dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation data sets and other data sets The GFED3. For convenience of description, emission sources of the former three types of fire are tentatively referred to as natural sources (that ORCHIDEE-SPITFIRE simulates explicitly), and those of the latter three types as anthropogenic sources (that ORCHIDEE does not explicitly include, although it is able to capture part of the deforestation fire emissions as explained in Sect. 2. 3). Note that the grouping of different emission sources in GFED3. 1 data does not necessarily reflect the exact nature of different fire types. For example, peat fires in tropics are mainly due to intentional drainage followed by burning to remove a (logged) forest (thus anthropogenic, e. g. Marlier et al. , 2015) , while in northern high-latitude regions, peatland fires might be due to drought (thus natural, e.
Comparison of simulated carbon emissions with GFED3. 1 at the global scale The simulated mean annual global fire carbon emissions for 1997-2009 are 2. 1 Pg C yr -1 , close to the estimate of 2. 0 Pg C yr -1 by GFED3. 1 data, where emissions from both natural and anthropogenic sources are included (Fig. 3 ), and higher than the 1. 5 Pg C yr -1 when peat, deforestation and agricultural fires are excluded from GFED3. 1. The model also simulates lower IAV of emissions than GFED3. 1, giving a coefficient of variation of 0. 05, compared to 0. 18 for the GFED3. 1 data (0. 15 when only natural sources are included in GFED3. 1). The interannual variability of fire carbon emissions is known to be partially decoupled from that of burned area (van der  Werf et al. , 2006) , mainly because emission variability is driven by forest fires with higher fuel consumption, whereas burned area variability is driven by savanna fires with relatively large burned fraction but low fuel consumption.
At the global scale, the IAV of fire carbon emissions is simulated to be closely related to that of burned area (Fig. S4 , giving a correlation coefficient of 0. 88 over 1997-2009 -all data detrended). In contrast, the correlation coefficient between GFED3. 1 natural source emissions and burned area is 0. 52 over the same period (0. 04 when emissions from both natural and anthropogenic sources are included), i. e. smaller than ORCHIDEE-SPITFIRE. Thus the IAV of carbon emissions is more strongly coupled to that of burned area in OR-CHIDEE than in GFED3. 1, because emissions are dominated by burning of litter (from grassland, savanna and forest) and are less driven by forest fires that involve a large amount of live biomass burning.
Comparison of simulated carbon emissions with GFED3. 1 for different regions Annual fire carbon emissions simulated by ORCHIDEE-SPITFIRE are compared with GFED3. 1 data for 1997-2009 for different regions in Fig. 4  (see figure caption for expansion of GFED region abbreviations and Fig. S5  for region distribution). The three regions with the most frequent fires, Northern Hemisphere Africa (NHAF), Southern Hemisphere Africa (SHAF) and Australia (AUST), have total fire emissions of 1. 17 Pg C yr -1 and contribute 59 % of the global total emissions in GFED3. 1. In ORCHIDEE, annual emissions are 1. 18 Pg C yr -1 for these three regions, an overestimation in NHAF being partly compensated for by underestimation in SHAF. The GFED3. 1 data have very low emissions in temperate North America (TENA), the Middle East (MIDE), central Asia (CEAS) and Europe (EURO) (50 Tg C yr -1 in total for the three regions; 2.
3, 1 -CV); and --, moderately underestimated, if the ratio falls in (0. 1, 0. 3). The CV for annual emissions and burned area by GFED3. 1 data was calculated using the annual time series. Total fuel consumption data for GFED3. 1 were obtained from  ORCHIDEE-SPITFIRE simulates much higher emissions (294 Tg C yr -1 ; 14 % of the global total), possibly because forest fire control measures  (Fernandes et al. , 2013; Keeley et al. , 1999)  and forest management in temperate countries  (Fang et al. , 2001; Luyssaert et al. , 2010)  are not modelled; this leads to a higher burned area and/or higher fuel load in the model. The overestimation of emissions in these three regions is partly driven by the overestimation of burned area (annual burned area of 70. 2 Mha yr -1 in the model versus 10. 1 Mha yr -1 in GFED3.
