dataesr/scientific-paragraphs-categorization huggingface dataset:
The main advantage of this approach is that it does not compromise the precision of the data. It just removes statements. The Semantic Web is governed by the Open World Assumption, which states that the absence of a statement implies neither its truth nor its falsehood. Thus, the removal of a statement does not influence the correctness of the data. It does influence its completeness, though. As always, watermarking remains a trade-off between the quality of the data and the ability to prove provenance. We show in theoretical analyses that only a few hundred facts have to be removed in order to protect the KB effectively from plagiarism. If this number is slightly increased, then the method can work even if only part of the KB is plagiarized  [19]. Further information on watermarking KBs can be found on the Web page of our project 9.
Classification as a core for the purpose of this study then involves the following additional criteria: (i) detection significance (as defined above) greater than or equal to 5. 0 in the column density map; (ii) detection significance greater than or equal to 5. 0 in at least two wavebands between 160 µm and 500 µm; (iii) detection significance less than 5. 0 for the 70 µm band and no visible signature on the 70 µm image, in order to exclude protostellar cores, i. e. , those cores which contain a protostellar object; (iv) ellipticity less than 2. 0, as measured by getsources; (v) source not spatially coincident with a known galaxy, based on comparison with the NASA Extragalactic Database. This procedure resulted in a total of 496 cores over the observed 2 •. 2 × 2 •. 2 region. The total mass, 88 M⊙, of the detected cores represents approximately 4% of the mass of the L1495 cloud, estimated to be 1500-2700 M⊙  (Kramer & Winnewisser 1991).
We therefore evaluate the uncertainties using a Monte Carlo technique in which we repeat the estimation procedure after adding a series of samples of random noise to the observational data and examine the effect on the estimated parameters. We have also implemented an alternate version of COR-EFIT, referred to as "COREFIT-PH," in which the dust temperature profile is based on a radiative transfer model, PHAETHON  (Stamatellos & Whitworth 2003) , rather than estimating it from the observations. In this model, the radial density profile has the same form as for the standard COREFIT (Eq. (  2 )) but with the index, α, fixed at 2. The temperature profile is assumed to be determined entirely by the heating of dust by the external ISRF; the latter is modeled locally as a scaled version of the standard ISRF  (Stamatellos, Whitworth & Ward-Thompson 2007 ) using a scaling factor, χISRF, which represents an additional variable in the maximum likelihood solution.
9 ± 0. 3 K. Based on the results of testing with synthetic data, this difference seems too large to be explained by systematic errors associated with dust grain models, although we cannot rule out that possibility. One could also question whether our χISRF values are spuriously low. We do, in fact, find that by forcing the latter parameter to a somewhat larger value (0. 5), the median ∆T can be reduced to zero with only a modest increase in the reduced chi squared, χ 2 ν (0. 85 as opposed to 0. 83 for the best fit). The observations are completely inconsistent with χISRF = 1. 0, however. As an additional test, we can take the COREFIT estimate of the radial density distribution for each core and use the standalone PHAETHON code to predict the central temperature for any assumed value of χISRF.
How do the COREFIT estimates of temperature and mass compare with the preliminary values estimated from the getsources SEDs? In the case of temperature, the relevant comparison is between the SED-derived value and the spatially averaged COREFIT value; the data in Table  3  then give a mean "COREFIT minus SED" difference of -0. 2 K, with a standard deviation of 1. 1 for individual cores. The temperature estimates are thus consistent. With regard to mass, Fig. 5  shows that SED fitting under the isothermal assumption yields masses that are systematically smaller than the COREFIT values; the mean ratio of COREFIT mass to SED-based mass is 1. 5, with a standard deviation of 1. 0 in individual cases. Since the internal temperature gradient increases with the core mass, one might expect that the correction factor for SED-derived masses would increase with mass, although Fig.
