dataesr/scientific-paragraphs-categorization huggingface dataset:
In this case, the first choice (solution 0) is the most parsimonious. Solution 0 is always the one corresponding to assembling closest hits together. However, this solution may come to a fault in the case of multiple nested or duplicated TEs corresponding to the same reference element. For example, in Figure  2B , solution 1 is the most parsimonious, that is, the one that minimizes the reorganization of the copy compared to the reference element structure. If this option is not specified, the default choice consists of choosing solution 0. As many ambiguous cases can arise, the RepeatMasker block ID (column 14 of RepeatMasker. out file) is used when this option is activated. These IDs come from the ProcessRepeats script implemented in RepeatMasker, which makes educated guesses if any pair of fragments is derived from the same element or not.
--dry-run This option performs all operations, but reports no results except the log file with all operations performed. It is designed to be used in tests, particularly those determining the number of ambiguous situations to be resolved. Running the program with this option before the actual analysis allows estimation of the time required to complete an analysis with the --choice option because the number of ambiguous situations can be high, and manual choice is time-consuming if applied to all elements in a genome. --unknown In the particular case in which the RepeatMasker program was run using a local library that did not use the naming system required to differentiate the class and the subclass (required format is described in RepeatMasker help file), the. out file is slightly different because column 11 (repeat class/family) is usually filled with 'Unknown' or 'Unspecified', which means that the type of individual TE is not specified.
To account for this possibility, the user can use the --unknown option, which will produce results for elements annotated as 'Unknown' or 'Unspecified' and deriving from the local, unannotated bank. --fasta and --flanking 'size_in_bp' The --fasta option allows for the retrieving of sequences of copies reported by the program from the local fasta sequence files used in the RepeatMasker program. To study flanking sequences of the determined copies, the --flanking option can be specified to allow the program to report the flanking regions of the specified size surrounding each copy in addition to the TE sequence. --insert 'size_in_bp' This option changes the code behavior for merging fragments into copies. By default, the furthest extremities of the considered fragment to be merged are compared, and merging takes place if they are less than twice the reference element length apart.
Because the program works locally only on a pair of copies, the best choice to start with is 0, assembling blue and red; after this selection, the program will automatically detect that the assembled blue-red could be concatenated with the green fragment and either propose it to the user if there is ambiguity or assemble them together if the case is unambiguous. (B) The best global solution appears to be the concatenation of blue and green (solution 1) rather than blue and red (solution 0). of the reference TE in bp (information from the consensus sequences, as found in the. length file). In the absence of either the internal or LTR portion of a LTRretrotransposon in the query files, the column will specify 'No_ref_available'; column 4, Fragments, number of hits found by RepeatMasker corresponding to a given TE; column 5, Copies, total number of copies reconstructed from the hits (if the --strict option was selected, this number can be null, meaning that none of the fragments passed our 80-80 rule); column 6, Solo_LTR, number of solo-LTRs reconstructed from the hits.
Running the program with and without the --strict option We did not initially specify use of the --strict option and successively ran the program with the UCSC and RM files. When the --strict option is not specified, the program considers every hit without filtering using our 80-80 rule. We observed the same amount of TEs globally (both in terms of copy number and chromosome coverage, see Additional file 2: Table  S2  and Additional file 3: Table  S3 ) for the two versions of the Repeat Library used with slightly more copies detected in the RM file (208 more copies, see Additional file 2: Table  S2 ). This observation can be explained by the fact that the library used in this case was more recent and thus capable of containing new reference elements. In the results from the UCSC files we observed that the DNAREP1 element was associated with the repeat class family LINE/Penelope, as proposed when it was first described  [24] , whereas it is now known to correspond to the repeat class family DNA/Helitron  [25].
Running the program with and without the --strict option We ran our program with and without the --strict option. Table  2  displays the percent coverage for each TE class in each chromosome and the two cases. The average coverage for each TE class without the --strict option was congruent with the admitted TE content in the human genome with 3. 23% DNA transposons, 19. 85% LINEs, 13. 16% SINEs, and 8. 73% LTR-retrotransposons, representing a total of 44. 98% TEs in the genome  [1]. One original feature of our program is the ability to compute detailed quantitative information chromosome by chromosome, which differs from the output table produced by RepeatMasker. This feature allows us to show that the representation of each TE class differs according to the chromosome. For DNA transposons, chromosomes 3 and 20 displayed the highest proportion of these elements (4.
