dataesr/scientific-paragraphs-categorization huggingface dataset:
Related Work The first contribution of our work consists of an approach to publish cultural heritage data as open data. Much has been done in the area of open data publication. For example, recent work tries to overcome open data publication and reuse challenges  [10]. Ref. [10]  focuses on open data quality measurement. Ref. [11]  details categories of open data producers (e. g. , public administration) and re-users (e. g. , citizens). Ref. [1]  points out 24 challenges related to the publication of open government data (OGD). Some of these challenges are particularly interesting to our work, namely: (i) too many OGD initiatives, (ii) not enough [accessible] resources, (iii) no standard process or policy for OGD publication, (iv) there is not always a centralised OGD portal available, and (v) lack of suitable software tools for OGD publication.
Ref. [12]  describes a first experiment of cultural heritage data publication on Wikipedia where three hundred cultural heritage POIs were converted to Wikipedia articles. However, the export process to Wikipedia is entirely manual and the texts originally written by experts were rewritten for the purpose of presenting the scientific information to the general public. Focusing on open data and cultural heritage data, some recent work aims to link cultural heritage data with linked open data (LOD)  [13, 14]. These projects' main purpose is cultural heritage corpora enrichment via LOD content and vice versa. The second contribution of our work focuses on recommending touristic itineraries to the user. As part of itinerary generation, it is essential to model data such as users, context, and itineraries.
This approach allows the compiling of a list of pertinent POIs while suggesting with parsimony one or more original POIs to complement those that closely correspond to the user's preferences. In order to do so, we integrate an ant colony algorithm in our solution. This algorithm is quite common in the recommendation of e-commerce products but is far from popular in POIs itinerary recommendation. Unlike Wang et al. [50] , who use an ant colony optimisation algorithm to calculate a crowd-aware trip (i. e. , adaptation of POI visiting order according to crowdedness knowledge), we propose to make use of ant pheromones to compute the social dimension of a POI accuracy score. In the following section, we present the two main contributions of this paper: (i) an open-source framework to export cultural heritage to well-known open data providers; and (ii) an open-source algorithm and framework to recommend a sequence (itinerary) of cultural heritage POIs to be visited by tourists.
Handling Heterogeneous Cultural Heritage Data Figure  1  presents the general architecture of our proposed solution. This architecture is split in two parts according to their roles: (a) data integration; and (b) data leveraging. As briefly discussed in the introduction, the cultural heritage data that we use in this work comes from different and heterogeneous sources. The data integration part is responsible for integrating this data into a single database in a homogeneous data model. More specifically, this integration is achieved through the integration system. The goal of this operation is simply to facilitate the use and manipulation of our data. The integration system is discussed in more details in the next section. The data leveraging part actually contains two applications. The first one, the Open Data Application, is responsible for publishing information from our database into the databases of other open data providers.
A Unified Model for Cultural Heritage Data In order to collect cultural heritage data, a researcher (e. g. , historian or anthropologist) typically visits a POI in the Pyrenees, gathers some information, and then saves it digitally using one of two software: RenablLP2 or Gertrude. (Both of these software have been traditionally used to manage cultural heritage data in France. Different regions adopt one or the other. ) Both software are capable of exporting data from their database into XML files. However, the data coming from each of them is very different in terms of structure, existing fields, and field names. In order to facilitate the use and manipulation of this data, we propose and adopt a unified and homogeneous model (Figure  2 ). This model describes all subjects (i. e. , POIs that are studied by researchers): buildings, objects, etc.
Each subject is georeferenced, has a global description, historic description, and a list of persons that are relevant to it (e. g. , the architect or sculptor who created it). A subject might also be linked to another subject: a building might be a composition of other smaller buildings or a piece of furniture might be inside a building. This model focuses on describing the information useful for our applications only. The integration system populates our database using this model. This data model respects the current standards adopted by the cultural heritage research community in France (e. g. , Mérimée (http://www. culture. gouv. fr/Espace-documentation/Bases-dedonnees/Palissy-consultable-depuis-le-moteur-Collections) [51] , Palissy (http://www. culture. gouv. fr/ Espace-documentation/Bases-de-donnees/Merimee-consultable-depuis-le-moteur-Collections)) and is also able to be easily exported to other models  [48].
Importing Heterogeneous Data In order to import heterogeneous data to our database, the integration system starts by reading XML data originating from RenablLP2 or Gertrude. Figure  3  presents an example of an XML file (from RenablLP2) containing a POI. These files are treated and imported by the importer components (Figure  4 ) in order to transform them from their original model into our model. These data are then stored in an Elasticsearch (https://www. elastic. co) database and become accessible through a consultation API (used by our applications). The integration system has been already developed (The results can be seen here: http://tcvpyr. iutbayonne. univ-pau. fr/) and is fully functional.
