dataesr/scientific-paragraphs-categorization huggingface dataset:
The slightly positive bias obtained in this study (+17 %) may be linked to the fact that some assimilated observations are not included in our evaluation dataset and/or to differences between the climatological guess and the mean precipitation amount of the 4 years under study. The strong overestimation of AROME is particularly notable for the largest amounts. The different distribution of precipitation and SD for AROME, with a higher proportion of strong precipitation than of strong snow accumulations, may be due to settling effects: the stronger the snowfall, the stronger the snowpack settles under its own mass, which shifts the distribution to the left. The overestimation of precipitation by AROME compared to precipitation gauges seems to be an apparent paradox, as we highlighted an opposite behaviour in terms of snow accumulation.
Acknowledgements. The authors acknowledge Electricité De France and  Confederación Hidrográfica del Ebro  for providing snow water equivalent and snow depth measurements from their  Pyrenean automatic stations network ,  Servei Meteorològic de Catalunya  for providing snow depth measurements from the automatic weather stations network of Catalunya,  S. Gascoin  ( CESBIO ) for providing snow depth measurements from automatic station Bassies, and  J. Revuelto  ( Instituto Pirenaico de Ecología ) for providing snow depth measurements from automatic station Izas. We particularly thank  E. Bazile  (CNRM),  F. Gottardi  (EDF-DTG),  S. Morin  ( CNRM/CEN ),  R. Mott  ( WSL-SLF ) and  B. Vincendon  ( CNRM ) for help and discussions and  Jean-Antoine Maziejewski  ( Météo France ) for English editing.
Liquid Chromatography-Mass Spectrometry (LC/MS) provides large datasets from which one needs to extract the relevant information. Since these data are made of non-negative mixtures of non-negative mass spectra, nonnegative matrix factorization (NMF) is well suited for its processing, but it has barely been used in LC/MS. Also, these data are very difficult to deal with since they are usually contaminated with non-Gaussian noise and the intensities vary on several orders of magnitude. In this article, we show the feasibility of the NMF approach on these data. We also propose an adaptation of one of the algorithms aiming at specifically dealing with LC/MS data. We finally perform experiments and compare standard NMF algorithms on both simulated data and an annotated LC/MS dataset. This lets us evaluate the influence of the noise model and the data model on the recovery of the sources.
interactions with the stationary phase. These compounds thus leave the column at different times, referred to as retention times. At each time t, the compounds leaving the column are sprayed, ionized in the source of the mass spectrometer, and then separated according to their mass to charge ratios in the analyzer (i. e. , an orbitrap analyzer in the present study). Each ion having a specific mass-to-charge ratio, the LC/MS process provides a two dimension separation (although imperfect) in both mass and retention time domains. It yields 2D data such as the ones shown in Fig. 1. These data are coined Y ∈ R m×n in the article, and each of the m lines of this matrix is a nsample long spectrum at a given acquisition time.
C. Contribution Although NMF is particularly well suited for processing LC/MS data, it has barely ever been used on this type of data. In this article, we test reference and state-of-the-art NMF algorithms on an annotated LC/MS dataset. We also propose an adaptation of the non-negative generalized morphological component analysis (nGMCA) aiming at specifically dealing with LC/MS data. The comparison highlights the behaviors of the algorithms in difficult settings, with large dynamics, multiplicative noise and potential non-linearities, and shows the efficiency of our adaptation. In the final section, based on the experiments, we discuss further improvements which could be brought to the existing algorithms in order to better handle LC/MS data.
II. LC/MS DATASET The data considered in this article were acquired from a mixture of eleven commercial chemical compounds for which the mass spectra and retention times are known (see Fig. 5  for the list of compounds). This sample was analyzed in an LC/MS pipeline using an orbitrap mass analyzer  [9] ,  [10]. We focus on a time range going from 2 to 18min and masses from 69 to 644 Dalton (Da) since these ranges concentrate most of the information of the sample. Since all ions related to the eleven molecules and their retention times were known, we can build a reference source matrix S annot. , in which each line is the mass spectrum of one of the molecules.
