dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, for the sake of the present work, two major limitations have been lifted. Firstly, at the level of the construction of models, we have overhauled the way in which FeynRules and NLOCT deal with on-shell renormalisation schemes, so that a much larger flexibility is achieved, that helps deal with the difficult cases especially relevant to supersymmetric theories. Secondly, at the level of the Mad-Graph5 aMC@NLO code proper, we have automated a variety of solutions to the problem posed by the presence of partonic channels that appear beyond the leading order in perturbative computations, and that feature narrow resonances; this problem is particularly acute in theories with a complex mass spectrum. Such solutions, that we have dubbed Simplified Treatment of Resonances (STR), generalise the so-called on-shell subtractions, and encompass the Diagram Removal (DR) and Diagram Subtraction (DS) strategies introduced in the last few years in the context of NLO+PS simulations.
Technically, these two pieces of work have been implemented, respectively, in a plugin for FeynRules, called MoGRe, and in a plugin for MG5 aMC, called MadSTR. As a proof-of-concept, we have generated an NLO UFO model for the MSSM with a widely-used renormalisation scheme, and we have studied processes that feature intermediate resonances at the LHC, at the NLO+PS accuracy and with a realistic set of final-state cuts. We have performed thorough self-consistency checks of our implementation, and compared some loop matrix elements generated by the code with those resulting from analytical computations. It is important to bear in mind that the core structure of the MadGraph5 aMC@NLO code, which has been only minimally affected by the present work, has by now been very extensively validated in countless simulations.
B The MoGRe package In order to circumvent the lack of flexibility concerning the choice of the renormalisation scheme in the current FeynRules release, we have developed a plugin, called MoGRe, that is fully flexible in the way a bare Lagrangian can be renormalised. This package can be downloaded from the wikipage collecting details about the MSSM at NLO implementation in FeynRules introduced in this paper, http://feynrules. irmp. ucl. ac. be/wiki/MSSMatNLO. In practice, the user starts by loading FeynRules and any given model implementation in the where one assumes that the current directory is the one containing the plugin.
The user has also the possibility, like in our example, to provide a list with the different sets of fields that mix at the one-loop level. Any physical field can be used in such a list, and the code further checks whether the input is compatible with the representation of the involved fields under the model gauge groups. In our example, we have forbidden any loop-level mixing, except the one of the two stop-eigenstates (denoted st1 and st2 in the FeynRules model) and the one of the two sbottom-eigenstates (denoted sb1 and sb2 in the FeynRules model), as given by eq. (2. 22). Finally, the last option indicates which type of interaction should be renormalised, among all the interactions declared in the MR$InteractionOrderHierarchy option of the FeynRules model implementation. In the MSSM implementation, two types of interactions are available, namely the QCD and QED ones.
The renormalisation of all relevant internal parameters is accordingly and automatically simplified through their functional dependence on the above parameters. Similarly, the code assumes by default that all fields get renormalised, although this may not be the case in practice. For instance, the weak boson two-point functions are insensitive to QCD corrections at one loop. This type of information can be passed to the code by means of the MoGRe`DeclareUnrenormalizedFields method that takes as arguments all fields that should not be renormalised. The arguments can be provided either sequentially or under the form of a list. For instance in the MSSM implementation worked out in this paper, we could use MoGRe`DeclareUnrenormalizedFields[ seL, seR, smuL, smuR, stau1, stau2, sne, snm, snt, A, W, Z ]; although the sleptons are in principle not necessary as they do not appear in any QCD vertex (but we keep them here for illustrative purposes).
They will however be reintroduced at the very end of the renormalisation procedure, as those interactions could appear into given loop diagrams. Moreover, MoGRe requires that all tree-level bilinear terms are canonically normalised and that all kinetic and mass mixings have been appropriately rotated away by the user. In practice, the code ignores all bilinear terms provided by the user and reintroduces the canonical ones directly on the basis of the model field content. As a last initialisation step, MoGRe makes use of the FeynRules model information on the external and internal parameters that must be exchanged during the renormalisation procedure (the FeynRules FR$LoopSwitches option  [58] ). In the FeynRules syntax, FR$LoopSwitches consists in a list of 2-tuples of parameters, FR$LoopSwitches = { { prm1 , prm2 } ,.
