dataesr/scientific-paragraphs-categorization huggingface dataset:
Note that Fiona-H does correct indel errors, as found at branching nodes in the tree, but no further indels are considered in the pairwise read alignment. We compare Fiona, Fiona-H and Fiona-RE on data sets with different regimes of coverage and genome complexity in Figure  3. The results show that Fiona-H, without edit distance pairwise comparison, usually shows a drop in gain value of 13-19%. This is due to a loss of sensitivity, where two reads that have several other indel errors downstream or upstream are not judged similar enough to be considered for correction. The optimal error formulation in Fiona, compared with random encounter mode in Fiona-RE, shows a pronounced improvement in error correction for more complex genomes and higher coverages as on the E. coli 163Â and Bordetella pertussis 85Â datasets.
Robustness Fiona uses different statistical formulas to infer the optimal correction parameters (see Section 2). We analyzed the robustness of error correction results on two datasets by varying the user-supplied sequencing error rate between 2 and 10%. Fiona produces corrections of similar quality with difference in gain value 515%, despite the large range of error rates tested, which indicates robust automatic parameter selection (see Supplementary Table  S3 ). Note that the gain is generally better when the error rate is overestimated. This can be explained by the variability of read quality observed in the sequencing sample, i. e. an average error rate does not account for the other reads of low quality.
Comparison with other methods We compared the performance of Fiona with state-of-the-art genome error correction methods for the respective type of sequencing technology. For 454 and Ion Torrent datasets, we compared with Coral 1. 4, Hybrid-Shrec 1. 0 and the error correction module of Allpaths-LG release 44994, which can correct indel errors. For Illumina, we add the tools HiTEC 1. 0. 2 and Quake 0. 3. 4. 2, which are designed for substitution errors. We did not include HybridShrec in those evaluations, as it consistently performed worse than HiTEC. In any case, all programs were run with eight threads if possible. The detailed parameterization of the programs is listed in Supplementary Section S5. Except ECHO and HiTEC, which do automatic parameter selection similar to Fiona, no other correction method adapts parameters depending on the data.
But whereas Allpaths-LG has a fixed set of parameters, Coral and HybridShrec expect the user to optimize its parameters for indel-prone datasets, which we did to allow for a fair comparison. For Coral, we report the results of two versions, Coral and Coral*, the default version with error rate set to 7% and the optimized version with the bestperforming gain and error rate set as high as 25%, respectively (Supplementary Tables  S8-S11 ). Coral* performed better than Coral on all datasets tested, clearly indicating the need for parameter adjustment for indel-prone datasets. HybridShrec has three parameters that have substantial influence on its performance, the strictness parameter for error detection and the minimum and maximum k-value for suffix tree traversal. As HybridShrec often terminates with default strictness value we have varied this parameter between values of 2-7 and took the best result.
Consequently, we report the results as HybridShrec*. The same was done for HybridShrec F , which uses the same k value range as determined by Fiona for a dataset. Even with the optimized set of parameters we explored, HybridShrec sometimes yields negative gain values. The optimized parameters for HybridShrec*, HybridShrec F and Coral* are listed in Supplementary, Sections S9 and S8. We have evaluated all methods on different datasets and list their relative performance in terms of gain, sensitivity, specificity, base error rate after correction, running time and memory in Tables  1  and 2. and Supplementary Tables S4-S7, respectively. Further, we relate the error correction performance to running time of a method in Figure  4 , where the best method appears in the upper left corner. Comparison on 454 data.
We have collected four different 454 dataset with different coverage values for D. melanogaster 18Â, E. coli 13Â, Staphylococcus aureus 34Â and Saccharomyces cerevisae 16Â. These datasets vary in the per-base error rate between 0. 6 and 1. 76%. Errors are often found in sequence regions with homopolymer runs, which are hard to correct  (Quail et al. , 2012). We observe that the optimized version of Coral* always outperforms the default parameters (Coral) on 454 data sets. Similarly, HybridShrec F mostly outperforms the optimized HybridShrec*, owing to adapted k-mer levels for each dataset. Similar to  Yang et al. (2013) , we observe that HybridShrec sometimes yields negative gain values, i. e. that more errors are introduced than corrected. As Figure  4  (right) shows Fiona has the highest gain among all methods except for the S.