C. Processing pipeline The processing of LC/MS data is usually performed with software such as XCMS  [14]  or MZmine  [15] , which perform (i) automatic peak detection, (ii) alignment of features in the m/z and chromatographic retention time domains, and (iii) results are returned as a peak table containing variable identity (i. e. , m/z and retention time) and signal abundances (i. e. , peak intensities and/or area of extracted ion chromatographic peaks) in the samples. As mentioned in  [6] , NMF is however particularly well-suited for the processing of LC/MS data and combines the extraction and gathering stages into a unique step. In this article, we evaluate the performances of several NMF approaches on an annotated dataset and show the feasibility of this approach, with the following processing pipeline: 1) Mass gridding: Each spectrum acquired by the mass spectrometer has a specific mass grid, different from the ones acquired at other times.
Also, the initial precision can mostly be recovered thanks to the inversion step 5). 4) Factorization: the data is decomposed as Y sub ≈ AS sub with A and S non-negative matrices, using an NMF algorithm. 5) Inversion: The full precision is recovered by solving the inverse problem 4 using the mixing matrix A found at the previous stage and the non-subsampled data Y explicited on the shared mass grid of stage 2). argmin S≥0 1 2 Y -AS 2 2 , (4) III. NMF ALGORITHMS Algorithms in NMF can aim at solving different formulations of the problem, with different divergences D and regularizations J. Also, because of its non-convexity, even one algorithm with different initializations can produce different results. In this section, we therefore evaluate the performances of several algorithms on LC/MS data.
C. Adaptation of nGMCA for LC/MS data Considering the drawbacks of the previous methods, we propose to adapt nGMCA to the case of LC/MS data by: • using divergence (7) instead of divergence (6) so as to be more robust to multiplicative noise. • add a stochastic term D in order to avoid the multiple stationary points, which arise from the specific structure of LC/MS data. 1 http://www. cosmostat. org/GMCALab. html 2 https://sites. google. com/site/nicolasgillis/code 3 ratio of coefficients smaller than 10 -6 times the largest one. 4 http://www. cc. gatech. edu/ ∼ hpark/nmfsoftware. php At each iteration k, both A and S are updated once -such as in standard nGMCA-so as to approach the solution of: argmin A≥0, S≥0 1 2 Y + σ (k) D D (k) -AS Σ 2 2 + Λ (k) S 1. (8) The coefficients of D (k) are taken as independent centered and reduced Gaussian variables, and σ (k) decreases at each iteration and reaches 0 at the end of the algorithm so as to causing a bias.
Kim & Park algorithm is globally 5dB under this version of nGMCA, but close to 5dB above all the other algorithms. One can recall that this algorithm was used with its standard parameters and was therefore not tuned at all. This highlights the fact that, as well as an adequate noise model, an accurate source model can be beneficial. Indeed, the regularization used in this algorithm favors sources which do not share any coefficient. This model is well verified on the dataset.
B. Qualitative study on the real data The above observations were made on very realistic simulated data. Evaluation of the algorithms on the real dataset is however trickier. Indeed, the annotations only provide the main peaks time and mass localization for each source, but not exactly the spectra which should be recovered. In this section, we observe results obtained on the real dataset so as to develop quantitative criteria. We consider the search of 18 sources, among which we wish to recover the 11 annotated sources. nGMCA based algorithms are provided with estimations of the noise level: σ add =12,000 and σ mult = 0. 05. The elution profiles A annot. in Fig. 7a  can be compared with the ones obtained on the real dataset by nGMCA and non-stat. nGMCA (stoch. ) (respectively Fig. 7b  and 7c ).
A couple of observations can be made from it: • the database is not perfect and may not contain all the compounds present in the mixture: nGMCA and non-stat. nGMCA (stoch. ) agree on the existence of an prominent non-annotated source (purple line at 12. 4min, source #12 and #14 respectively). Its mass spectrum presents a structure typical of the presence of adducts. (source #7, blue line at 11. 45min), and source #10 (dashed cyan line at 12. 1min) with adipic acid (source #9, red line at 11. 9min). The interferences with DLvanillomandelic acid (source #9) do not appear with nonstat. nGMCA (stoch. ), and the ones with the adipic acid are weaker, which tends to suggest a better reconstruction quality obtained with this algorithm. Fig. 8  provides an example of mass spectrum reconstruction with nGMCA for beta-D-fucose, in red crosses.