Whereas it features a user-friendly graphical interface, one can also design CPN models in an XML-based input syntax. Acceleo. Acceleo is a model-to-text transformation tool (its development started in 2006); its main purpose is to implement code generators. An Acceleo program requires a metamodel and a model compliant with that metamodel, from which it generates source code. Acceleo comes in the form of a user-friendly Eclipse plugin, hence easy to install, and features syntax highlighting tools, completion, etc. The metamodel and the model are defined using EMF, which makes Acceleo compatible with other tools based on EMF. In particular, all models created from the ATL (Atlas Transformation Language) framework  [2]  can be directly passed to Acceleo. Acceleo is based on the notion of template, that will implement the transformation rules.
Each rule in a template will map an element from the metamodel (and hence the model) to the text to be generated. An advantage is that the structure of the Acceleo templates will directly reflect the structure of the generated code; in particular, the destination model is directly generated, with no need for postprocessing. A main feature of Acceleo is that the generated text is mixed with Acceleo syntax. Figure  2a  describes function SupEn() (used in  [1] ). This function takes as parameters a state s and the global state machine, (a) Specification using an SMD p in FTS FTS [t = N] [t = N] PL PT Busy F [h = PL] play Busy H pause pause Figure  1 : An example of a CD player  [13]  and writes to the output file the entry behaviour of s. This function mixes control structures to find the entry behaviour with the CPN code to be written to the output file.
Advantages and Limitations of Acceleo Our implementation of the translation mechanism of  [1]  using Acceleo has been greatly facilitated by the user-friendly features of Acceleo: integration into the powerful Eclipse environment, and the use of the metamodel, avoiding us to deal with complex parsing mechanisms. However, the translation also suffered from several drawbacks coming from the features (or lack of features) of Acceleo. Functions. First, the lack of user-defined functions has been a major problem while implementing our translation. Note that, even when changing the form of the algorithms of  [1] , the notion of function, in the sense of an operation depending on some input, would still be required by the translation. However, the notion of template offered by Acceleo partially compensates the lack of function, at the price of a less clear code and lower performances.
Summary of our Experience We managed to implement using Acceleo the whole translation of SMDs into CPNs following the rules defined in  [1]  (except the third algorithm of  [1] , i. e. the translation of history states, that was not implemented due to lack of time 2  ). Using the resulting tool UML2CPN, we could successfully translate several simple examples of SMDs, thus allowing for verification using CPN Tools. The Acceleo code of our translation is available on a dedicated Web page  3. Nevertheless, we do not consider our overall experience of model transformation using Acceleo as entirely successful, for several reasons. First, due to some exponential loops in our Acceleo code, we think that our tool may be very inefficient in the case of large SMD examples. This said, for the (small) examples we have translated using UML2CPN, the translation time was always below 1 second.
Second, the lack of features (in particular global variables, functions and data structures) have been limitations during our implementation of the translation, may be not in a theoretical point of view, but in a practical point of view (leading to a larger and less clear code). Third, Acceleo, as a code generator, is intrinsically dependent on the destination syntax. Hence, a future change in the syntax of CPN Tools would lead us to considerably change our Acceleo templates. One could argue that we could generate using Acceleo an abstract destination syntax instead of the CPN Tools concrete syntax; but one of the advantages of model-to-text transformation is actually to directly generate the destination syntax without the need for an additional parsing tool. Similarly, we could have separated the destination syntax from the rest of the Acceleo code, by using templates that would either generate destination concrete code, or encode functions without any concrete code.
Final Remarks and Perspectives We presented here a report on an automated translation of UML state machines to coloured Petri nets using Acceleo. The resulting tool UML2CPN allows to automatically generate a CPN that can then be analysed by CPN Tools and hence formally prove or disprove the original system safety. Due to the problems we encountered when using Acceleo, we think that our tool may not be easily maintained. Whereas it is understandable that Acceleo was limited to only some features to keep it simple, some extensions would greatly increase its expressive power, and ease its usability and dissemination. In particular, adding global variables, functions and user-defined data structures would certainly make easier the use of Acceleo even beyond its primary purpose (code generation).
We introduce a classification method (cumulative discriminant analysis) of the discriminant analysis type to discriminate between cloudy and clear-sky satellite observations in the thermal infrared. The tool is intended for the high-spectral-resolution infrared sounder (IRS) planned for the geostationary METEOSAT (Meteorological Satellite) Third Generation platform and uses IASI (Infrared Atmospheric Sounding Interferometer) data as a proxy. The cumulative discriminant analysis does not introduce biases intrinsic with the approximation of the probability density functions and is flexible enough to adapt to different strategies to optimize the cloud mask. The methodology is based on nine statistics computed from IASI spectral radiances, which exploit the high spectral resolution of the instrument and which effectively summarize information contained within the IASI spectrum.
