dataesr/scientific-paragraphs-categorization huggingface dataset:
? TROUBLESHOOTING 29) Polishing genome assembly with Illumina reads. This step can be run with multiple threads. $ cd. /. /02. Illumina-read-based_Assembly_Polishing $ bash LRSDAY. 02. Illumina-read-based_Assembly_Polishing. sh ▲CRITICAL STEP When running LRSDAY for your own data, if you performed PacBio sequencing and also have access to the PacBio SMRT Analysis software package (http://www. pacb. com/products-and-services/analytical-software/smrt-analysis/), we recommend to run the first-pass of assembly polishing based on the raw PacBio reads by using PacBio's own Quiver/Arrow pipeline 10 before proceeding to this step. Likewise, if you performed Oxford Nanopore sequencing, we recommend using nanopolish (https://github. com/jts/nanopolish) or equivalent tools to polish the genome assembly using the raw Nanopore reads first.
? TROUBLESHOOTING Troubleshooting advice can be found in Table  2. • TIMING The following timing information was measured on a Linux computing server with an Intel Xeon CPU E5-2630L v3 (1. 80GHz) using four threads. Enable multithreading can substantially decrease the processing time. Those optional steps were not processed when measuring the computation time. Step 1-22, set up LRSDAY: < 1h. Step 23-24, prepare the project directory for the test example: 5 s. Step 25, prepare the reference genomes for the test example: 1 s. Step 26, prepare the long reads for the test example: 22 min. Step 27, prepare the Illumina reads for the test example: 20 min. Step 28, de novo assembly using long-reads: 18 h. Step 29, assembly polishing using Illumina reads: 1h. Step 30, reference-guided scaffolding for the raw assembly: 8 min.
5) Place the downloaded GATK package (file name: "GenomeAnalysisTK-3. 8-0. tar. bz2") in the "LRSDAY/build/GATK-3. 8" directory by entering the following command in terminal: Go to the website (http://www. girinst. org) and register a user account to download the most recent version of the RepeatMasker-edition RepBase database (current version:.
AKNOWLEDGEMENTS We thank  Gilles Fischer ,  Samuel O'Donnell  and  Lorenzo Tattini  for testing LRSDAY and providing valuable feedback. This work was supported by  ATIP-Avenir (CNRS/INSERM) ,  Fondation ARC pour la Recherche sur le Cancer  ( PJA20151203273 ),  Marie   Curie Career Integration Grants  ( 322035 ),  Agence Nationale de la Recherche  ( ANR-16-CE12-0019 ,  ANR-13-BSV6-0006-01  and  ANR-11-LABX-0028-01 ),  Cancéropôle PACA  (AAP émergence 2015) and a  DuPont Young Professor Award  to G. L. J. -X. Y. is supported by a postdoctoral fellowship from  Fondation ARC pour la Recherche sur le Cancer  ( PDF20150602803 ).
5) Place the downloaded GATK package (file name: "GenomeAnalysisTK-3. 8-0. tar. bz2") in the "LRSDAY/build/GATK-3. 8" directory by entering the following command in terminal: Go to the website (http://www. girinst. org) and register a user account to download the most recent version of the RepeatMasker-edition RepBase database (current version: $ chmod 755 trf409. linux64 $ ln -s trf409. linux64 trf $ mv GenomeAnalysisTK-3. 8-0. tar. bz2 $LRSDAY_HOME/build/GATK-3. 8 6) Uncompress and set up GATK by entering: $ tar xjf GenomeAnalysisTK-3. 8-0. tar. bz2 17) RepBaseRepeatMaskerEdition-20170127) for RepeatMasker. Place the downloaded file $ mv GenomeAnalysisTK-3. 8-0*/GenomeAnalysisTK. jar. in the "$LRSDAY_HOME/build/RepeatMasker" directory. $ chmod 755 GenomeAnalysisTK. jar 18) Uncompress the RepBase database: 7) Go to the official website of MAKER (http://yandell.
