Hi-C scaffolding pipelines with extensive dependencies that compound the initial complexity of the task and add yet more configurable options to know in advance. Finding the correct combination of CUDA and Python dependencies to install instaGRAAL on a given machine can be challenging as well. Therefore, our benchmarking attempt should be rather seen as a way to stress the importance of implementing sensible default parameters that readily cover as many use cases as possible for the end user.
Integrating information from the Hi-C analysis with other types of data Aggregating data from multiple sources to construct a high-quality assembly remains a challenging problem with no systematic solution. As long read technologies become more affordable, there is an increasing demand to reconcile the scaffolding capabilities of Hi-C based methods with the ability of long reads to span regions that are difficult to assemble, such as repeated sequences. The most intuitive approach would be to perform Hi-C scaffolding on an assembly derived from high-coverage and corrected long reads, as was done for several previous assembly projects  [14, 36]. Alternative approaches also exist, InstaGRAAL shows that high quality metrics can still be attained without the help of long reads, but it can nevertheless integrate them when necessary or available.
Contact map generation Contact maps were generated from reads using the hicstuff pipeline for processing generic 3C data, available at https://github. com/koszullab/hicstuff. The backend uses the bowtie2 (version 2. 2. 5) aligner run in paired-end mode (with the following options: --maxins 5 -verysensitive-local). Alignments with mapping quality lower than 30 were discarded. The output was in the form of a sparse matrix where each fragment of every chromosome was given an unique identifier and every pair of fragments was given a contact count if it was nonzero. Fragments were then filtered based on their size and total coverage. First, fragments shorter than fifty base pairs were discarded. Then, fragments whose coverage was less than one standard deviation below the mean of the global coverage distribution were removed from the initial contact map.
Validation with the genetic map The validation procedure with respect to linkage data was implemented as part of instaGRAAL. Briefly, the script considers a set of pseudochromosomes where regions are separated by SNP markers, and a set of Hi-C scaffolds where regions are bins separated by restriction sites. It then finds best-matching pairs of pseudochromosomes/scaffolds by counting how many of these regions overlap from one set to the other. Then, for each pair, the bins in the Hi-C scaffold are rearranged so that their order is consistent with that of the corresponding pseudochromosome. Such rearrangements are parsimonious and try to alter as little as possible. Since there isn't a one-to-one mapping from restriction sites to SNP markers, some regions in the Hi-C scaffolds are not present in the pseudochromosomes, in which case they are left unchanged.
Benchmarking For each genome, the 3D-DNA program was run using the run-assembly-pipeline. sh entrypoint script with the following options: -i 1000 --polisher-input-size 10000 --splitterinput-size 10000. The Hi-C data was prepared with the Juicer pipeline as recommended by 3D-DNA's documentation. The SALSA2 program was run with the -cutoff=0 option, and misassembly correction with the -clean=yes option. No expected genome size was provided. The program halted after 9 iterations for Ectocarpus sp. , and 18 iterations for Desmarestia herbacea. Hi-C data was prepared with the Arima pipeline as recommended by SALSA2's documentation.
Funding This research was supported by funding to R. K. and S. M. C. from the  European Research Council  under the  Horizon 2020 Program  ( ERC grant  agreements  260822  and  638240 , respectively) and from  Agence Nationale pour la Recherche  ( JPI-EC-AMR STARCS ANR-16-JPEC-0003-05 ) to  R. K. This  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No 764840.
com/koszullab/ectocarpus_scripts. 0 2 7 0 3 3 Scaffolds integrated into pseudochromosomes (out of 1561) 325 531 793 0 4 5 0 0 Percent sequence data Integrated into pseudochromosomes 7 8 70. 10 % 5 1 90. 50 % 96. 80 % 0 9 1 0 0 Integrated oriented scafflolds in the (pseudo)chromosomes 12 % 12 15 2 1 49 % 100 % 0 22 1 0 23 1 0 Number of (pseudo)chromosomes 34 26 1 28 27 0 27 1 0 33 2 0 36 2 0 37 1 0 51 1 0 69 1 0 74 2 List of abbreviations 0 0 76 97 1 1 RF: Restriction fragment 0 0 99 107 1 1 MCMC: Markov Chain Monte Carlo LD: Linkage desiquilibrium IQR: Inter-quartile range 3C: chromosome conformation capture GRAAL -3D: genome (re)assembly assessing likelihood from 3D Availability of data and materials The raw, unfiltered datasets generated and analysed during the current study are available in the SRA repository, SRR8550777.