Leveraging Cultural Heritage Data We propose two approaches to leverage cultural heritage POIs, both making use of open data concepts. One consists in publishing this data into the database of well-known open data platforms such as Wikipedia. The other one consists in an open-source application capable of recommending personalised touristic itineraries. Not only is this application open-source, but the services developed to support this application (such as the one allowing recommendation) will be also be publicly accessible and available to anyone interested. In the next section, we present a proposal to publish our database as open data.
Cultural Heritage Open Data Publication We propose a fully automated open data publication process according to the work of Kucera et al. [1] and Abella et al. [11]  on open data publication and reuse challenges. For us, data producers are the experts from the public administrations of the Pyrenees (i. e. , Aquitaine and Occitanie regions), data re-users are professionals such as tourism offices, and end-users include tourists and academics. Our proposition is implemented within a software that supports an automated publication (challenge (v), Section 2) of cultural heritage POIs. It should be noted that this is a strength of our process as most of the current Wikipedia publication processes are manual. We automatically create concepts in Wikidata linked to each cultural heritage POI and corresponding images are published in Wikimedia Commons.
Then, the Wikipedia article can be put online. Figures  1  and 5  present the architecture of our open data application responsible for publishing our cultural heritage data as open data. In order to do so, this application uses a communication layer capable of performing HTTP requests that are handled by an API that communicates directly with the database. Once it has the required data, one or more export components are used to communicate with the database of an open data provider. Figure  6  shows an example of a French Wikipedia article published by our application. This article is based on the "Bassin des Ladres", (https://fr. wikipedia. org/wiki/Bassin_des_Ladres) an ancient pool in the town of Ax-les-Thermes registered in our database. The component responsible for publishing on Wikipedia has been developed in Java and uses an API called JWiki (https://github.
com/fastily/jwiki) to interact with Wikipedia. As of now, only one component, able to export our data to Wikipedia, has been developed as a prototype. More export components may be implemented similarly. We plan to implement a new one to publish our data in OpenStreetMap in the near future. Wikipedia was chosen as our first target open data platform in order to take into account challenges number (i), (ii), (iii), and (iv) mentioned in Section 2. In order to deal with the heterogeneity of ways in which open data is normally published, the French Ministry of Culture is launching a big project named the POP platform. (www. pop. culture. gouv. fr/) The aim of this platform is to give general public access to the cultural heritage collected by all its services. However, we believe that as of now, Wikipedia still is a de facto standard for publishing open data in a generally accepted and popular platform and centralised portal.
The Tourist Application and Its Recommendation Mechanism In this paper, we focus on the use of our cultural heritage data to recommend itineraries to tourists. In our approach, an itinerary is built progressively, re-evaluating the pertinence of each POI after a new one is added. This characteristic is extremely important to make sure the relevancy of the sequence is taken into account. For example, suppose three POIs A, B, and C have relevancy scores of 1, 0. 6, and 0. 4, respectively. Now suppose a tourist is able to visit only two of the three POIs. One could argue that visiting A and then B would be the best itinerary, since it has the highest sum score. However, if A represents a beach and B represents a fancy restaurant, it is very unlikely that A followed by B is a good itinerary to be followed.
The former is responsible for consulting data from our main database, enriching it with data from open data (e. g. , Datatourisme, WikiData) and storing it in a second database. We opted to maintain two separate databases in our project because our original and primary database contains only authoritative data produced by experts. The second database contains a mix of this data with crowdsourced data. Besides enriched cultural heritage data, this database also stores the history of interactions between all users and POIs, such as POIs visited and sequences followed. The Recommendation Engine uses all this data to generate and suggest an itinerary when requested by the user. Once the suggested itinerary is accepted by the user, it is stored in a small client-side database that ensures it can be followed even when no internet connection is available.
A Recommendation Algorithm for Itineraries In this section, we present the main contribution of our work: an algorithm to recommend tourist itineraries leveraging our cultural heritage database. This algorithm is a TDOP (see Section 2) in which the travelling time between POIs is taken into account. Algorithm 1 describes the main structure of our approach. Some particularities are described later. Add POI with highest score to the itinerary The algorithm starts by filtering out POIs that are not geographically relevant to the user (line 1). Then, an estimated score for each POI is calculated a first time (the scoring function is described in detail later in Formula (1) of Section 4. 4. 1). Once finished, the origin (first POI to visit) is defined and inserted into the itinerary. Next, if there are any POIs in the region left and there is enough time to visit one, the two last steps are repeated.
In fact, the pheromone (sequence pertinence) can only be assessed when two POIs are being analysed (the last one in the sequence and the new one to be added). Once all this input data is provided, the scoring function calculates and outputs the set of POIs, each POI being associated with its respective score according to the formula (1)) previously presented. In the following, we describe how each part of the function is calculated, namely the pertinence of a POI according to the user preferences (P pre f erence ), its position in a sequence and visit time (P temporal ), and the preferences of other users (P social ).