The χ 2 s statistic makes use of information provided by NWP forecasts and/or analysis for sea and surface temperature, T s. This temperature is contrasted with that directly estimated by IASI time-space co-located observations. For the case of sea surface, the IASI estimate of T s relies on the classical split window algorithm for the estimation of skin temperature. It is based on the two temperatures T 874 and T 900. The algorithm we have developed for IASI is dependent on the field-of-view angle and on the type of air mass. For the calculation of the regression coefficients involved in the algorithm we have assumed the Northern and Southern Hemisphere to be climatically equivalent. For practical computations, one needs a suitable database of atmospheric and surface parameters. To this end, we have used the ECMWF Chevalier database  (Chevalier, 2001).
Cumulative discriminant analysis Estimate of suitable thresholds for each statistic is required to produce a cloud mask. This is accomplished by (a) choosing the most effective statistics among the considered ones and by (b) actually determining the corresponding thresholds. Towards this objective, this section introduces the cumulative discriminant analysis, which is the engine that will drive the discrimination/classification methodology for producing the final cloud mask. It is important to stress that we look at discrimination/classification in a probabilistic fashion. Also the CDA engine has to be run only once to discriminate the most effective statistics and to generate thresholds. This is done on the basis of a suitable training data set. Once we get the thresholds, we do not need to run CDA again to apply the scheme to a given IASI spectrum.
Limitations of CDA CDA is based on the assumption that the decision rule involves one threshold for each statistic. If we recall how a discriminant analysis based on the probability density function works, this means that the threshold has to split the domain of the statistic in two parts, and in each of them one of the density functions in turn has to be higher than the other one in general. Visually, this occurs when the two density functions look to be translated between each other. From the cumulative distribution function point of view, we require that the two functions do not cross (or cross at very far tails). This condition strictly occurs with parametric discriminant analysis (LDA, QDA), due to the hypothesis of Gaussianity of the density functions. For the IASI data this assumption is verified for most statistics.
Computational aspects The code for the computation of the multidimensional CDA has been developed in Matlab. In particular, an optimized one-dimensional version has been implemented where a direct minimization of the cost function is performed by means of a direct exhaustive search over the step-wise estimated cumulative distribution function. Its generalization to the fully general D-dimensional case is not computationally possible due to the exponential increase of the computational cost with D. Therefore the hypothesis of independence of statistics is resorted to, and minimization of the cost function is accomplished with an optimization method that is derivative-free (Nelder-Mead, based on simplex). We observe by simple arguments that increase of the number of involved statistics can not degrade the cost function.
Results This section will show the main results of the numerical experiments worked out on the databases IASI1 and IASI2 and with the additional validation data set qualified for clear sky with the SEVIRI cloud mask. Finally, a comparison with the IASI level 2 cloud mask implemented at EUMETSAT will be shown. The present work is not intended to yield a fully operative cloud detection algorithm, which requires extensive and independent training and validation data sets covering all climatologies. Rather, we work out some sample test cases to prove feasibility of the full methodology. We will present the results obtained by a transform to PCs. We will not show the optimal thresholds because they make sense only if endowed with the prior PC transform that, as is well known, is unique only apart from rotations.
All thresholds and transform matrices are available upon request. For the present work, the statistic sh (see Table  3 ) was not considered, mostly because it added a poor value or no value to the remaining eight statistics. Let us define the vector of statistics, x, as x = h s , χ 2 s , T 0 , T CO 2 , W 1 , W 2 , W 3 , W 4 , T , (10) where the superscript T stands for transpose. Note that the statistics are ordered in the vector according to their list number as defined in Table  3. For the case of tropical climate zone and sea surface, Fig. 4  (left) shows the covariance matrix of x used to build up the PC basis along with the eigenvectors. The covariance matrix was obtained on the basis of the data set IASI2. It is seen that the matrix is not diagonal, which supports the transformation of x to principal components.
Validation -coinciding training and validation data sets To summarize the performance of the cloud mask we introduce the merit function M = 100(1 -C). M is, indeed, the fraction of successful classifications S (see Eq. 1). The CDA methodology has been applied to select the thresholds for the cloud detection scheme. In doing so, the statistics introduced in Sect. 3 have been transformed to the PC space as in Sect. 4. 7. In the training phase the thresholds have been estimated on the basis of IASI1 or IASI2 data set depending on which population has the larger size for the climate zones at hand. In the present Section validation is made for the same training set; later on (Sect. 5. 2) validation will be made on the other (smaller) data set. For example we used IASI2 as a training data set and will use IASI1 as a validation one in Sect.