Fire fuel consumption and latitudinal pattern of emissions Simulated fuel consumption (g C per m 2 of area burned) in fire is compared to GFED3. 1 data in Fig. 5. Both ORCHIDEE-SPITFIRE and GFED3. 1 show a large amount of fuel consumption in boreal regions, but fuel consumption in the Russian boreal forest is smaller in the model than GFED3. 1 (simulated 400-2000 g C m -2 compared to 2000-5000 g C m -2 in GFED3. 1). The model also fails to capture the high fire fuel consumption (5-20 kg C m -2 ) at the southern edge of the Amazonian rainforest and in Southeast Asia, which are associated with deforestation fires or peat fires (see also  Figs. 6 and 13 in van der Werf et al. , 2010). The fire fuel consumptions for savannas and woodland savannas in Africa and Australia are higher in the model than GFED3.
9 Pg C (Le  Quéré et al. , 2013). The cumulative land sink in fireOFF is 127. 2 Pg C, suggesting a cumulative sink reduction of 17. 6 Pg C by fire since 1959. The correlation coefficient between detrended time series of NBP by the fireON simulation and the residual sink is 0. 59, indicating that the model is moderately successful at capturing the IAV of the carbon sink by the terrestrial ecosystem. Prentice et al. (2011)  pointed out that fire emissions account for one-third and one-fifth of the IAV of the 1997-2005 global carbon balance as indicated by atmospheric inversions, when emissions were from the GFED3. 1 data and simulated by the LPX vegetation model, respectively. In our study, fire carbon emissions explained 20 % of the IAV of simulated NBP (which is the R 2 of the linear regression of detrended annual NBP against simulated carbon emission), congruent with their results.
Summary and conclusions In this study, we used the ORCHIDEE land surface model with the recently integrated SPITFIRE model to estimate the role of fires in the terrestrial carbon balance for the twentieth century. The simulated global fire carbon emissions for 1997-2009 are 2. 1 Pg C yr -1 , close to the 2. 0 Pg C yr -1 as estimated by the GFED3. 1 data (when all types of fires are included), owing to error compensation among different regions in the model. Fire carbon emissions are mainly underestimated in Southern Hemisphere tropical regions and this error is compensated for by an overestimation in temperate ecosystems. The regional emission errors are found to be coincident with the errors in simulated burned areas, with the exception that fire fuel consumption is underestimated in regions featuring peatland or deforestation fires such as equatorial Asia, because these fires are not explicitly included in the model.
). This theory of human motivation is a natural and cultural hypothesis, which is corroborated by Umwelt  [14] , Activity  [18]  and Semiotic  [13]  pragmatic theories. These life and logical sciences are components of the Biosemiotics interdisciplinary research  [19] , which was introduced before the advent of Internet as the "Semiotic Web"  [20]. By comparison, Semantic Web is the dyadic combination of form and sense of the linguistic Sign  [21] , taken as a signifier (form) and signified (sense). It is rational. Semiotic Web is more generic and living. It complements the Semantic Web (form and sense) with the referents (content) that are observed data (interpretations) georeferenced in a 3D information world (Immersive Web) as Web Services by subjects pertaining to communities of practice (Social Web 2.
0). This makes our Sign management ecosystem a tetrahedron model (Figure  6 ) that is more involved in concrete life with end-users on a specific territory such as Reunion Island. The Web of Signs combines: 1. The Web of Data and Objects, i. e. the flow of raw and digital contents produced by specialists (teachers) and transmitted by engineers in databases and knowledge bases in the frame of an Information System (one-way flow), but progressively becoming interoperable through Web services with other Information Systems, 2. The Web of Subjects, i. e. a bidirectional communication platform between users (teachers and learners) using different e-services within a community of practice to exchange interpretations of data and objects, and negotiate their value, 3. The Web of Information that is geo localized in attractive virtual worlds representing the real landscape (metaverses), and accessible at any time, anywhere, on any devices (mobiquity).