aureus dataset, where Coral* performs best. However, Coral* runs $10 times longer than Fiona on this dataset. Allpaths-LG generally is the fastest or among the fastest methods, but with a loss in gain performance between 90 and 50% compared with Fiona. Fiona shows a fast running time for all datasets, and is also memory efficient compared with the Coral and HybridShrec versions. Comparison on Ion Torrent data. We next compared the performance of the different methods on the eight Ion Torrent   S2 ). Optimal corrections always lead to higher gain values datasets. On seven of eight datasets, Fiona significantly outperforms the other methods in terms of gain. Fiona shows an increase in gain to the second best method ranging from 10 (E. coli 163Â) to 56% (Plasmodium falciparum). For the human dataset, only Allpaths-LG and Fiona could be run with the available memory, with a gain improvement of 35% for Fiona.
Only for the E. coli (156Â) dataset Fiona and Coral* have comparable gain values, although Fiona runs approximately six times faster. Except for Fiona, all methods show a large variation in their ability to correct errors as shown in fluctuations of their gain values. For example, all datasets with an error rate 43. 3% are poorly corrected when using Coral default parameters. In these evaluations, Allpaths-LG uses the lowest amount of memory. Fiona's memory usage scales linearly with the dataset size (Supplementary Table  S2 ), which is in line with the expected memory consumption (Supplementary Section S2. 6). Comparison on Illumina data. To show that Fiona is on par with methods that are optimized for Illumina data, we made comparisons on seven datasets. We compared Fiona-H, the  version that corrects indels but only considers hamming pairwise read distance (Fiona-H) to Coral, Allpaths-LG, HiTEC and ECHO.
On all datasets, Fiona-H and Allpaths-LG are the best two methods in terms of gain, with Fiona always being among the two first-ranked methods (Table  3 ). Fiona-H outperforms Allpaths-LG with a significant increase in gain of 17% on the D. melanogaster 5Â and P. syringae 41Â datasets. Conversely, Allpaths-LG ranks first, with a gain increase of 1-3. 5% over Fiona-H on D. melanogaster 28Â, E. coli 30Â and the Caenorhabditis elegans dataset. Of the remaining methods, Quake ranks second two times and third five times. In Supplementary Table  S5 , we list the runtime and memory consumption for these comparisons. Fiona-H scales well with dataset size and has lower memory consumption than HiTEC and Coral. Allpaths-LG and Quake were the fastest methods in our comparisons, with Fiona-H ranking third most of the time.
DISCUSSION In this study, we introduced Fiona, a new algorithm for the correction of sequencing errors without the need for a reference sequence. Fiona builds over existing strategies to accurately correct errors, accounting for indels with automatic parameter selection. One of the main advantages of Fiona is the use of variable seed lengths, combined with a global optimization criterion to choose the best correction for a read. Our experiments show that Fiona outperforms other methods on datasets from different sequencing technologies. For the evaluation of the different error correction methods we chose to use the gain statistic that is commonly used for this task. However, evaluating the accuracy of nucleotide corrections based on the available reference sequence can be misleading, as haplotype variants may be penalized and therefore the number of false-positive/negative corrections inflated.
Despite this disadvantage, the comparison should not favor any method because all compared methods work exclusively on the read set without alignment to the reference sequence. We introduced a new statistic for error detection that uses a hierarchical model for the stepwise process of first selecting a subset of reads from a genome and then introducing errors during sequencing. This formulation provides an easily extendible framework and can be extended to accommodate more general scenarios, like the presence of heterozygous positions in diploid genomes, coverage overdispersion or the distribution of repeat elements, as well as base quality values. For our experiments, we fixed the error rate estimate to 5% for indel-prone datasets and show that reasonable variations to this value lead to minor performance differences.
In principle, the error rate could be estimated from the base-calling procedure of the sequencer. Alternatively, it could be estimated from the raw sequencing data in a preprocessing step as was recently shown by  Wang et al. (2012)  for Illumina data. Further research on how to determine sequencing error rates in the context of de novo assemblies, where no reference sequence is available, is necessary. In conclusion, Fiona is a reliable method that automatically determines parameters, corrects indels and scales well to large datasets. We believe that users will improve their downstream analysis by using Fiona in their pipelines and made it publicly available at http://www. seqan. de/projects/fiona.