, 2017). Four levels of detail were then considered. At the city scale, data obtained from GIS database for Toulouse allowed us to integrate the local urban horizon line which is then integrated in a. hor auxiliary file. At the district scale, 2. 5D data exported from ArcMap (ArcGIS) for each one of the three districts studied were used. At the urban block scale, geometries were considered as simplified 3D buildings to be vectorised together with the envelope detailed materials attributed in the elements of decomposed façades. This fourth level of detail were considered for façades and surrounding ground surfaces allowing more detailed response in terms of irradiation (short and longwave) exchange (Figure  7 ). In order to model the 2. 5D and 3D geometry, Citysim allows importing regular 3DFACE.
Acknowledgments We acknowledge the  French Environment and Energy Management Agency ,  ADEME , for the financial support of the research project 'multipliCités' in the framework of the national call for projects, MODEVAL-URBA 2015. The authors would also like to thank the Brazilian research funding agency  CAPES  for the financial support, and the companies  ESSS  and  ESTECO  for providing the modeFRONTIER® license used in this research.
The data were obtained as part of the MUSE consortium Guaranteed Time Observations (GTO) programme and first commissioning run. The final goal of our project in lensing clusters is to set strong constraints on the relative contribution of the LAE population to cosmic reionization. As shown in  Richard et al. (2015)  for SMACSJ2031. 8-4036, Bina et al. (2016)  for A1689,  Lagattuta et al. (2017)  for A370,  Caminha et al. (2016)  for AS1063,  Karman et al. (2016)  for MACS1149 and  Mahler et al. (2018)  for A2744, MUSE is ideally designed for the study of lensed background sources, in particular for LAEs at 2. 9 ≤ z ≤ 6. 7. The MUSE instrument provides a blind survey of the background population, irrespective of the detection or not of the associated continuum. This instrument is also a unique facility capable of deriving the 2D properties of "normal" strongly lensed galaxies, as recently shown by  Patricio et al.
9 and 6. 7. The data were obtained as part of the MUSE GTO programme and first commissioning run (for A1689 only). All the observations were conducted in the nominal WFM-NOAO-N mode of MUSE. The main characteristics of the four fields are listed in Table  1. The geometry and limits of the four FoVs are shown on the available HST images, in Fig. 1. A1689: Observations were already presented in  Bina et al. (2016)  from the first MUSE commissioning run in 2014. The total exposure was divided into six individual exposures of 1100 s. A small linear dither pattern of 0. 2 was applied between each exposure to minimize the impact of the structure of the instrument on the final data. No rotation was applied between individual exposures. A2390, A2667, and A2744: The same observational strategy was used for all three cubes: the individual pointings were divided into exposures of 1800 sec.
Data reduction All the MUSE data were reduced using the MUSE ESO reduction pipeline  (Weilbacher et al. 2012 (Weilbacher et al. , 2014)). This pipeline includes bias subtraction, flat fielding, wavelength and flux calibrations, basic sky subtraction, and astrometry. The individual exposures were then assembled to form a final data cube or a mosaic. An additional sky line subtraction was performed with the Zurich Atmosphere Purge software (ZAP;  Soto et al. 2016). This software uses principal component analysis to characterize the residuals of the first sky line subtraction to further remove them from the cubes. Even though the line subtraction is improved by this process, the variance in the wavelength layers affected by the presence of sky lines remains higher, making the source detection more difficult on these layers.
Complementary data (HST) For all MUSE fields analysed in this paper, complementary deep data from HST are available. They were used to help the source detection process in the cubes but also for modelling the mass distribution of the clusters (see Sect. 4). A brief list of the ancillary HST data used for this project is presented in Table  2. For A1689 the data are presented in  Broadhurst et al. (2005). For A2390 and A2667, a very thorough summary of all the HST observations available are presented in  Richard et al. (2008)  and more recently in  Olmstead et al. (2014)  for A2390. A2744 is part of the HFF programme, which comprises the deepest observations performed by HST on lensing clusters. All the raw data and individual exposures are available from the Mikulski Archive for Space Telescopes (MAST), and the details of the reduction are addressed in the articles cited above.