And some people do the activity better than others. They are called the experts. As such, knowhow is closer to data (Praxis) and information (Techne) than to knowledge (Scientia). Finally, know-how and know-that or know-what are different categories of knowledge and should not be conflated  [35]. Knowledge synthesizes what makes sense in the head of skilled persons for doing well the tasks of their activity. Starting from these differences of interpretations about the term of knowledge, and considering the domain of activity that we want to deal with, i. e. education with ICT, we prefer to focus on managing interpretations, and firstly the good ones from professors. Sign management manages live knowledge, i. e. subjective objects found in interpretations of real subjects on the scene (live performances) rather than objective entities found in publications (bookish knowledge).
Conclusion As computer scientists and knowledge engineers, we want to design a new Iterative Sign Base System: ISBS = IKBS + @-MUSE. It will be the kernel of our Information Service for defining ontologies and terms, describing pieces work, classifying them with machine learning techniques, and identifying the name through a multimedia interactive questionnaire. The objective of such a tool is to become an instrument in users' hands for monitoring biodiversity in the fields with the National Park of Reunion Island  [36] , and music at home with the Regional Music Conservatory  [37]. For achieving this, we stressed on the importance of reducing the gap between interpretations of teachers (specialists) and learners (amateurs) to get the right identification name and then access to information in databases, or to get the correct gesture that gives the right sound for playing music.
We previously described a method that enables us to differentially tag 5' ends of primary and processed RNAs  [Fouquier d'Hérouël et al. , 2011]. In the present work, we have coupled this method to RNA-seq, yielding novel insights into the bacterial transcriptome landscape where the primary and the processed RNAs are unveiled within a single experiment; we call the totality of primary and processed RNAs the ppRNome. We have sorted transcription start sites (TSSs) and processing sites (PSSs) and validated the method by reproducing known results for E. coli. The presented data provide a first comprehensive transcriptional landscape of the human pathogen E. faecalis.
, 2011]. We have combined this differential 5' RNA end tagging with deep sequencing technologies and termed it "tagRNA-seq" to visualise the primary and processed transcripts of E. faecalis in a comprehensive manner (Figure  1  and "The ppRNome browser" website, see section "Visualisation of Results" in Materials and Methods). TagRNA-seq was performed on total RNAs extracted from bacteria grown in static (S) and respiratory (R) conditions, providing transcriptomes coined "St" and "Rt", respectively (Section S2, Table  S1 ). In parallel to these, and as control, three other RNA libraries from E. faecalis and one from E. coli were sequenced on different next generation sequencing platforms (See Materials and Methods and Section S2). In order to account for variations in total number of reads and to be able to compare experiments, RNA levels are reported normalised to the total number of reads mapped, as commonly done in RNA-seq  [Robinson and Oshlack, 2010].
, 2003, and references therein]. TagRNA-seq data enables us to map locations 1121951/-53 with high tag counts corresponding to PSSs (Tables SA, SE and the ppRNome browser). The RnpB upstream-most 5' end predicted in the Rfam database allocates a position at 1121944 in the chromosome  [Griffiths-Jones et al. , 2005] , a location spaced by 4 and 7 nt from the TSS and PSS we have mapped, respectively. Further experiments will be necessary to shed light on the details of the processed transcriptome and its complex organization. Nevertheless, to our knowledge, this is the first study mapping PSSs at a global scale in bacteria.
Antisense organisations are shown by vertical blue lines. ganisation also results from overlapping mR-NAs and may involve coding sequences as well as 5'-or 3'UTRs (e. g. [Nicolas et al. , 2012 , Rasmussen et al. , 2009 , Sharma et al. , 2010 , Toledo-Arana et al. , 2009 , Wurtzel et al. , 2012] ). For instance, the 5'UTR ef0282 (fabI ) overlaps the 5'UTR ef0283 (fab-F1 ); ORFs ef0479 and ef0480 are embedded in a long opposite transcript originating 3,000 bp upstream; and the transcript that contains ef0522 -ef0523 in an operon is antisense to a transcript carrying ef0524. Similar examples are observed on plasmids pTEF1 and pTEF2 (the ppRNome browser and Table  SF ). However, one of the most striking antisense organisation was found in the region spanning from ef2298 to ef2324 (Figure  4 ).