Performance on 454 (top) and IonTorrent (bottom) datasetsNote: a The programs were run on machine with 16 physical and 32 virtual cores and 370 GB of RAM. b Out of memory-The table shows the base error rate (e-rate, in percent) before and after correction with the methods as well as the gain statistic. For each dataset, the results with the best gain value are highlighted in bold. The results are separated by sequencing technology, the 454 results are above the IonTorrent results.
Running time and memory consumption for 454 (top) and IonTorrent (bottom) experimentsNote: a The programs were run on machine with 16 physical and 32 virtual cores and 370 GB of RAM. b Out of memory-Time (in minutes and fractions thereof) and memory (in GB, rounded to the next GB) for the read correction runs from Table1. For each dataset, the results with the lowest running time and memory are given in bold. The results are separated by sequencing technology; the 454 results are above the IonTorrent results.
Performance on Illumina datasets This table shows the same metrics as Table1. a The program ran too long-The problems were Coral and HiTEC produced a segfault, requiring more than 72 GB of memory. b The program crashed. ECHO was killed after running more than 4 days in the case of C. elegans and D. melanogaster datasets and the subprogram NeighborJoin crashed on the full E. coli dataset;.
The tree (Figure  4b ) has a hierarchical as opposed to a network structure, and represents a sequence of events occurring in time culminating in the outcome of interest. In a BBN, decision or logic tree, node states are mutually exclusive and exhaustive, but this is not necessarily the case in an event tree (e. g. see  Marzocchi et al. 2006). In this study, OpenPNL b (an open source C++ library initially developed by Intel) was used for the computational BBN. A separate "link connection strength" package, an add-on to OpenPNL by Ebert-Uphoff (2007), was used to calculate mutual information (described below). There are numerous alternative software tools with graphical user interfaces which make BBN inference and analysis accessible. Many also provide API (Application Programming Interface) functionality enabling custom code to be written for automated inference and data input/output.
Expert elicitation A structured expert elicitation is a formalized method for assimilating group judgements in a robust and reproducible way. In a crisis, a carefully targeted elicitation can be completed in an hour or so, including expert calibration, and the findings processed within a further hour, as happened many times in Montserrat in 1995-96 (e. g. Aspinall et al. 2002, pp82-83). In the present exercise, individual expert uncertainty judgements were combined with equal weights using the "Classical Model" formulation  (Cooke 1991; Aspinall and Cooke, 2013)  and its implementation in the EX-CALIBUR software package  (Cooke et al. 2000). The elicitation questions [see Additional file 1] were structured to obtain enough information to enumerate all CPTs, and hence fully characterize the BBN.
To avoid this, we used weights of 1, 1. 5, 1 for the 5, 50 and 95%ile respectivelythat is, giving a higher weighting to the median value, and imposed the further condition that P(blast) < P(magmatic eruption). This approach more closely reproduced the groups' 'best estimate' values, and creates a logically consistent overall outcome. Code has been written to perform random sampling from the best-fit Beta (for binary nodes) and Dirichlet distributions for each node using the Scythe Statistical Library  (Pemstein et al. 2007). Given the relevant distribution parameters, a set of random samples is returned, corresponding to the probability of each possible outcome (with the total summing to 1). These samples populate the conditional probability tables (CPTs) for each node. As there is no real basis for enforcing correlations between the elicited probability density functions, each distribution was sampled independently.
Endnotes a One of us, WPA, was present at various times in Guadeloupe in 1976, participating in monitoring activities. Another, JCK, was Director of the Guadeloupe Volcano Observatory (IPGP) from 1997 to 2001. b OpenPNL is available at http://sourceforge. net/projects/openpnl/ and https://github. com/crishoj/OpenPNL. c As, for example, in two eruptions of the nearby Soufriere of  St. Vincent volcano in 1971 and 1979 (Aspinall et al. 1973; Shepherd et al. 1979). In these instances, there was almost no (recorded) precursory seismicity: in 1971 the nearest seismometer on St Vincent was 30 km from the volcano, while in 1979 there were seismometers on the volcano but only low-level instrumental tremor was detected and then only in the 12 hours before the first explosion. d Both Dirichlet and Generalised Trapezoidal distributions were compared, the Dirichlet distribution gave a better fit to the joint DM quantiles and was used in the final analysis.