CORE STABILITY Assessments of core stability are frequently made using SED-based estimates of core mass and temperature and observed source size, assuming that cores are isothermal and can be described as Bonnor-Ebert spheres  (Lada et al. 2008). Using the SED-based data in Table  3  in conjunction with the getsources estimates of core size, we thereby find that the estimated core mass exceeds the Bonnor-Ebert critical mass for 10 of our 20 cores, suggesting that half of our cores are unstable to gravitational collapse. Our COREFIT parameter estimates enable us to make a more detailed assessment of core stability based on a comparison with the results of hydrostatic model calculations that take account of the non-isothermal nature of the cores. This is facilitated by the modified Bonnor-Ebert (MBE) sphere models of Sipilä, Harju & Juvela (2011).
Adopting their model curves, based on the Li & Draine (2001) grains which best reproduce our estimated core temperatures, the locus of critical non-isothermal models on a density versus mass plot is shown by the solid line on Fig. 10. Also plotted In this variant of the estimation procedure, the dust temperature profile is modeled using a radiative transfer code (PHAETHON) instead of estimating it from the observations. on this figure, for comparison, are the COREFIT estimates of those quantities. The seven points to the right of this curve represent cores that we would consider to be gravitationally unstable based on the modified Bonnor-Ebert models. Although this is somewhat less than the 10 that were classified as unstable based on the SED fits, the difference is probably not significant given that several points on the plot lie close to the "stability" line.
COMPARISON WITH PREVIOUS OBSERVATIONS The deduced physical properties of our cores may be compared with previously published spectral line data in H 13 CO + and N2H + , both of which are known to be good tracers of high density gas (n(H2) > ∼ 10 5 cm -3 ). Of our 20 cores, we find accompanying observations for 10 in H 13 CO +  (Onishi et al. 2002)  and seven in N2H +  (Hacar et al. 2013). The relevant parameters are given in Table  4. Considering first the H 13 CO + data, comparison of observed peak locations with dust continuum source positions from COREFIT shows a distinct lack of detailed correspondence. This behaviour is apparent in Fig. 1  and from Table 4 which includes the angular distance (labeled as "Offset" in the table) between each of the H 13 CO + source locations and the corresponding dust continuum core location.
ACKNOWLEDGMENTS We thank  T. Velusamy ,  D. Li , and  P. Goldsmith  for many helpful discussions during the early development of the COREFIT algorithm. We also thank the reviewer for many helpful comments. SPIRE has been developed by a consortium of institutes led by  Cardiff Univ. (UK)  and including:  Univ. Lethbridge (Canada) ;  NAOC (China) ;  CEA ,  LAM (France) ;  IFSI ,  Univ. Padua (Italy) ;  IAC (Spain) ;  Stockholm Observatory (Sweden) ;  Imperial College London ,  RAL ,  UCL-MSSL ,  UKATC ,  Univ. Sussex (UK) ; and  Caltech ,  JPL ,  NHSC ,  Univ. Colorado (USA). This development has been supported by national funding agencies:  CSA (Canada) ;  NAOC (China) ;  CEA ,  CNES ,  CNRS (France) ;  ASI (Italy) ;  MCINN (Spain) ;  SNSB (Sweden) ;  STFC ,  UKSA (UK) ; and  NASA (USA). PACS has been developed by a consortium of institutes led by  MPE (Germany)  and including  UVIE (Austria) ;  KU Leuven, CSL, IMEC (Belgium) ;  CEA ,  LAM (France) ;  MPIA (Germany) ;  INAFIFSI/ OAA/OAP/OAT, LENS, SISSA (Italy) ;  IAC (Spain).
PhotoSwipe. Quizz As indicated by its name, this is a fork of PhotoSwipe  8. This module allows to generate automatically a quizz that will ask the player questions the answer of which are an image (see Figure  2 ). It uses a JSON formalism to generate the quizz based on a text file and various image files. The system integrates a very rough traces gathering module. As we plan to interconnect with a real interaction traces repository, we will need to make adaptations to this module. PhotoSwipe. Quizz is used to carry out the observation task and modifies local storage variables to enable browserquest to detect completion of the task (cf. dotted arrows to NPC 1 & 2). To help the player complete the task, the system provides a feedback giving them hints leading to the solution  9  whenever an erroneous solution is selected.