DARWIN2. 3 is the reference package used for fuel cycle applications in France. It solves the Boltzmann and Bateman equations in a coupling way, with the European JEFF-3. 1. 1 nuclear data library, to compute the fuel cycle values of interest. It includes both deterministic transport codes APOLLO2 (for light water reactors) and ERANOS2 (for fast reactors), and the DARWIN/PEPIN2 depletion code, each of them being developed by CEA/DEN with the support of its industrial partners. The DARWIN2. 3 package has been experimentally validated for pressurized and boiling water reactors, as well as for sodium fast reactors; this experimental validation relies on the analysis of post-irradiation experiments (PIE). The DARWIN2. 3 experimental validation work points out some isotopes for which the depleted concentration calculation can be improved.
Introduction DARWIN2. 3  [1]  is the French reference package used for fuel cycle applications. It solves the Boltzmann and Bateman equations in a coupling way to compute fuel cycle parameters, at any irradiation and cooling time. The quantities of interest for fuel cycle are fuel inventory, masses, residual decay heat, neutron, α, β, γ sources and spectra, and radiotoxicity. A package is defined by a nuclear data library, one or several computer codes, and one or several calculation schemes (for instance: a reference routine and an industrial routine). For DARWIN2. 3, nuclear data used comes from the JEFF-3. 1. 1 evaluation  [2]. DARWIN2. 3 includes both deterministic transport codes APOLLO2  [3]  (for light water reactors) and ERANOS2  [4]  (for fast reactors), which provide neutronic data to the DARWIN/PEPIN2 depletion solver  [5] , each of them being developed by CEA/DEN with the support of its industrial partners.
These data are self-shielded cross sections libraries and collapsed neutron fluxes. In addition, data such as multigroup activation cross sections at infinite dilution, a full filiation chain, and specific nuclear constants are used in the DARWIN/PEPIN2 solver, directly taken from JEFF-3. 1. 1. This allows taking into account isotopes and reactions that are not described in the simplified filiation chain used in APOLLO2 or ERANOS2. The reference calculation scheme used for PWR DARWIN2. 3 calculations, called CYCLE2008-PWR, is a e-mail: axel. rizzo@cea. fr based on the recommended APOLLO2. 8 calculation scheme  [6]  used for neutron transport calculations. They mainly differ in the flux solver used. From the JEFF-3. 1. 1 multigroup nuclear data, a spatial selfshielding calculation is carried out with SHEM  [7]  recommendations.
Then the neutron flux is calculated using a Pij flux solver to provide neutronic data for the first depletion step. Once the depletion calculation is achieved, the fuel inventory is updated, and so is the flux thanks to a Pij method-based calculation at specific burn up values. The self-shielded cross sections are also updated along the whole depletion calculation at specific burn up values. All these data are stored in a file that will be used by the DARWIN/PEPIN2 depletion solver. The DARWIN2. 3 package has been experimentally validated for light water reactors for the material balance and decay heat calculation  [1]. It has also been experimentally validated for sodium fast reactors for the material balance of the main actinides and fission products involved in burn up-credit calculations  [8].
This experimental validation relies on the analysis of dedicated PIE, mostly consisting in the chemical characterization of in-pile irradiated fuel pellets. The experimental validation of the DARWIN2. 3 package for material balance calculation was performed for a large range of burnup from 10 to 85GWd/t for UOX fuels and 10 to 60GWd/t for MOX fuels. As regards decay heat calculation for PWR UOX fuel assemblies, the experimental validation of the package covers both short cooling time (40 minutes -40 days) for lowburnup fuels  [9]  and long cooling time (10-30 years) for intermediate to high burnup (20-50 GWd/t) ranges  [1]. The DARWIN2. 3 experimental validation work points out some isotopes for which the depleted concentration calculation can be improved; all the more some of them play an important role in the fuel cycle management (fuel inventory, decay heat, radiotoxicity.
). Table  1  displays some examples of nuclides for which the calculation could be improved; and illustrates the results of the experimental validation for these nuclides. The DARWIN2. 3 calculation biases and uncertainties are defined with the C/E values provided by the experimental validation. The uncertainties associated with C/E values gather those due to the irradiation history modelling, those due to the burn up determination, and those due to the experimental uncertainties. Some other isotopes such as 14 C, have no available experimental validation, and the accuracy associated with the calculation of their concentration can be provided by a simplified propagation of a priori nuclear data covariance, provided that the latter are available. Developing and improving the performances of a neutronic package relies on the VVUQ process, where VVUQ stands for Verification, Validation and Uncertainties Quantification.