Acknowledgements We would like to thank  Dorival Goncalves  and  David Lopez-Val  for collaboration at early stages of the development of MadSTR and for discussions on related topics. We also thank  Davide Pagani  for discussions on these topics. BF and HSS are supported by the  LABEX ILP  ( ANR-11-IDEX-0004-02 ,  ANR-10-LABX-63 ). KM is supported by  JSPS KAKENHI  Grant No. 18K03648. MPAS is supported by the  BMBF  under contract  05H18PMCC1  and the  DFG  through the  Research Training Network 2149 "Strong  and weak interactions -from hadrons to dark matter". MZ is supported by the  Netherlands National Organisation for Scientific Research (NWO). SF is grateful to the  CERN TH division  for the hospitality during the course of this work. VH is supported by the  European Research Council (ERC)  grant  No 694712  (PertQCD), and by the  Swiss National Science Foundation (SNSF)  grant  No 179016.
Experiment Day and Data Collection The experiment was carried out on Tuesday, September 20, 2016. The appropriate norm-activating message (according to the group to which the restaurant was randomly assigned) was placed in a message holder and posted next to the roach fish patties before the restaurant opened for lunch. As the position and reachability of different food items may affect food choice  (Rozin et al. , 2011) , the restaurants were all instructed to place roach fish patties similarly (as the first dish of the lunch buffet). The data were gathered with questionnaires that were placed on the tables together with pencils before the customers arrived. The restaurant staff were instructed to take care, with as little interference as possible, that enough questionnaires and pencils were available throughout the time that lunch was being served.
273). SPSS Statistics Version 24 (IBM) was our principal tool for statistical analyses, but as it cannot handle a hierarchical ANOVA, this particular two-level ANOVA test was performed using a spreadsheet-based instrument developed and provided by  McDonald (2014)  for this very purpose. With a nested ANOVA we can examine whether the treatment has a significant impact on food choices, and if so, what share of the variation in the food choices can be attributed to the treatment. To obtain more accurate p values for unbalanced data we used the Satterthwaite approximation  (McDonald, 2014, p. 170). There is some long-standing debate around the question whether ANOVA can be used for nonnormal Likert data such as ours. For example,  Norman (2010)  argues in his widely cited article that although it is strictly speaking true that ANOVA requires interval-scale data and normally distributed residuals, arguments against using ANOVA fail to account for the robustness of parametric tests.
33, 0. 67, and 1, corresponding to "fully out," "more out than in," "more in than out," and "fully in," respectively, although some of our conditions are crisp by nature. Our calibration protocol is outlined in Table  3. Since QCA does not tolerate missing values, the number of cases (responses) was somewhat reduced (N = 237). We used the software fs/QCA 3. 0 (www. fsqca. com). First, we carried out the necessity analyses. Neither treatment (Baltic Sea protection or Local food) emerged as a necessary condition for roach fish patties choice, which was not surprising considering the statistical results. Instead, Habit (the respondent being a habitual fish-eater) was a necessary condition for the choice of roach fish patties with a consistency of 0. 91 and a coverage of 0. 71, and Novelty (the respondent being a novelty-seeking eater) with a consistency of 0.
Acknowledgments We gratefully acknowledge the generous collaboration of  John Nurminen Foundation ,  Palmia , and  Dr. Chiara Lombardini  in carrying out the field experiment. Our special thanks go to the personnel in Palmia restaurants and all the participants in the study. We also thank  Editor Timothy Smith , the two anonymous reviewers, as well as participants in the Aalto Sustainability in Business seminar and the  GRONEN 2018 Research Conference  for their valuable comments.
XYOM, for XY Online Morphometrics, is an online implementation of the geometric morphometric (GM) approach. It is a platform-independent product, and is presented here as an optional alternative software to client side morphometrics software. From the point of view of the user, the interesting features of a web application are: no download, no installation, no configuration, and automatic updating. Because XYOM is accessible through a standard web interface, it is expected to allow an easier and faster learning process. Additional benefits are that users will have their own highly secured cloud storage, with a 24/7 access from any device, allowing users to share their data, export/download them into their device. Ideally, there would be a permanent, anywhere anytime access on any device (computer, tablet,  smartphone, etc).
We present an alternative product, XYOM, as a solution to the problems generally linked to client side software. It circumvents most of the desktop software inconveniences: no download, no installation, no configuration are necessary. The user benefits from an automatic updating, and needs not worry about platforms compatibilities. At this stage of development, the XYOM software implements the main analyses required to apply modern morphometrics in medical entomology, i. e. , characterization and identification  [1].
Digitization XYOM allows the user to digitize and process landmarks, semilandmarks and pseudolandmarks (contours). The first image must be scaled. This is true even when all images have been collected in exactly the same way. In case some images have been obtained under a different optical configuration, the user is invited to scale each image. Digitization data are stored in the user's Google Drive. Each image and its landmarks can be retrieved later for further examination. Raw landmarks, semilandmarks and pseudolandmarks, as well as size and shape variables computed from them, are stored in the user's Google Drive or in the computer hard disk as text, CSV or TPS formats. They may be exported in formats allowing to use them with other statistical software.