This would be problematic in that the task actually creates the possibility for players to be physically able to communicate. This makes the actual city locations part of the environment of the game in exactly the same way as BrowserQuest. The location of the "observation task" initiating our scenario is in effect an entry point to our game through the presence of a QR code that can be scanned by any passer-by or bystander curious about players present in the premises. This exemplifies the concept of social expansion inherent to PG. This underlines the issue of how to favor entering a mixed reality game such as this one and the consequences of design decisions in term of guild constitution and community engagement. In our future works, we plan to conduct several experiments with people of different backgrounds to study the implications of these decisions on their progress in the learning scenario and on the level of knowledge acquired in the archaeological domain at the completion of the game.
Acknowledgements. This work was supported by the  LABEX IMU  ( ANR-10-LABX-0088 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). We would like to thank our archaeologist colleagues for their participation in this project and involvement in the various field tests performed. Additionnally, the pictures in Figures 2,3 and Figure 4 were taken respectively by  Marie-Odile Rousset ,  Myriam Fincker  and  Alexandre Rabot. The archaeological content in the activities of Figures 2 and4 were respectively designed by  Jean-Claude Decourt & Jean-Pierre Girard , and by  Jean-Pierre Girard & Claudine Maréchal. Myriam Fincker ,  Jean-Charles Moretti ,  Anne Pariente  and  Emmanuelle Vila  contributed to the content of the scenario.
Background: DNA repeats, such as transposable elements, minisatellites and palindromic sequences, are abundant in sequences and have been shown to have significant and functional roles in the evolution of the host genomes. In a previous study, we introduced the concept of a repeat DNA module, a flexible motif present in at least two occurences in the sequences. This concept was embedded into ModuleOrganizer, a tool allowing the detection of repeat modules in a set of sequences. However, its implementation remains difficult for larger sequences. Results: Here we present Visual ModuleOrganizer, a Java graphical interface that enables a new and optimized version of the ModuleOrganizer tool. To implement this version, it was recoded in C++ with compressed suffix tree data structures. This leads to less memory usage (at least 120-fold decrease in average) and decreases by at least four the computation time during the module detection process in large sequences.
Visual ModuleOrganizer interface allows users to easily choose ModuleOrganizer parameters and to graphically display the results. Moreover, Visual ModuleOrganizer dynamically handles graphical results through four main parameters: gene annotations, overlapping modules with known annotations, location of the module in a minimal number of sequences, and the minimal length of the modules. As a case study, the analysis of FoldBack4 sequences clearly demonstrated that our tools can be extended to comparative and evolutionary analyses of any repeat sequence elements in a set of genomic sequences. With the increasing number of sequences available in public databases, it is now possible to perform comparative analyses of repeated DNA modules in a graphic and friendly manner within a reasonable time period.
Except for phylogeny analysis, there are few bioinformatic tools (VISTA  [11] , GATA  [12] , GraphDNA  [13] , Recon  [14]  and DomainOrganizer  [15] ) that facilitate the analysis of relationships and variations between the copies of a given family of repeats  [16, 17]. In a previous study, we developped ModuleOrganizer that indexed all maximal repeats (MR) of sequences via a suffix tree in order to detect conserved modules within the repeated sequences  [18]. Indeed, the algorithm recursively associates two MR if the spacer between them is smaller than the size of the largest maximal repeat and if http://www. mobilednajournal. com/content/5/1/9 the edit distance between spacers of all occurrences is not greater than the size of the smallest maximal repeat. The MR association yields to the formation of modules under restrictions defined by the user such as the minimal length of the module (MinSizeModule) and the minimal number sequences (MinSequences)  [18].
Although the previous version of the ModuleOrganizer tool can efficiently detect repeated modules within sequences of smaller sizes (<100 Kbp), its implementation with larger sequences remains problematic. This is often because memory usage becomes bottleneck. Since Mod-uleOrganizer command lines are the limiting factors for its use, a graphical interface should be useful for launching and analyzing ModuleOrganizer results. To overcome these difficulties, we propose an optimized version of the ModuleOrganizer software with its corresponding graphical interface called Visual ModuleOrganizer.