Acknowledgements The authors would like to thank  A. Rust ,  J. Gottsmann  and  Y. Legendre  for participating in the expert elicitation and their contributions to discussions. This work was supported by  VOLDIES (Advanced European Research Council  grant to RSJ Sparks). EU  Project  EXPLORIS  ( EVR1-2001-00047 ) and  Aspinall & Associates  partly funded early stages of this work. Support was provided by the  Institut National des Sciences de l'Univers (INSU-CNRS)  and the  CASAVA  Project ( ANR-09-RISK-02 ,  J. C. Komorowski ). JCK is grateful for valuable and insightful discussions with F. Beauducel. We gratefully acknowledge the original intellectual stimulation and advice of  Dr Gordon Woo , who encouraged us to undertake this study.
Experimental set-up, digitization, labeling Spectra were extracted at the temporal midpoint between each fricative's acoustic onset and offset after applying a 256 point discrete Fourier transform with a 40 Hz frequency resolution, 5 ms Blackman window, and a frame shift of 5 ms to the target words using the Emu Speech Database system  (Harrington, 2010). The subsequent parameterization of these data involved the data reduction of each spectrum (at the sibilant's acoustic temporal midpoint in all cases) to a set of mel-scaled coefficients using the discrete cosine transformation. More specifically, for an N-point mel-scaled spectrum, x(n), extending in frequency from n = 0 to N -1 points over the frequency range of 500-3500 Hz, the mth DCT-coefficient C m (m = 0, 1, 2) was calculated with the formula in (  1 ) C m = 2k m N N -1 n = 0 x(n) cos (2n + 1)mπ 2N (1) These three coefficients C m (m = 0, 1, 2) encode the mean, the slope, and curvature respectively of the signal (in this case of a given sibilant's mel-scaled spectrum extracted at its temporal midpoint) to which the DCT transformation was applied  (Harrington, 2010).
Since C 0 , which is proportional to the dBmean across the entire spectrum, is largely irrelevant for the /s-/-distinction, only C 1 and C 2 (the spectral slope and curvature) were used for further quantification. We quantified the degree of neutralization of the /s-/distinction by calculating the Euclidean distances, E s and E , in the C 1 × C 2 space separately for each sibilant in the database to the Standard German speakers' /s/-centroid and to the Standard German speakers' / /-centroid, respectively. These two centroids are the positions in the C 1 × C 2 space averaged across all Standard German speakers' /s/-tokens and all Standard German speakers' / /-tokens respectively that occurred in the words from the reading condition. We then calculated for each sibilant its log-Euclidean distance ratio d sib , from (2): d sib = log (E s /E ) = log (E s ) -log (E ) ( 2 ) Thus, there is one d sib value per sibilant which is a relative measure: greater positive values denote a closer distance of a given sibilant to the / /-centroid; greater negative values are associated with distances closer to the /s/-centroid; and a value of zero on d sib denotes that a given sibilant is equidistant in the C 1 × C 2 space between the /s/ and / /-centroids (e.
ACKNOWLEDGMENTS This research was supported by an  European Research Council  grant number  295573  " Sound change and the acquisition of speech " ( 2012-2017 ) to  Jonathan Harrington. The authors thank  Ulrich Reubold  for help with creating the stimuli,  Lena Ehlermann  and  Elena Maslow  for recruiting  Standard German  subjects and running the experiments with them, and  Nikola Koczuba ,  Carolin Sabath , and  Rebecca Stegmaier  for help with segmenting and labeling the data.
Signals from the sensor were analyzed by Dakel XEDO AE 4. 0 in 80-400 kHz frequency range. Measured dataset was stored on the hard drive of a standard PC and analyzed by Minitab statistic so ware. Using so ware Daeshow are visualised acoustic emission events corresponding with synergy of corrosion and stress loading. The sample of typical AE event shows Fig. 4. The AE event consists of many frequency components. The higher frequency components will attenuate more quickly as the wave travels and can only be detected close to the source. Background noise such as that from external sources will be lower frequency. Therefore, the resonant frequency of the sensor is chosen to give maximum sensitivity without background noise. The piezoelectric material in the acoustic emission sensor transforms the signal to voltage.