Comparison to Similar Approaches In addition to the experimentation, we compare our approach to similar ones that are presented in  [55, 56]. The comparison is based on (i) input and output data of the recommendation engine and (ii) implemented algorithms. Ref. [55]  recommends POIs while  [56]  recommends touristic and leisure activities. In our approach, we recommend cultural heritage POIs. All three approaches have the same output, which is an ordered list of POIs/activities to visit during a given period. However, they do not have the same input. Ref. [55]  takes as input a set of trajectories crossing a set of POIs, whereas our approach and the one presented in  [56]  take as input a set of candidate POIs/activities. All three approaches take into consideration visit duration, user preferences, distance between POIs/activities, and POI/activity popularity.
Conclusions In this paper, we presented our research that aimed at leveraging cultural heritage data to promote tourism in the French Pyrenees. In order to do so, we worked on two fronts: (i) the ability to export this data from their original databases and data models to well-known open data platforms, and (ii) the proposition of an open-source algorithm and framework to recommend a sequence of cultural heritage points of interests (POIs) to be visited by a tourist. The idea is to assist in the opening up of cultural heritage data (often catalogued but largely unknown or difficult to find) to the general public as well as to experts. We first showed how we created a unified cultural heritage database from heterogeneous sources. In order to do so, we proposed a unified data model that follows the cultural heritage research community standards.
This unified data model also enabled us to propose and develop an application to publish information from our database to well-known open data providers such as Wikipedia. The second important part of our work, and one we gave more attention to, consisted in the recommendation of a sequence of cultural heritage POIs. We proposed a hybrid approach that benefits from content-based and collaborative filtering strengths. The content-based aspect takes into consideration the profile of a user (e. g. , his location, means of transportation, availability time-window, preferences, etc. ) to recommend POIs that are pertinent. Collaborative filtering is implemented using an ant colony algorithm that adjusts or refines the recommendation, taking into account the popularity of POIs according to other users and also the relevance of a POI in a specific sequence (i.
This version is intended to dynamically adapt an itinerary according to the context. This new prototype should be also able to enrich our recommendations with open data resources (e. g. , Datatourisme) in order to offer users cultural events close to their location and preferences. Finally, we also plan to generalise our approach by feeding the recommender system with heritage POIs (e. g. , Pyrenean POIs exported by us or by others) directly published on Wikipedia (i. e. , without going through our local databases).
The MadGraph5 aMC@NLO framework aims to automate all types of leading-and next-to-leading-order-accurate simulations for any user-defined model that stems from a renormalisable Lagrangian. In this paper, we present all of the key ingredients of such models in the context of supersymmetric theories. In order to do so, we extend the FeynRules package by giving it the possibility of dealing with different renormalisation options that are relevant to supersymmetric models. We also show how to deal with the problem posed by the presence of narrow resonances, thus generalising the so-called on-shell subtraction approaches. We extensively compare our total rate results with those of both Prospino2 and Resummino, and present illustrative applications relevant to the 13 TeV LHC, both at the total-rate and differential levels.
The main goal of this paper is to render systematically feasible, for any user-defined process and in the fully-fledged MSSM, fixed-order, matched, and merged simulations that include NLO QCD effects. In order to do this, we shall rely on automated techniques, which are by now extremely well established; in particular, we shall work in the MadGraph5 aMC@NLO framework  [56]  (MG5 aMC henceforth). We remind the reader that MG5 aMC is a meta-code, which uses hard-wired information on Quantum Field Theories (such as general rules of Feynman diagrammatics, the structures of matrix elements, phase spaces, and cross sections, and so forth), and external information equivalent to the Lagrangian of the theory one is interested in (these are a set of rules, called a UFO model  [57] , obtained automatically from the Lagrangian by means of codes such as FeynRules  [58] , possibly used jointly with NLOCT  [59] ), to construct on the fly a computer code specific to the production process that one is interested to simulate.
We validate our implementation against Prospino2 and Resummino in section 3. The total cross sections of several SUSY benchmark processes in simplified scenarios are presented in section 4. The general algorithms for the treatments of resonances in perturbative computations, and their implementations in MG5 aMC, are discussed in section 5. A case study of jets plus missing energy at NLO+PS accuracy at the 13 TeV LHC is considered in section 6, in the context of a non-simplified benchmark point. We draw our conclusions in section  7. Some details about one-and two-point loop integrals, the MoGRe package (the acronym standing for More General Renormalisation in FeynRules), and the settings relevant to the decoupling mass limit in MG5 aMC can be found in the appendices A, B, and C, respectively.