Alignment and coverage Reads were aligned to the E. faecalis v583 and E. coli K12 substrain MG1655 reference genomes (respectively GenBank Accession IDs [GenBank:AE016830. 1] (chromosome), [GenBank:AE016831. 1] (pTEF2), and [GenBank:AE016833. 1] (pTEF1) ; [Gen-Bank:U00096. 3] ) using Bowtie 1. 0. 0  [Langmead et al. , 2009]  with default options, but allowing for multiple matches (-a best command line option). The coverage is calculated by counting the number of reads mapped at each position on the genome for each strand. In the case of multiple matches, the number of matches correspondingly divides the contribution to the read count. In cases mentioned explicitly in the text where repeated regions are excluded from the analysis; those multiply matched reads are ignored in the count. Similarly, when rRNAs and tRNAs are excluded, we impose a zero coverage over the corresponding regions.
Acknowledgements We thank the eBIO computing platform of Orsay, particularly  C. Drevet  and  C. Toffano-Nioche , for hosting the "ppRNome browser". We thank  Ingemar Ernberg  at  Karolinska Institutet  for his hospitality by providing lab space and equipment for AdH, and  V. Cantoni  for the suggestion to use edge detection algorithms. We are grateful to the 'CPE team',  A. Gruss  and  D. Halpern  for comments and discussions, and  S. Gaubert ,  L. Girbal ,  T. Esquerré  and  M. Cocaign-Bousquet  for communicating results prior to publication. Many thanks to our colleagues,  P. Palcy ,  V. Bourgogne ,  N. Eberlin  and  P. Régent  for invaluable administrative, IT and equipmentrelated support. This research was supported by the  Swedish Science Council  through grant  621-2012-2982  (E. A), and by the  Academy of Finland  through  Finland Distinguished Professor programme  and the  Center of Excellence COIN (E.
We determine the spectrum and correlation functions of Liouville theory with a central charge less than (or equal) one. This completes the definition of Liouville theory for all complex values of the central charge. The spectrum is always spacelike, and there is no consistent timelike Liouville theory. We also study the non-analytic conformal field theories that exist at rational values of the central charge. Our claims are supported by numerical checks of crossing symmetry. We provide Python code for computing Virasoro conformal blocks, and correlation functions in Liouville theory and (generalized) minimal models.
With this hypothesis, correlation functions appear to be singular, but the singularities can be eliminated by a simple regularization. The crucial evidence that our ideas are correct will be provided by numerical checks of crossing symmetry. For that evidence to be acceptable, the corresponding computer code should be publicly available. One could even argue that the software which is used should be free and open source. So we wrote Python code in the IPython Notebook format, and released it on GitHub  [11]. In particular, the code for generating our data tables and plots can be found in the notebook "article support. ipynb", which has the same structure as the present article. Using our code, we checked crossing symmetry not only in Liouville theory with c ≤ 1, but also in Liouville theory with generic complex values of c, and in generalized minimal models.
Acknowledgments We wish to thank  Olivier Babelon ,  Marco Picco ,  Volker Schomerus , and particularly  Vladimir Belavin , for helpful comments on the draft of this article. We are grateful to  Teresa Bautista Solansa ,  Atish Dabholkar , and  Harold Erbin  for intense discussions. R. S. is grateful to  Jacopo Viti  for interesting discussions, and to  Vincent Degat  for help with a computer. We thank  Pasha Gavrylenko  for reporting a mistake in eq. (4. 6).
I. Introduction Fixed-precision floating-point ("FP") operations come with varying performance and accuracy guarantees. Basic operations such as multiplication are typically implemented in hardware and, being correctly rounded, provide perfect accuracy. So-called elementary functions like exp and log are provided in software through general-purpose mathematical libraries (libms). They are well-optimized for performance and either correctly rounded, as recommended by the IEEE754 Standard  [16] , or provided with maximum error not exceeding one unit in the last place. This work is concerned with special functions, i. e. , "functions which are widely used in scientific and technical applications, and of which many useful properties are known"  [12]. Some, like Bessel functions or the Gaussian error function, are present in some libms.