(3) An example of a S/N curve defined this way is provided in Fig. 7. For a given source, this computation was done on every layer of every cube part of the survey. When computing the S/N of a given source in a cube different from the parent cube, the seeing difference (see Table  1 ) is accounted for by introducing convolution or deconvolution procedure to set the detection image of the LAE to the resolution of the cube considered. As a result for each LAE, three additional images are produced. The four images (original detection image plus the three simulated ones) are then used to measure the value of the brightest pixels in all four seeing conditions. For the deconvolution a python implementation of a Wiener filter part of the Scikit-image package (van der Walt et al. 2014) was used. The deconvolution algorithm itself is presented in  Orieux et al.
where Φ * is a normalization parameter, L * a characteristic luminosity that defines the position of the transition from the power law to the exponential law at high luminosity, and α is the slope of the power law at low luminosity. In logarithmic scale the Schechter function is written as Φ log (L)d(log L) = L log e Φ * L * L L * α exp - L L * d(log L). ( 11 ) This function represents the numerical density per logarithmic luminosity interval. The fits were done using the Python package Lmfit  (Newville et al. 2014) , which is specifically dedicated to nonlinear optimization and provides robust estimations for confidence intervals. We define an objective function, accounting for the strong asymmetry in the error bars, whose results are then minimized in the least-squares sense, using the default Levenberg-Marquardt method provided by the package.
The direct consequence is that we are not very efficient in probing the transition area around L * and the high luminosity regime of the LF. Instead, the lensing regime is more efficient in selecting faint 4 Lmift uses the emcee algorithm implementation of the emcee Python package (see  Foreman-Mackey et al. 2013)  and low luminosity galaxies and is therefore much more sensitive to the slope parameter. To properly determine the three best parameters, additional data are needed to constrain the bright end of the LFs. To this aim, previous LFs from the literature are used and combined together into a single average LF with the same luminosity bin size as the LFs derived in this work. This last point is important to ensure that the fits are not dominated by the literature data points that are more numerous with smaller bin sizes and uncertainties.
Acknowledgements. We thank the anonymous referee for their critical review and useful suggestions. This work has been carried out thanks to the support of the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the  "Investissements d'Avenir" French government  programme managed by the  ANR. Partially funded by the  ERC  starting grant CALENDS (JR, VP, BC, JM), the  Agence Nationale de  la recherche bearing the reference  ANR-13-BS05-0010-02  (FOGHAR), and the  "Programme National de Cosmologie and Galaxies" (PNCG)  of  CNRS/INSU ,  France. GdV , RP, JR, GM, JM, BC, and VP also acknowledge support by the  Programa de Cooperacion Cientifica -ECOS SUD  Program  C16U02. NL acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  669253 ), ABD acknowledges support from the  ERC  advanced grant " Cosmic Gas ',' LW acknowledges support by the  Competitive Fund of the Leibniz Association  through grant  SAW-2015-AIP-2 , and TG acknowledges support from the  European Research Council  under grant agreement ERC-stg-757258 (TRIPLE).
Experimental Results In this section, we test the four variations of our algorithm on several real-world networks, in order to evaluate their performances. The platform for our tests is a server with 1515 GB RAM and 48 Intel(R) Xeon(R) CPU E7-8857 v2 cores at 3. 00GHz, running Debian GNU Linux 8. The algorithms are implemented in C++, and they are compiled using gcc 5. 3. 1. The source code of our algorithm is available at https://sites. google. com/a/imtlucca. it/borassi/publications.
Comparison with the State of the Art The first experiment compares the performances of our algorithm KADABRA with the state of the art. The first competitor is the RK algorithm  [39] , available in the opensource NetworKit framework  [44]. This algorithm uses the same estimator as our algorithm, but the stopping condition is different: it simply stops after sampling k = , and it uses a heuristic to upper bound the vertex diameter. Following suggestions by the author of the NetworKit implementation, we set to 20 the number of samples used in the latter heuristic  [7]. The second competitor is the ABRA algorithm  [40] , available at http://matteo. rionda. to/software/ABRA-radebetw. tbz2. This algorithm samples pairs of nodes (s, t), and it adds the fraction of st-paths passing from v to the approximation of the betweenness of v, for each node v.
The stopping condition is based on a key result in statistical learning theory, and there is a scheduler that decides when it should be tested. Following the suggestions by the authors, we use both the automatic scheduler ABRA-Aut, which uses a heuristic approach to decide when the stopping condition should be tested, and the geometric scheduler ABRA-1. 2, which tests the stopping condition after (1. 2) i k iterations, for each integer i. The test is performed on a dataset made by 15 undirected and 15 directed real-world networks, taken from the datasets SNAP (snap. stanford. edu/), LASAGNE (piluc. dsi. unifi. it/lasagne), and KONECT (http://konect. uni-koblenz. de/networks/). As in  [40] , we have considered all values of λ ∈ {0. 03, 0. 025, 0. 02, 0. 015, 0. 01, 0. 005}, and δ = 0. 1. All the algorithms have to provide an approximation b(v) of bc(v) for each v such that Pr ∀v, b(v) -bc(v) ≤ λ ≥ 1 -δ.