2. 4. After having implemented the model described in section 2. 1 and its tree-level Lagrangian in terms of superfields, we make use of the superspace module of FeynRules  [91]  to re-express the MSSM Lagrangian in terms of the model physical degrees of freedom and four-component fermions. The renormalisation is then performed with the MoGRe package, that is introduced in appendix B and that is necessary for a flexible definition of the renormalisation scheme. We firstly impose that all external parameters insensitive to QCD corrections are kept unrenormalised. We hence enforce vanishing renormalisation constants for all electroweak inputs (the Fermi constant G F , the inverse of the electromagnetic coupling at the Z pole 1/α and the Z-boson mass m Z ), the parameters of the Higgs sector (tan β, the α angle and the µ parameter), the slepton trilinear couplings (A e , A µ and A τ ), the soft masses associated with the electroweak particles and the electroweakino mixing matrices (U , V and N ) that are external parameters in the SLHA conventions  [65].
Moreover, the first and second generation squark trilinear couplings (A u , A d , A c and A s ) are irrelevant as multiplied by a vanishing quark mass and will thus not be renormalised. These constraints are imposed by using the MoGRe`DefineUnrenormalizedParameter function introduced in appendix B. 2. Secondly, the quark mass dependence of the (remaining) trilinear squark-Higgs couplings ( Tu and Td ) as well as the one of the fermion Yukawa couplings must be made explicit to guarantee the correct functioning of NLOCT. This is achieved by making use of the RemovingInternalCst method introduced in appendix B. 6. 1. The same method is finally also used to replace all occurrences of the g s renormalisation constant in terms of the α S one. Next, we indicate to the code that fields that are insensitive to the strong interaction at the oneloop level (the electroweak gauge and Higgs bosons) do not need to be renormalised.
This is achieved by making use of the MoGRe`DefineUnrenormalizedField method detailed in appendix B. 2. Whilst other purely electroweak fields such as electroweakinos or (s)leptons have in principle to be analogously tagged as unrenormalisable objects, they do not appear in any QCD vertex so that they will be automatically discarded by the code. We finally impose that all field wave-function renormalisation constants are real (via the MoGRe`RealFieldRenormalization method presented in appendix B. 2). In practice, the MoGRe package is initialised as SetOptions [ MoGRe$Renormalize , Exclude4Scalars -> True , FlavorMixing -> {{ st1 , st2 } , { sb1 , sb2 }} , CouplingOrders ->{ QCD }]; which indicates to the code that the stop and sbottom fields mix and that the renormalisation of the four-scalar interactions can be ignored.
Some of these computations have been carried out with MG5 aMC. We recall here that MG5 aMC makes use of the FKS method  [153, 154]  (automated in the module MadFKS  [155, 156] ) for dealing with IR singularities. The computations of one-loop amplitudes are carried out by switching dynamically between two integral-reduction techniques, OPP  [157]  or Laurent-series expansion  [158] , and tensor-integral reduction  [71, 159, 160]. These have been automated in the module MadLoop  [56, 161] , which in turn exploits CutTools  [162] , Ninja  [163, 164] , Iregi  [165] , or Collier  [166] , together with an in-house implementation of the OpenLoops optimisation  [167]. Finally, in the case of matching with PS, the MC@NLO formalism  [34]  is employed, whereas NLO multi-jet mergings rely either on FxFx  [50]  or UNLOPS  [53].
We point out that the original MG5 aMC paper  [56]  had the goal of including as many information on Quantum Field Theories as possible in the meta-code, so as to allow it to simulate both SM and BSM processes by using the inputs in the form of UFO models constructed by codes such as FeynRules or Sarah  [168]. Recently, the program has been upgraded, and can for instance now handle mixed-coupling scenarios, in particular QCD+EW simultaneous corrections  [70]. However, the most general BSM calculations beyond LO feature a number of non-trivial characteristics that are absent in the SM. While fermion-flow-violating interaction vertices and non-renormalisable operators (which were not available at the time of the first release  [56] ) can now be handled, colouredsextet particles and the renormalisation-group running of new couplings are not yet included in MG5 aMC.
Conclusions With the steady increase of the statistics accumulated by the LHC experiments, and the absence of positive results in the searches for new physics, it becomes necessary to improve the accuracy of the simulations of BSM signals, thus matching that of their SM backgrounds. By far and large, this currently means matrix elements computed at the NLO in QCD matched to parton showers (NLO+PS). Such a necessity has spurred some recent theoretical activity, whereby authors have addressed the needs of specific search strategies. The goal of this paper has been that of rendering such an improvement systematic (i. e. achievable for arbitrary processes and for a vast class of renormalisable theories). This is feasible thanks to the powerful and flexible environment constituted by the automated program Mad-Graph5 aMC@NLO and the physics models it can use for simulations.