Work plan A work plan for the improvement of the DARWIN2. 3 calculations has been established. It relies on identifying the potential ways of improving it. These opportunities are determined through the quantification and the analysis of biases and uncertainties engendered by the deterministic methods used in DARWIN2. 3 calculations on the one hand and by nuclear data on the other hand. This quantification is carried out via numerical and experimental validation methodologies from the VVUQ process. The three axis of the work plan are presented in the following paragraphs. The recent coupling of TRIPOLI4 stochastic transport code with the MENDEL deterministic depletion solver allows performing depletion calculation with TRIPOLI4. Even though this process is not considered as a reference procedure for depletion calculation, the benchmarking of TRIPOLI4 and DARWIN2.
3 has been investigated to provide first elements of modelling biases quantification. Early results are presented in the following part. Regarding the experimental validation, the feasibility of an experimental program in the MINERVE reactor at Cadarache is currently investigated. This program is called CARMINA for CARbon Measurement for Induced Neutron Activation. Its goal is to validate the 14 C buildup via 17 O(n,α) and 14 N(n,p) reactions, which are the major ways of formation in PWR UOX/MOX fuel and cladding. A third approach to improve the DARWIN2. 3 package calculations is to work on nuclear data, analyze it and re-estimate nuclear data by assimilation of integral experiment results. For instance, 137 Cs is systematically underestimated in PWR UOX fuel depletion calculations with DRAINW2.
Early results Table  2  summarizes the results obtained for some nuclides of interest for the fuel cycle, classified by decreasing abundance at 60GWd/t. The TRIPOLI4 standard deviation given in Table  2  is associated to the dispersion of the concentration values obtained with 64 independent replicas. The discrepancies on the nuclides presented are below 1%. The values can be compared with table 1: one can note that the APOLLO2 / TRIPOLI4 differences are low compared to the C/E values. Thus, the major part of the discrepancies presented in Table  1  should be induced by nuclear data uncertainty. It is likely that the self-shielding approximations and the flux solver used are responsible for the discrepancies given in Table  2. Further studies will be led to investigate both possibilities, respectively by performing a depletion calculation with a refined energy mesh (to dispense with self-shielding models) and by using a more accurate flux solver, based on the Method of characteristics (MOC).
Conclusion This paper exposes the guidelines for the improvement of the calculation of nuclides of interest for the fuel cycle with the DARWIN2. 3 package. A work plan has been established that can be split in three parts: • quantification of modelling biases using the numerical validation methodology, for which early results have been presented, • computation of C/E values using the experimental validation methodology, and • nuclear data improvement. The modelling biases quantification is tackled here to estimate its contribution on the C/E values taken from the experimental validation report of the DARWIN2. 3 package. Normally, one would expect these modelling biases to be low, since the CYCLE2008 calculation scheme is derived from the APOLLO2 SHEM-MOC scheme, which has been extensively validated.
Perspectives The next step of the study will tackle the interpretation with TRIPOLI4 of an experiment from the DARWIN2. 3 qualification report experimental database. This will allow comparing DARWIN2. 3 against TRIPOLI4 and therefore having an accurate estimation of the modelling biases since the geometry, the irradiation history, etc. compared would be the same. A work on filiation chains will be done to have an even more accurate consistency between both calculations, wishing to confirm that the major part of the C/E values is gathered by nuclear data. In addition to this, and in order to extend the experimental validation of the DARWIN2. 3 package, the CARMINA experimental program feasibility study is ongoing. It will aim at providing information for the 14 C concentration calculation. More generally, it would be interesting to extend the experimental validation of the DARWIN2.
This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words. The coherence between topics is ensured through a copula, binding the topics associated to the words of a segment. In addition, this model relies on both document and segment specific topic distributions so as to capture fine grained differences in topic assignments. We show that the proposed model naturally encompasses other state-of-the-art LDA-based models designed for similar tasks. Furthermore, our experiments, conducted on six different publicly available datasets, show the effectiveness of our model in terms of perplexity, Normalized Pointwise Mutual Information, which captures the coherence between the generated topics, and the Micro F1 measure for text classification.