It is difficult to give a quantitative evaluation of these results on the real dataset. Still, these figures provide some insights on the quality of the identifications of the searched sources: • all the annotated sources are not necessarily recovered. On the top figure (nGMCA), the line associated to carnitine is empty and thus no peak from this source has been identified by the algorithm. Non-stat. nGMCA (stoch. ) however recovers at least one peak from each of the sources on this example. • some estimated sources contain peaks from several compounds (columns with more than one non-null coefficient). These additional peaks are interferences which must be avoided. A comparison between Fig. 9a  and Fig. 9b , seems to indicate that the non-stat. nGMCA (stoch. ) is less prone to interferences than nGMCA.
In summary, the adaptation of nGMCA to non-stationary noise and the use of a stochastic term yields very good and robust results. Kim & Park's algorithm benefits here from the fact that the mixtures in this dataset are not very complex, with spectra sharing few common peaks and elution profiles which do not overlap too much. It would however not perform so well in more difficult settings where the dominant mixture model is not verified anymore, such as with a larger number of chemical compounds for instance.
Given the logistical difficulties associated with initiating and maintaining mass balance monitoring programmes in alpine environments, such a method based on remotely sensed imagery may prove useful in helping to reconstruct mass balance records for other New Zealand glaciers. In lieu of direct glaciological monitoring, the equilibrium line altitude (ELA) is regarded as a good proxy for annual balance  (Braithwaite, 1984; Rabatel et al. , 2005). Accordingly, ELA measurements from remotely sensed data have been used as a substitute for direct mass balance monitoring  (Chinn et al. , 2005). This is the approach currently used in New Zealand to infer the mass balance variability of 50 "index" glaciers situated in the Southern Alps, including Brewster Glacier  (Chinn et al. , 2012). For these glaciers, the position of the end-of-summer snowline (EOSS, an approximation of the ELA) is identified from oblique photographs obtained from annual aerial surveys conducted since 1977.
Based on the method originally proposed by  Dumont et al. (2012)  we assess the relationships between albedo measurements derived from MODIS data and the annual balance of Brewster Glacier. This allows the mass balance series to be extended over the time period 2000-2013 when MODIS/TERRA images are available. We further develop the method by showing how the evolution of glacier-wide albedo over a mass balance year can be used to reconstruct both winter and summer mass balance records. This provides 5 additional years  (2000) (2001) (2002) (2003) (2004)  of seasonal mass balance data that may provide valuable insights into the accumulation and ablation processes governing the evolution of Brewster Glacier  (Cullen et al. , 2016). Finally, an application of this method is presented whereby the extended mass balance series is used to reconstruct the mass balance record through the full length of the EOSS programme.
The next section provides a description of Brewster Glacier, the observed mass balance record, the field albedo measurements, and the required MODIS data products. In the third section, the albedo retrieval algorithm and the mass balance reconstruction method are described. The fourth section considers the results of the MODIS accuracy assessment and evaluates the relationships between MODIS-derived albedo, mass balance, and SLA. The reconstructed mass balance records are also presented. The final section discusses the reconstructed sequence of mass balance of Brewster Glacier and its links to regional climate in the Pacific region. 2 Site description and data sets.
Further work to resolve the mechanism for driving the mass balance anomalies is still required, ideally by modelling the surface energy and mass balance of Brewster Glacier over the EOSS period. 7 Data and code availability MODIS data used in this research are freely available from the Level 1 and Atmosphere Archive and Distribution System (LAADS) Web. Ground albedo data are available upon request from N. Cullen. Glaciological mass balance data are published in  Cullen et al. (2016). NZSoSDEM is freely available from the koordinates. com geographical data repository. The MODImLab software is available upon request from P. Sirguey.