As a fi rst analyse was performed a normality test for data distribution evaluation. This analysis is shown on following Fig. 5. Tab. I shows the main values for statistic analysis gained from normality test. For determination of the type of measured values distribution is the p-value signifi cant. For normal distribution it must be less than 0. 05. It was found that the distribution of AE amplitudes shows normal distribution therefore is possible to use control charts for other analysis. Fig. 6  shows control charts for mean and standard deviation from 200 amplitudes from AE events. Subgroup size is 10 points. The measurement has been performed in 20 days, each day was ten values recorded and continuously transformed into control charts and visualised. The central lines and control limits (UCL and LCL) are determined experimentally.
Acknowledgement The research has been supported by the project  TP 5/2013  " Application of non-destructive methods of technical diagnostics in agricultural technology " fi nanced by  IGA AF MENDELU. Preparation and implementation of this article is supported also by resources of the project  CZ. 1. 07/2. 3. 00/20. 005 "The excellence of Ph. D. studies at  FA MENDELU  for the following scientifi c European career".
iLOVECLIM The iLOVECLIM model is a code fork of the LOVECLIM model in its version 1. 2, as presented in  Goosse et al. (2010). It is identical to the latter with respect to its base components: Atmosphere, Ocean and Vegetation (AOV). It has been modified in a number of aspects to include oxygen water isotopes  (Roche, 2013)  and an interactive icesheet model  (Roche et al. , 2013). The general goal of the new developments within iLOVECLIM is to include the suite of processes needed for climate simulations on the Milankovic timescale. We summarize in the following the main characteristics of the AOV components as described in  Roche et al. (2007)  and  Goosse et al. (2010). The following paragraph is taken from  Roche et al. (2013). "The atmospheric component ECBilt was developped at the Dutch Royal Meteorological Institute (KNMI)  (Opsteegh et al.
Carbon cycle in the terrestrial biosphere The VECODE terrestrial biosphere model  (Brovkin et al. , 1997)  was already included in iLOVECLIM  (Goosse et al. , 2010). The model simulates two types of plants: trees and grass, as well as desert. The plants are divided into 4 compartments that exchange carbon: leaves, wood, litter and soil. Photosynthesis depends on the local climate (precipitation and temperature) and on the atmospheric CO 2 (CO 2 fertilization). We have added the isotopes of carbon to this pre-existing version of VECODE in every carbon compartment as was done in CLIMBER-2.
Conclusions We have described the implementation of a carbon cycle module in the iLOVECLIM model, including the carbon isotopes 13 C and 14 C. Comparison with modern data show that the model performs well for the main carbon cycle variables, and reproduce the most important features of the different water masses. In particular, the good representation of the 13 C and 14 C in the ocean interior paves the way for past studies for which they represent most of the available data. Therefore the iLOVECLIM model with the carbon cycle is well suited for long term simulations of a few thousand years in the past but also in the future. Some improvements will be considered in future work, such as the inclusion of iron and silicate, a better parameterization of the O 2 air-sea exchange with wind and better parameterization of the remineralization profile.
Discussion Paper | Discussion Paper | Discussion Paper | Framework Programme (FP7/2007-2013), ERC grant agreement number 247220, project "Seachange". This is a contribution to the ACCLIMATE ERC project; the research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 339108. Discussion Paper | Discussion Paper | Discussion Paper | Brovkin, V. , Ganopolski, A. , Archer, D. , and Rahmstorf, S. : Lowering of glacial atmospheric CO 2 in response to changes in oceanic circulation and marine biogeochemistry, Paleoceanography, 22, PA4202, doi:10. 1029/2006PA001380, 2007. 3940, 3942, 3944 Campin, J. M. and Goosse, H. : A parameterization of density driven downsloping flow for coarse resolution model in z-coordinate, Tellus.
Code availability The iLOVECLIM source code is based on the LOVECLIM model version 1. 2 whose code is accessible at http://www. elic. ucl. ac. be/modx/elic/index. php?id=289. The developments on the iLOVECLIM source code are hosted at https://forge. ipsl. jussieu. fr/ ludus but are not publicly available due copyright restrictions. Access can be granted on demand by request to D. M. Roche (didier. roche@lsce. ipsl. fr).