Implementation Since the previous algorithm was written in C language, the new version of ModuleOrganizer  [18]  was first completely recoded in C++ which drives better memory management. Then, as using a standard suffix tree  [19]  to compute and store all maximal repeats (MR) leads to a high memory usage during the process of ModuleOrganizer, a data structure based on the compressed suffix tree Välimäki  [20]  was applied to the new algorithm version. Therefore, the final re-implementation of the new version contains options of the previous algorithm such as 'search of palindromic modules', 'search of exact repeats', 'search of truncated modules', 'creation of a classification tree file', 'search in a minimal number of sequences' and 'association distance between MR'. In addition, new options were also developed including: the '-limit integer' option which stops the ModuleOrganizer run process after the detection of integer modules, and the '-f MinSizeMR' option that selects MR of equal or greater size than MinSizeMR bp before the formation of the module.
This option decreases the ModuleOrganizer run time but have little effects on the sensitivity/specificity of module detection (data not shown). Finally, the new option '-p m' prints each m minute the approximative progress of the software. As the input, ModuleOrganizer needs FASTA file containing the nucleotide sequence(s) (input-file1, mandatory). It can also use a FASTA reference (input-file2, optional) file. This reference sequence file must contain a unique sequence. The '-REF input-file2' option limits the module detection to MR present in both input and reference sequences. ModuleOrganizer creates one mandatory output file (named 'Module File') that contains the list of detected modules in a tabular format as follows: where START1, START2, START3 and START4 (END1, END2, END3 and END4) correspond to the start (end) location of modules within the sequences.
x and y represent the identification number of modules. nb_sequence is the number of sequences in which the module is present and orientation gives the orientation of the module in sequences (letter 'd' for the direct strand and letter 'c' for the reverse strand). If the input file contains three or more sequences, Mod-uleOrganizer creates an Unweighted Pair Group Method with Arithmetic Mean (UPGMA)-based tree from the matrix of presence/absence of modules in sequences. This tree is then saved as a second output file, with 'upgma' extension name. The '-MR' option writes out (in a tabular format) the list of detected MR (see Additional file 1). The option '-SVG' allows the creation of an optional output file in Scalar Vector Graphics (SVG) format, a XML-based vector language that grants modifications with any Scalar Vector Graphics-enabled image processing tool.
Data-processing improvements During the run process of the previous algorithm, positions of the selected MR are first copied in the computer memory space before the building of all potential modules. By consequence, this method might lead to high memory usage and therefore slows down the detection process. In order to improve the efficiency of ModuleOrganizer memory usage, the new algorithm directly reads the MR positions through the compressed suffix tree data structure. As shown in Figure  1 , this change (from suffix tree to compressed suffix tree) dramatically decreases the memory usage of ModuleOrganizer, particulary for large sequences. Indeed, in the previous and the new version, artificial random sequences from 10 to 240 Kbp exhibit a memory usage of 29 to 5326 Mb and 13 to 25 Mb, respectively.
The visual ModuleOrganizer tool parameters Through Visual ModuleOrganizer, ModuleOrganizer parameters (Area 1 in Figure  2 ) can be used in two different ways: (i) ModuleOrganizer is launched by selecting 'No' on the 'Using Previous Results' button; or (ii) previous results from ModuleOrganizer are displayed by selecting 'Yes' with the same button. In the first case, the two first mandatory parameters are the 'Input: FASTA File' button, which selects the input file, and the 'Output: Module File', in which ModuleOrganizer results are written and displayed by the Visual Module-Organizer interface. The third mandatory parameter (e. g. MinSizeModule) is the minimal size of the module, which is chosen from a list (Figure  2 ). Therefore, a module is detected and selected by the algorithm if its size is equal or longer than MinSizeModule.
By default (with ' Automatic' size), ModuleOrganizer proposes to set MinSizeModule to the minimal value of x such that it does not exists a word of size x in the sequence(s). In addition to the three mandatory parameters, Visual ModuleOrganizer has the seven optional parameters. The three first parameters ('Palindromic modules', 'Truncated modules', and 'Exact repeats') provide binary choices and were described in the previous version  [18]. Four others were computed in the new version of ModuleOrganizer. 'Minimal number of sequences' (e. g. MinSequences) and 'Maximal number of modules' (e. g. MaxModules) options require an integer value x, and limit the detection of modules in at least x sequences and to a maximal number of modules x in the whole set of sequences, respectively. The option 'Module located in a reference' refers to detected modules located in the reference sequence as well as in the mandatory 'Input FASTA File' sequences.