5. 2 for the mid-latitude climatic zone in winter (96 522 and 40 941 samples, respectively); we made the reverse choice in summer (81 729 samples for IASI1 and 40 177 for IASI2). Both data sets are quite equivalent for the tropical climatology (186 787 samples for IASI1 and 230 267 for IASI2). Note that we could have jointly used both data sets in the training phase when training and validation data sets coincide; however we kept their distinction to have the same training data set in both experiments of coinciding (this section) and different (Sect. 5. 2) training and validation data sets. Thresholds have been estimated for all the climatic zones of Table  1.
Sea surface The performance of the CDA methodology for the relevant climatic zones is summarized in Table  4. We found that CDA always selects only the first principal component to optimally discriminate clear from cloudy sky, but high-lat summer NH, high-lat winter SH and two sea ice climatologies seem to yield a better score with the first and second principal components. Figure  5  exemplifies the cumulative distribution function, F , for clear and cloudy sky as estimated from the training data set. Figure  5a  applies to tropics and it is seen that the right tail of F Cloudy has a relatively wide overlap with F clear. This overlap is likely due to warm clouds, which tend to be radiometrically equivalent to the colder clear-sky scenes. Another source of possible misclassification is due to IASI sub-pixel clouds, that is, broken clouds which do not uniformly fill the IASI filed of view.
Validation -different training and validation data sets Apart from the tropics, the data sets IASI1 and IASI2 cover different climatic zones. Therefore we present results only for the tropical climatic zone. For the case of sea surface we have used the data set IASI2 for the training and the data set IASI1 for validation. Performance of the methodology is shown in Table  7. The same experiment has been carried out for land, where we distinguish between day and night (results also shown in Table  7 ). During daytime the data set used for training is IASI1, whereas validation is performed on the data set IASI2. The reverse occurs during night-time because of the better coverage of the latter. Comparing Table  7  with Table  4 , it is seen that the performance obtained on the validation data set is largely consistent with the case of coinciding training and validation data sets.
Validation with SEVIRI Validation has been also performed with a cloud mask independent of CMS, on which the training data set relies. For southern Europe and the African continent three consecutive, daytime, IASI orbits belonging to the set IASI2 have been co-located with SEVIRI imagery. The IASI footprints considered in this experiment are shown in Fig. 7. The first IASI footprint observation took place on 22 July 2007 at 06:42:12 UTC, and the last observations at 10:14:18 UTC. To have an idea of the cloud coverage and type over the target area, Fig. 7  shows an RGB composite SEVIRI full-disk image starting at 09:27:43 UTC of the same day. Since the orbits are in the daytime, the SEVIRI cloud mask also benefits from visible channels. In contrast only information in the infrared spectral region is used with IASI.
The IASI footprints have been co-located with the SEVIRI operational cloud mask and flagged as clear sky if 100 % of SEVIRI pixels falling within the IASI field of view are themselves clear sky. In this way we have a SEVIRI-based cloud mask for IASI which can be compared with that obtained with our procedure. The results for this experiment are shown in Table  8. It is seen that the agreement with the SEVIRI-based cloud mask is excellent, which further testifies the very good performance of the cloud detection over land in the case of the tropical climatology. The SEVIRI cloud mask has been also used to classify a data set independent of IASI1 and IASI2. We have used a set of IASI data co-located with SEVIRI imagery from 25 September to 4 October 2010. It was acquired over Europe/Africa in the framework of the IASI/SEVIRI intercalibration experiment, performed within the activities of EUMETSAT GSICS.
Conclusions With regard to developing a cloud mask for MTG-IRS, we have designed and implemented a stand-alone cloud detection tool expected to run only using MTG-IRS spectral radiances. The strategy has been implemented and tested using IASI as a proxy for MTG-IRS. The scheme relies on a set of nine statistics. Six of them are heritage of the AVHRR-based cloud classification and discrimination, while three more have been specifically designed to take into account the hyperspectral characteristics of MTG-IRS. Furthermore, a methodology (cumulative discriminant analysis) has been developed in the context of classification of binary variables, which, using all the statistics, optimally computes thresholds needed to classify a given pixel as clear or cloudy. The system necessitates a training data set as well as a corresponding reference cloud mask.
At present, we rely on the AVHRR cloud mask, which is operationally produced by CMS. The algorithm has been tested against independent data sets as well as cloud masks. As a result, we have the agreement being normally well above 80 %, apart from regions covered by sea ice or land snow/ice, where also the reference cloud masks tend to have a relatively high rate of failure. We think that, once applied to real MTG-IRS scenes, the cloud detection should even improve performance because MTG-IRS is an imager and its pixel size is expected to be 4 times smaller than that of IASI. The paper has been mostly dealing with the scientific basis of CDA and an initial evaluation of the scheme. Although the methodology yields results which are equivalent or better than those of concurrent schemes for IASI (e.