Coordinates files format Coordinates of landmarks are stored in files as data matrices for subsequent statistical analyses. The typical format of such file is a data matrix starting at the second row of the file, as for the CLIC package (http: //www. xyom-clic. eu). The first row allows any free comment describing the data and their possible subdivision. The subdivision is a sequence of numbers corresponding to the size of each constitutive group in the file. There is no column(s) describing each individual (row), so that if the data contain individuals belonging to different species or geographic areas, they are supposed to be assembled as successive groups. Although not necessarily required for some analyses to run, the subdivision information is very useful to interpret the graphical output.
Morphometrics analyses Most statistical algorithms were adapted from the Tcl source of the CLIC package (http://www. xyom-clic. eu), replacing the Hume's linear algebra Tcl library (http://www. hume. com/la/la. html) by the javascript one: numerics. js (http://www. numericjs. com/). A machine learning module is proposed, based on a multilayer perceptron library available at https: //www. npmjs. com/package/mlp. The different analyses currently developed by XYOM are listed in Table  2. The graphical illustrations presented here were derived from data published in Santillán-Guayasamín et al. [2]. They are available as example data from the "help" button of the XYOM menu (https://xyom-clic. eu/ xyom-analyzes/).
Size In case of landmarks (and semilandmarks) digitization, size is computed as centroid size  (Bookstein, 1991). For outlines defined by pseudolandmarks, the size internally used for normalization is half the major axis of the first ellipse (first harmonic), but for ease of visual representation, the output files of size contain either the perimeter or the square-root area of the contour. Statistical comparisons of size among groups are performed by a oneway ANOVA, and illustrated by quantile boxes. Classification based on global size variation is possible using a maximum likelihood based validated reclassification approach  [3] , as well as a multilayer perceptron (https://www. npmjs. com/package/mlp).
Identification Any tentative identification of unidentified or doubtful specimens requires their comparison with reference ones. Generally, the tentative identity of an unknown specimen is given by selecting the reference group closest to it. XYOM proposes three statistical techniques using unknown and reference specimens; the Mahalanobis distance method, the maximum likelihood method  [12, 3]  and the artificial neural network  [13, 14]. For the two first methods, the user is invited to separately upload the file with unknown data and the file with reference data. If input data are raw coordinates of either landmarks or pseudolandmarks, XYOM internally computes the corresponding shape variables (ORP, or NEF). For raw coordinates of landmarks, shape variables are computed on concatenated unknown and reference data.
This utility allows to create a new file containing part of an input file, a submatrix, either a bloc of rows or a bloc of columns. After calling the input file, the user enters the first row or column, and the last one, to create the required submatrix. XYOM detects the situation where an outline file is submitted, allowing to extract rows from a table of data with unequal number of columns according to the individuals. File conversion. The format currently used by XYOM is the one of CLIC (https://xyom-clic. eu), i. e. , a data matrix after a very first row containing a free comment. This format can be converted to the TPS format, or a TPS file can be converted to the XYOM format. There is a special conversion needed by XYOM in case of entering a data matrix containing exclusively pseudolandmarks (outline data): the conversion adds three columns, the first one is the number of points (including the centroid point), the next two ones are the coordinates of the centroid point.
DISCUSSION Because of the mathematical complexity underlying the study of forms, the use of specialized statistical software to perform geometric morphometric analyses is mandatory. In the last two decades a number of excellent propositions has been made freely available by the best specialists in the field (see http://life. bio. sunysb. edu/morph/). All of them need to be previously installed on the user's computer. This very first step may cause troubles due to platform compatibility (MS windows, OS X, Linux, Chrome OS, Unix, Android, iOS, etc. ). Moreover, the installation process itself may be delicate. Users may be reluctant to spend time in updating or securing the current version of the software they succeeded once to install on their computer. The result is, they find themselves using outdated tools.
Mountains play a key role in the provision of nature's contributions to people (NCP) worldwide that support societies' quality of life. Simultaneously, mountains are threatened by multiple drivers of change. Due to the complex interlinkages between biodiversity, quality of life and drivers of change, research on NCP in mountains requires interdisciplinary approaches. In this study, we used the conceptual framework of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) and the notion of NCP to determine to what extent previous research on ecosystem services in mountains has explored the different components of the IPBES conceptual framework. We conducted a systematic review of articles on ecosystem services in mountains published up to 2016 using the Web of Science and Scopus databases.
Methods We conducted a systematic review of peer-reviewed scientific articles written in English on ecosystem services in mountainous regions worldwide using the Web of Science and Scopus databases. We followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for conducting systematic reviews. PRISMA was published to set standards in the reporting of systematic reviews in 2009  [38]  and has proved essential for increasing both reporting and methodological quality  [39]. The PRISMA checklist of rules for this study is given in S1 Table. To avoid double counting, we searched for primary research articles and excluded reviews and conference proceedings. The search string used for the review comprised three main elements: (1) mountainous systems (e.