Mutation analysis introduces program defects with the intend of verifying whether candidate tests are able to trigger anomalous behaviour. In case the tests can distinguish the defective behaviour from that of the original program, they are considered of good quality -otherwise developers need to design new tests. While, this method has been shown to be effective, industry-scale code challenges its applicability due to the sheer number of mutants and test executions it requires. In this paper we present PIT, a practical mutation testing tool for Java, applicable on real-world codebases. PIT is fast since it operates on bytecode and optimises mutant executions. It is also robust and well integrated with development tools, as it can be invoked through a command line interface, Ant or Maven. PIT is also open source and hence, publicly available at http://pitest.
DOI: http://dx. doi. org/10. 1145/0000000. 0000000 drawback; it only checks if a line/instruction is tested, not how well it is tested. Mutation testing  [4]  is a technique that gives a better understanding of what the tests exercise on the program under analysis. Mutation testing consists of introducing defects, in the form of small code modifications. Those modifications should result in an abnormal behaviour when exercised by the tests. If the tests fail to expose the defects then the testers/developers can reasonably infer that the tests are not checking every possible behaviour and that the tests need to be improved. This paper presents PIT, a mutation testing system for Java. PIT is considerably fast as it manipulates bytecode and runs only the tests that have a chances to kill the used mutants (i.
PIT: REAL WORLD MUTATION PIT is a mutation testing framework for Java developed to support the day to day development on real codebases. This means that PIT aims at: • having a good integration with build tools (e. g. , Maven, Ant, Gradle), integrated development environments (IDEs, such as, Eclipse or IntelliJ) and static code analysis tools (e. g. , SonarQube). It is easy to start working with PIT using build tools • being fast. PIT uses three techniques to obtain its quick results: working on bytecode instead of source code, selecting the tests to run against the mutants and minimising the number of mutant executions. • providing a clear report of the tests execution, which makes a navigation between source code and mutants easy and highlights the mutants that were not killed (those for which the testers need to investigate).
Running PIT PIT is fully integrated to a variety of build tools, IDEs and static code analysis tools. Thus there is no need for additional effort when one of these common tools is used. To use PIT with Ant or Maven we need to add a task (or plugin) to their build file so that PIT's behaviour is configured. PIT's configuration is straightforward and can be limited to the specific classes we want to test. We can also configure the output directory (for the test reports). A number of other parameters are also offered (e. g. , mutation operators, timeout factor that is used to infer that an infinite loop has been encountered). Once the PIT task has been configured, the build tool can be used without any other concern. This means that PIT will not change the workflow, it will make its process and generate its report without any user interference.
Mutant Generation and Execution PIT generates mutants via bytecode manipulation. The approach taken offers significant performance advantages when compared with compiled mutant files as it practically reduces the mutant generation cost to zero (because bytecode manipulation is computationally inexpensive). Also PIT avoids input output operations and keep memory overhead low. The bytecode representation of the mutants does not require any program to be written on the disk but, instead to keep it in memory (to reduce the memory overheads, only a single mutant is held in memory at a time). Mutant generation is a two stage process. An initial scan is performed in the main controlling process. All classes in the system under test are examined and possible mutation points (referred to as MutationIdentifiers) are recorded and stored in memory.
Mutation Report The HTML report generated by PIT uses a colour code to show both the line coverage and the mutation score, see Figure  1  that displays a report on an example application 2. Light green lines correspond to code coverage with no mutant generated: line 11 in the example. Dark green correspond to the lines for which tests were executed and failed (which means the mutants were killed): this represents the mutation coverage, for instance lines 9, 12 and 13 in the example. Light pink shows lines with no code coverage (outside the scope of the tests): line 4 in the example. Dark pink is used to show instructions on which mutants were generated and not detected by the tests (surviving mutants): line 8. Beside each line number, another number, embedding an internal web link, gives the number of mutants generated for the line.