In order to explain these data, we take a closer look at the improvements obtained through the bidirectional BFS, by considering the average number of edges m avg that the algorithm visits in order to sample a shortest path (for all our competitors, m avg = m, since they perform a full BFS). In Figure  2 , for each graph in our dataset, we plot α = log(mavg) log(m) (intuitively, this means that the average number of edges visited is m α ). The figure shows that, apart from few cases, the number of edges visited is close to n 1 2 , confirming the results in Section 4. This means that, since many of our networks have approximately 10 000 edges, the bidirectional BFS is about 100 times faster than the standard BFS. Finally, for each value of λ, we report in Figure  3  the number of samples needed by all the algorithms, averaged over all the graphs in the dataset.
G Wikipedia and IMDB Results In this section, we report our results on the Wikipedia citation network, and on all snapshots of the IMDB actors collaboration network. In the ranking column, we report one number if the position in the ranking is guaranteed with probability 0. 9, otherwise we report a lower and an upper bound, which hold with the same probability. We remark that, as for the IMDB database, the top-k betweenness centralities of a single snapshot of a similar graph (hollywood-2009 in  [10] ) have been previously computed exactly, with one week of computation on a 40-core machine  [47].
G. 1 The Results on the IMDB Graph In 2014, the most central actor is Ron Jeremy, who is listed in the Guinness Book of World Records for "Most Appearances in Adult Films", with more than 2000 appearances. Among his non-adult ones, we mention The Godfather Part III, Ghostbusters, Crank: High Voltage and Family Guy 3. His topmost centrality in the actor collaboration network has been previously observed by similar experiments on betweenness centrality  [47]. Indeed, around 3 actors out of 100 in the IMDB database played in adult movies, which explains why the high number of appearances of Ron Jeremy both in the adult and non-adult film industry rises his betweenness to the top. The second most-central actor is Lloyd Kaufman, which is best known as a co-founder of Troma Entertainment Film Studio and as the director of many of their feature films, including the cult movie The Toxic Avenger.
Results: We developed an image analysis algorithm that semi-automatically detects RHs and measures their length along the whole differentiation zone of roots. This method, implemented as a simple automated script in ImageJ/Fiji software that we termed Root Hair Sizer, slides a rectangular window along a binarized and straightened image of root tips to estimate the maximal RH length in a given measuring interval. This measure is not affected by heavily bent RHs and any bald spots. RH length data along the root are then modelled with a sigmoidal curve, generating several biologically significant parameters such as RH length, positioning of the root differentiation zone and, under certain conditions, RH growth rate. Conclusions: Image analysis with Root Hair Sizer and subsequent sigmoidal modelling of RH length data provide a simple and efficient way to characterize RH growth in different conditions, equally suitable to small and large scale phenotyping experiments.
1a, b ). For sake of simplicity and robustness we adopted the symmetrical sigmoidal curve f(d) where d is the distance from the root tip, L max and L min define the higher and lower asymptotes, d 50 is the Save the chart and the ROI generated all along the process a Manual step b Step that need to be adjusted in the algorithm source code depending of the processed images; c: strategy inspired from ImageJ discussion that won't be develop here (http://forum. image j. net/t/measu ring-skele tal-lengt h/1262/9) (2) f (d) = L min + L max -L min 1 + e (d 50 -d)/δ distance corresponding to the mid-point or inflexion point of the curve, and δ is a slope factor at inflexion point (Fig. 1b ). The value of L min , which would be ideally equal to zero, corresponds to a small amount of noise measured by the image analysis process on the root section devoid of RHs.
Hence, for clarity and hereafter, L min will now be named L noise. This model fit the experimental data presented below with a r 2 = 0. 93 ± 0. 03 SD. Several biologically significant parameters can be inferred. Firstly, the length of newly formed, fully grown RHs can be measured as L max. Secondly, the intersections of L noise and L max asymptotes with the tangent at the inflection point, placed at d 50 -2δ and d 50 + 2δ, can be used to define in an arbitrary fashion the positions of initiation and arrest of RH growth. Furthermore, these positions also delineate the approximately linear portion of the sigmoid curve, of width 4δ. Since epidermal cells have stopped their elongation in this region, linearity of this central portion indicates that RHs are growing at the same rate. This rate can be easily computed provided that the root growth rate is known (for more details on this, see "Discussion"):.