Most are provided by specialized libraries such as GSL or Root. Others are only available in computer algebra systems like Maple and Pari and have no efficient fixed-precision implementation. The NIST Digital Library of Mathematical Functions  [1]  contains a good overview of existing implementations. There is a huge gap in both performance and rigor between these implementations and state-of-the-art elementary functions. However, implementing or reimplementing special functions manually in the classical way is tedious and requires considerable expertise. And indeed, in many cases, publicly available implementations of a given special function all derive from a single source, typically Cephes  [23]. This article presents the first results of a project exploring the implementation of special functions through automatic code generation.
e. , 2 -53 ≤ ε ≤ 1. These restrictions can be loosened with some manual work so that implementations on the whole set of double precision numbers become feasible. Code generation for mathematical functions is not entirely a new idea. A powerful toolbox, including in particular Gappa  [8]  and Sollya  [6] , has been developed in recent years to help human developers with the most tedious steps of the implementation process. Building on these advances, several authors have considered code generation both for "flavors" of a fixed function and for "black-box" functions given as executable code  [19] ,  [9] ,  [3] ,  [18] ,  [4]. Independently, Beebe  [2]  produced a new, very extensive mathematical library with the help of semi-automatic code generation. We base our work on the black-box approach of  [19] , where the starting point is executable code able to evaluate f and its first few derivatives on intervals, up to any required precision.
II. Overall architecture This text describes both an approach to code generation for D-finite functions and experiments performed with a prototype implementation of this approach. Our prototype, named Frankenstein, is available from https://gforge. inria. fr/git/metalibm/frankenstein. git. It is based on modified versions of two existing software packages, Metalibm  [18] , written in Sollya  [6] , and NumGfun  [21] , written in Maple. The name should give a pretty accurate idea of how the combination is realized. Thus, not all design choices have the same significance: while some would survive a cleaner rewrite, others are only justified by the goal of producing a usable prototype out of two research-quality codes that had never been intended to work with each other. Here we focus on the former category, with brief mentions of more peculiar features of Frankenstein as necessary.
For the FP x just after the zero, ξ will fit on a small number of bits due to cancellation in ξ = xt k , hence FP Horner evaluation will behave correctly, too. The accuracy of evaluation can be checked statically using Gappa as explained below. Note, though, that the polynomial r passed to the code generation step then contains coefficients involving the Newtoniteration based representation of c. In contrast, in the usual case (when f has no zero in the domain), the coefficients directly come from the Remez-Stiefel minimax algorithm.
We therefore use a technique  [5]  based on lattice reduction which globally searches for a FP-valued-coefficients polynomial "close" to the original real-coefficients polynomial. 3) Starting with the target accuracy ε, the backend estimates suitable accuracies for the intermediate steps of a Horner evaluation of pk. This choice of accuracies determines the FP precisions-and, possibly, double-double or triple-double expansions-to be used in the different steps. The technique is described in  [10] ,  [19]. The backend then outputs C code for polynomial evaluation along with a Gappa  [8]  proof script. 4) Finally, the backends runs the proof script using Gappa, hence verifying the correctness of the accuracy estimates established in the previous step.
E. Evaluation-based vs. polynomial-based interfaces In a cleanly designed special function code generator, the backend would take the rough polynomial approximations produced by the frontend and proceed with refining them without ever returning back to the original function f. However, this would require the frontend to take into account the relative error due to replacing f by a polynomial, which requires some understanding of FP properties of the eventual implementation when f has zeros in the domain. For that reason and for reasons due to the reuse of existing code, Frankenstein works in a slightly different way. The backend binds a Sollya function object, corresponding to f , to the evaluator dedicated to tiny intervals provided by the frontend. This would in principle be enough for the backend to run, since the backend-in all steps, even its polynomial approximation ones-is purely based on (interval or point) evaluation.
In practice, this simple interface is not enough. The number of function evaluations is too large to allow for reasonable code generation performance. In addition, an evaluation-based interface makes it hard to capture non-local properties of the function f , such as monotonicity. We therefore enhanced Sollya to allow a function object, such as the one bound to the frontend's representation for f , to be annotated with an alternate approximate representation along with a bound on the approximation error. The approximation polynomials generated by the frontend provide such an approximate representation. When it needs to evaluate the function f at some point or over some interval, Sollya first tries to use the annotation. If this first try is inconclusive, for instance if the approximation error is too large in view of the evaluation precision, it falls back to the original evaluator bound to the frontend.