Experiments We conducted a number of experiments aimed at studying the impact of simultaneously segmenting and assigning topics to words within segments using the proposed segLDAcop model. Datasets: We considered six publicly available datasets derived from Pubmed 2   (Tsatsaronis et al. , 2015) , Wikipedia  (Partalas et al. , 2015) , Reuters 3  and New York Times (NYT) 4   (Yao et al. , 2016). The first two collections were considered in (Balikas et al. , 2016a), we followed their setup by considering 3 subsets of Wikipedia with different number of classes (namely, Wiki0, Wiki1 and Wiki2). The Reuters dataset comes from Reuters-21578, Distribution 1. 0 as investigated in  (Bird et al. , 2009)  and the NYT dataset is collected from full text of New York Times global news, from January 1st to December 31st, 2011.
These collections were processed following  (Blei et al. , 2003)  by removing a standard list of 50 stop words, lemmatizing, lowercasing and keeping only words made of letters. To deal with relatively homogeneous collections, we also removed documents that are too long. The statistics of these datasets, as well as the admissible maximal length for documents, in terms of the number of words they contain, can be found in Table  1.
Settings: We compared our models (segLDAcop p=0 , segLDAcop λ=0 , segLDAcop) with three models, namely the standard LDA model, and two previously introduced models aiming at binding topics within segments: 1. LDA: Standard Latent Dirichlet Allocation implemented using collapsed Gibbs sampling inference  (Griffiths and Steyvers, 2004)   (Bird et al. , 2009) ) and single words. In addition, a copula is also used to bind topics within NPs, from the document specific topic distribution. Both senLDA and copLDA implementations, can be found in https://github. com/ balikasg/topicModelling. In all models α and β play a symmetric role and are respectively fixed to 1/K, following  (Asuncion et al. , 2009). For copula based models, λ is set to 5, following  (Balikas et al. , 2016b). As already discussed, p is set to 0 for segLDAcop p=0 ; it is set to 0.
Perplexity We first randomly split here all the collections, using 75% of them for training, and 25% for testing. In order to see how well the models fit the data and following  (Blei et al. , 2003) , we first evaluated the methods in terms of perplexity defined as: P erplexity = exp -d∈D w∈d log K k=1 θ d k φ k w d∈D |d| , where d is a test document from the test set D, and |d| is the total number of words in d, and K is the total number of topics. The lower the perplexity is, the better the model fits the test data. Table  2  shows perplexities of different methods for K = 20 and K = 100 topics. From Table  2 , it comes out that the best performing model in terms of perplexity over all datasets and for different number of topics is segLDAcop. Further, segLDAcop λ=0 , that uses both document and segment specific topic distributions, performs better than segLDAcop p=0 , which in turn outperforms copLDA, bringing evidence that using all possible segmentations rather than only NPs unit extracted using a chunker yields a more flexible and natural topic assignment.
Topical induced representation for classification Some studies compare topic models using extrinsic tasks such as document classification. In this case, it is possible to reduce the dimensionality of the representation space by using the induced topics  (Blei et al. , 2003). In this study, we first randomly splitted the datasets, except NYT that does not contain class information, into training (75%) and test (25%) sets. We then applied SVMs with a linear kernel; the value of the hyperparameter C was found by cross-validation over the training set {0. 01, 0. 1, 1, 10, 100}. For datasets where certain documents have more than one label (Pubmed, Reuters), we used the one-versus-all approach for performing multi-label classification. In Table  3 , we report the Micro F1 (MiF) score of different models on the test sets.
For the reported scores, we aggregate the topic coherence scores over three different topic cardinalities: N ∈ {5, 10, 15}. segLDAcop model which  uses copulas and segmentation together, shows the best score for the given reference meta-data (Wikipedia) in all of the datasets. It should be noted that segLDAcop λ=0 which has not copula binder inside the model has less improvement against the segLDAcop p=0 which has the copula. This means using copula has more effect on the topic coherence than only the segment-specific topic distribution.