DEMONSTRATION RESULTS To demonstrate the applicability and the performance of PIT we select 5 Java projects (recorded in Table  3 ) that are frequently employed in academic evaluations. Table  2  records: the version, lines of code (calculated with the Ja-vaNCSS tool, number of classes (for which test suites exist) and number of tests are reported. Joda-time is a date and time manipulation library. Jfreechart is a popular library for creating charts and plots. Jaxen is an engine for evaluating XPath expressions. Commons-lang provides a set of utility methods for the commons classes of Java. Finally, commons-collections is a set of data structures for Java. Tables  4  and 5  record the results obtained from the selected programs. These demonstrate that PIT is applicable on real world programs and that the extended operator set is feasible and practical.
Gene and whole-genome duplications are widespread in plant nuclear genomes, resulting in sequence heterogeneity. Identification of duplicated genes may be particularly challenging in highly redundant genomes, especially when there are no diploid parents as a reference. Here, we developed a pipeline to detect the different copies in the ribosomal RNA gene family in the hexaploid grass Spartina maritima from next-generation sequencing (Roche-454) reads. The heterogeneity of the different domains of the highly repeated 45S unit was explored by identifying single nucleotide polymorphisms (SNPs) and assembling reads based on shared polymorphisms. SNPs were validated using comparisons with Illumina sequence data sets and by cloning and Sanger (re)sequencing. Using this approach, 29 validated polymorphisms and 11 validated haplotypes were reported (out of 34 and 20, respectively, that were initially predicted by our program).
2012) , Gossypium  (Flagel et al. 2008; Salmon et al. 2010) , Coffea  (Combes et al. 2011 ), or Triticum (Akhunova et al. 2010)  species. In these systems, the diploid parental (or related) representatives are known and can be used to identify duplicated homeologs in the allotetraploids. The strategy developed in these studies is to assemble NGS datasets using parameters adapted to optimize recovery of paralogous and homeologous copies. Contigs obtained are then compared with diploid parental genomes using species-specific polymorphic sites. The detection of different copies in polyploid species where the parents are not identified or extinct still requires the development of adapted tools. The goal of this study is to develop a bioinformatic pipeline to detect the different copies within a set of NGS reads from a highly polyploid species without any reference parental diploid genome.
We developed this pipeline to explore the heterogeneity of the coding and noncoding regions of the highly repeated 45S unit of the hexaploid Spartina maritima. The strategy was to: (i) identify single nucleotide polymorphisms (SNPs) and indels among reads from the hexaploid genome and (ii) assemble reads based on shared polymorphisms (after removing putative sequencing errors) to distinguish the different copies from reads. Polymorphisms were validated using comparisons with Illumina sequence data sets and by cloning and Sanger (re)-sequencing. The method presented here can be used to identify duplicated copies from any sequenced regions of polyploid or diploid genomes.
This first mapping procedure (Figure  2B ) was performed using the GSMapper tools from Newbler (ml = 100 bp; mi = 95%). Annotation of the different 45S domains was performed by aligning the S. maritima contig with Oryza sativa and Zea mays 45S genes available on the NCBI database (GenBank IDs: X00755. 1, AF169230, M11585. 1 and X03989, NR_036655. 1, AF019817, NR_028022. 2, respectively) using BLASTn with default parameters for the alignment algorithm Megablast (Table  1 )  (Altschul et al. 1997). The Oryza sativa genome was downloaded from www. phytozome. net (v. 204) to locate the 59-ETS region. To identify this region, we followed the approach of  Barker et al. (1988) , who identified the repeat unit of the rDNA intergenic region and the transcription initial site.
SNPs detection and sequencing error removal To detect SNPs, we developed a pipeline allowing the parsing of ". ACE" alignment files (Figure  2C ) using the Ace. py program from biopython (http://biopython. org/) and custom python script for editing homopolymer-driven false-positive SNPs. This step produced three files containing original reads, the original contig, and the list of the original reads (Figure  2D ). This pipeline uses a mapping step with GSMapper (v. 2. 6, Roche; default parameters: ml = 40 bp; mi = 90%) to enhance alignments and ease SNP detection. The newly generated ". ACE" file is parsed to extract the aligned reads (Figure  2E ). To remove false-positive SNPs caused by sequencing errors (within reads, and at the first or last position), the first five and the last five nucleotides of each read, as well as nucleotides with frequency lower than 20% were not considered.