As we are aware that ecosystem service research is a fast evolving field with increasing numbers of publications each year, we also reviewed 10% of the 310 papers that met the screening criteria of a total of 413 papers that were returned after applying the search methodology to papers published between 2017 and 2018. Analyses of these additional papers was undertaken to confirm that the patterns of ecosystem service research found in this study are consistent with current literature from 2017-2018. S3 Text provides a synthesis of the results of the review of 10% of the papers for 2017 and 2018. The information extracted from each paper was organized in a database according to (1) characteristics of the publication (i. e. year of publication and journal); (2) study location; (3) methodological approach (i.
The increases in the number of components and in network density (i. e. , number of edges) could have resulted only from the increasing number of publications over time (Fig  2 ). The simulation of random datasets and associated networks showed that this was true for the number of components but not for network density (S4 Text). The increase in the number of components resulted from the increasing number of publications over time, as more publications were likely to cover a larger number of topics. But the high network density in the last time period could be not explained only by the large number of papers (S4 Text). In other terms, the most recent papers considered more diverse combinations of topics. The IPBES components with highest weighted degree and betweenness also changed over time, although the impact of land-use change on biodiversity was consistently represented across the four periods of time.
This research has partially been funded by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under grant number  308428 ,  OpenNESS Project  (www. opennessproject. eu),under grant number  308393 ,  OPERAs Project  (www. operas-project. eu),and IP was funded by the  Spanish MEC  under grant number  IJCI-2016-28475. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The 2D-XANES map of a selected element is obtained by recording a series of a few hundred X-ray radiographies at different energies, the energy being tuned in small steps (0. 2-10 eV) across the absorption edge of selected element. Then a data preprocessing, including image alignment, normalization, and several other corrections, allows for creating the full-field XANES data set, which represents a 3D radiography stack consisting of a few hundred images recorded at different energies. Therefore, each pixel contains a full high-resolution XANES spectrum [2, 7].
Currently, these algorithms are well integrated in most preprocessing software available on the TXM beamlines, or in data processing software such as TXM-Wizard package. Like the pixel size and the FOV are energy dependent, the spatial resolution and the FOV of 2D XANES image are, however, limited to the smallest FOV and the largest pixel size of image series. The concept of XANES microcopy is illustrated in Figure  1 of Ref. [7]. Most of the software allow us to combine several image series in order to obtain a larger 2D XANES image and thus to investigate an area larger than the FOV of TXM used. However, this mode (called mosaic mode) can be rapidly time consuming and for investigating large areas, it can be more efficient to scarify the resolution and to use the nonfocused geometry outlined in the previous paragraph (Section 2.
Data processing A 2D XANES image contains several millions of XANES spectra, and it was necessary to develop specific collections of programs to process these huge data, which were integrated in existing software package as, for instance, in PyMca package  [10]  or gather in new software such as in TXM-Wizard  [11]. The latter was used for data processing of examples described in the next paragraphs. The reference described in detail the main features of toolkits available within the package. This software can be downloaded free from its web page https://sourceforge. net/projects/txmwizard/ as well as reference papers.
FF-XANES analysis The radiographies were recorded on ID21 at ESRF using the 20× objectives, and the data preprocessing allowing to create the FF-XANES stacks was performed with the inhouse software package PyMca [10] using the image realignment described in Ref. [15]  and now included in the PyMca package. The closer data analysis was carried out using the TXM-Wizard software following the process described in Ref. [11]. Figure  2(a)  shows one of the radiographies recorded before the Fe K-edge (at 7070 eV). The glassy coating appears as a dense and homogeneous layer of around a few tens microns, while the body reveals a more heterogeneous and porous structure. The first information, which can be extracted without further data processing, are the edge height and the edge energy position of all pixels (Figure  2 ).
doi. org/10. 5772/intechopen. 83633 The study of the iron speciation needs a further analysis since it requires the use of the whole XANES spectrum and not only two parameters (height and energy position of jump edge) deduced from this spectrum. The number of pixel spectra is too huge (close to 2 millions) to consider an individual treatment. TXM-Wizard software proposes two approaches for a closer inspection of the set of XANES spectra: (i) a principle component analysis (PCA) followed by a k-means clustering or (ii) a least squares linear combination (LSLC) fitting with standards associated to a R-factors correlation analysis  [11]. The first approach allows us to obtain a first result with open-minded solution. Here the PCA results show that the first four components (Figure  3 ) allow a rather good system description.