Visualization In order to illustrate the results obtained by segLDAcop, we display in Figure  4  the top 10 most probable words over 5 topics (K = 20) for the Reuters dataset, for both segLDAcop and LDA. In segLDAcop, topic 1, the top-ranked words are mostly relevant to the topic "date" (e. g. ,  march, january, year, fall, february, week). However, a similar topic learned by LDA appears to involve less such words (year, january, february), indicating a less coherent topic. Figure  5  illustrates another aspect of our model, namely the possibility to detect topically coherent segments. In particular, as one can note, the sentence is segmented in six parts by our model, the first one is a NP, Ralph Borsodi where one single topic is assigned to both words. We observe a similar coherence in topic assignments on other NPs and segments, in which a single topic is used for the words involved.
Introduction Beyond the huge increase in computer resources required by the rapid expansion of Next-Generation Sequencing technologies, the community recognizes the urgent need for software systems ensuring the reproducibility of the analytic workflows. Indeed, although any publication is conditioned to the deposit of raw sequences in short read archives, there is still no incentive to provide a formal description that would enable a third-party to reproduce the full analytic workflow or even trace the steps and parameters that led from the raw sequences to the published results. Snakemake (Köster and Rahmann, 2012) is a flexible workflow engine developed for large-scale biological data analysis, which is being adopted by a growing community of users. The common practice is to implement a separate script for each analysis, specifying the commands for each step of the workflow (rules) and the desired output files (targets).
Rules and workflows Snakemake is a Python library inspired by concepts of the make language: • rules are recipes to perform an operation (using python, R or shell); • dependencies between rules are inferred by inputs and outputs names; • expected result files are defined by a list of targets. The SnakeChunks library currently contains 70 rules, organised in separate files to enable their modular assembly into workflows. This flexibility of composition is illustrated by 5 demo workflows. A given rule can thus be recycled in several workflows, avoiding code duplication, and can readily be included in new custom pipelines. We have interfaced a variety of alternative tools for crucial steps such as download from short read databases, read mapping, peak-calling for ChIPseq, differential expression analysis for RNA-seq.
Metadata and parameters Different tools can be chosen for each step, and their parameters need to be fine-tuned to cater workflows to different datasets. Our generic workflows can be adapted to a given study case by editing three files: • a tab-delimited file containing the list of samples to process, as well as any relevant information about these samples; • a tab-delimited file describing the design of the analysis (e. g. conditions or samples to be compared); • a YAML-formatted configuration file, specifying the analysis-specific information: directories, reference genome, annotation files, tools, and optionally, customized parameters for each tool. These metadata and parameter files actually contain all the required information to submit NGS data to short read databases.
Conclusion and Perspectives The efficiency and flexibility of the SnakeChunks framework was demonstrated with a variety of use cases of RNA-seq and ChIP-seq analysis in D. melanogaster,  A. thaliana (Castro-Mondragón, Rioualen et al. , 2016) , mouse, yeast, bacteria… In a study of Glossina palpalis infection by Trypanosoma congolense  (Tsagmo et al. , 2017) , we submitted the raw data in GEO with an archive containing the version of the SnakeChunks library used to produce the results along with the metadata and configuration files, which enables anyone to reproduce the full processing from the raw reads to the published results. All of this material is versioned and available in a public GitHub repository (github. com/SnakeChunks/SnakeChunks). It has been successfully ported and tested on unix servers, Linux and Mac OS X personal computers, and in virtual environments.
The calculation of the Prompt Fission Neutron Spectrum (PFNS) was performed using the FIFRELIN Monte Carlo code simulating the de-excitation of the whole fission fragments. This de-excitation is governed by the Hauser-Feshbach statistical model, which has the advantage to take into account the conservation laws for the energy, spin and parity of the initial and final states. In this way, the competition between prompt neutron and prompt gamma emission can be properly accounted for. Assuming that the prompt neutron emission comes only from an evaporation process of the fully accelerated fission fragments, our calculations are not able to reproduce satisfactorily the experimental data. In this context, we have added an additional source of neutrons that may arise during the sudden rupture of the neck (the so-called scission neutrons).