The last option, labeled ' Annotations', opens a new frame (see upper right frame in Figure  2 ) that allows the user to add genomic annotations from Genbank  [21] , EMBL  [22] , AB-BLAST  [23] , or NCBI-BLAST  [24]  and/or repeat annotations from RepeatMasker  [8] , Censor  [9] , and Repet  [25]  formats within the graphical interface. It is noteworthy that each annotation should be added independently for each sequence. Interestingly, the user can add its own annotation in a tabular format. In the second case, when the button 'Yes' from 'Using Previous Results' is selected (see upper left frame in Figure  2 ), ModuleOrganizer parameters become invisible. They are replaced by a mandatory parameter 'Input: Mod-uleOrganizer File' and two optional parameters labeled 'Option: Tree file' from an upgma-based tree and ' Annotations' as described above.
'Draw Modules present in at least M Sequences' and 'Draw Modules by Size' sliders display modules located within a minimal number of sequences and with a minimal length, respectively. Minimun and maximum values of the two sliders are automatically taken from the ModuleOrganizer process with the 'M Sequences' ranges from 1 to total number of sequences and the 'Size' ranges from minimal and maximal length of the detected modules. Under the graph, four buttons facilitate the modification of the graphical view: 'Zoom in' and 'Zoom out' buttons increase and decrease by a factor 2 the graph width, respectively. 'Global view' button adjusts the graph width (including the graph elements) according to the largest sequence. The last button, labeled 'Enlarge Graph', (or 'Reduce Graph' after a click on it) removes the parameter area (Figure  2 , Area 1) (or displays it) from the interface.
With a MinSizeModule settled to 25 bp, 'Palindromic modules' and 'Truncated modules' options selected, the Module-Organizer algorithm discovered 23 modules (Figure  3A ). Palindromic structures of the FB4 sequences are described by modules 1-5 that should correspond to Terminal Inverted Repeat (TIR). Internal sequences are mainly composed of the modules 8-10 which are repeated in tandem, looking like minisatellites. Those are often present in the internal sequence of non-autonomous transposable elements  [1, 28]. According to the module composition, the upgma-based tree clusters the FB4 sequences in 4 distinct groups: Group1 = FB4_3, FB4_8, and FB4_4; Group2 = FB4_1, FB4_9, and FB4_5; Group 3 = FB4_10 and FB4_11; Group4 = FB4_2 and FB4_7, allowing interand intra-groups comparison of the detected modules.
Indeed, the reverse occurrence of modules 3 and 4 were deleted in FB4_2 and FB4_7 (from Group4) and reverse modules 2-5 were absent in FB4_10 and FB4_11 (from Group3). These findings clearly suggest that partial deletions of these palindromic structures would impair the transposition of these FB4 sequences. Through the Visual ModuleOrganizer interface, the 'Draw Modules present in at least M Sequences' slider was settled to 9, allowing only the display of modules present in at least nine of the ten FB4 sequences. As a result, the palindromic modules (module 1 to 5) and the module 9 from the internal sequence are still displayed (Figure  3B ), indicating that those palindromic modules are evolutionary conserved within the FB4 family and might be essential for the transposition. This observation also puts forward that the 'Draw Modules present in at least M Sequences' option can be useful for comparative analysis (insertions, deletions, repetitions, rearrangements,.
) of modules in a given set of a sequence family. In a similar way, when the 'Draw Modules by Size' slider is set to 58 (e. g the displayed modules are equal or longer than 58 bp), only modules 1, 4, 5 and 15 are displayed in Figure  3C , therefore allowing the identification of large conserved modules. Altogether, the case study of FB4 spotlights the ability of Visual ModuleOrganizer for comparative analysis of highly complex and variant repeat structures in a given set of sequences. These complex repeat features include biological known repeat structures (palindromes, minisatellites,. ) that are usually not observable by standard analysis tools such as VISTA  [11] , GATA  [12] , GraphDNA  [13] , and Recon  [14]. Indeed, it has been shown that these software which combine multiple alignment with http://www.