dataesr/scientific-paragraphs-categorization huggingface dataset:
Constraints on scenarios with multiple XQs In this section, we test the ability of XQCAT to set bounds on scenarios with many XQs in toy models as well as more realistic scenarios. Re-interpreting the experimental results on XQs in models where more than one new fermion is present is not an easy task, as already intimated. It is even more involved when the XQs are degenerate in mass. The main difficulty in such re-interpretation of the (single XQ) searches is found in the fact that signals coming from different states can contribute to the same signal bin, therefore one needs to calculate the efficiencies and number of events that pass all the cuts before calculating the eCL. This is exactly what our program XQCAT does. In general, signal bins can receive contributions from different physical states in the following two cases: -the model contains two states with the same decay channels which therefore produce the same final state; -the model contains states with different decay channels, however, the signal features are (even partially) sensitive to the different final states.
Our code XQCAT is not limited to vector-like fermions and can also be applied to models with chiral exotic fermions, like fourth generation ones. To illustrate this application, we focus on the model proposed in Ref. [67]. It consists of two Higgs doublets with a new set of anomaly free chiral fermions. The new exotic states involve two separate left-handed doublets {X, T } and {B,Y}. (Again we use the notation introduced by the authors of  [67]  for the new doublets). Imposing an appropriate discrete symmetry, all the Flavour Changing Neutral Current (FCNC) interactions are suppressed at the LHC energies, whereas the new resonances are allowed to mix with the third generation quarks with the rates BR(X → W + t) = BR(T → W + b) = 100%, BR(B → W -t) = BR(Y → W -b) = 100%. The results shown in Fig.
In this Section we will focus on the scenario where the XQs mix with the light generation quarks: this case has recently received great attention by the experiments, as it may give rise to large single production cross sections  [29]. It has also been shown that the flavour bounds do not disfavour cases where significant mixing to both the top and either the up or the charm quarks is turned on  [30]. Nevertheless, no specific search focused on pair production followed by decays to light jets is available and here we will show that SUSY data samples can already set significant bounds. In Fig. 5 (a), we first consider the simple case of a singlet T , which is allowed to mix with light quarks only, with the following decay rates: BR(W j) = 0. 5 and BR(Zj) = BR(Hj) = 0. 25. Following the same method as in Sect.
In fact, XQs generally come numerous in any theoretical BSM scenario, while experimental searches generally only look for one such states. While this approach is well motivated from the experimental point of view, it becomes a non trivial task to translate the bounds in scenarios with multiple resonances. In order to remedy this obvious drawback, we have developed and presented, in this paper, the XQCAT code, a tool which recasts the available results from the aforementioned experimental searches into those applicable to any spectrum of heavy quark states decaying to W , Z and H bosons plus jets, top and/or bottom quarks. For any implemented search (or combination thereof), our program XQCAT evaluates the efficiencies and the number of events passing all corresponding cuts. Our outputs are presented as eCLs on the corresponding parameter spaces.
Despite some limitations exist in our approach to multi-XQ quark scenarios, insofar as not accounting for possible non-SM-like (including cascade) decays, interference effects and some higher order corrections, XQCAT proved robust and flexible enough in starting the pursuit of the above aim. After validating the code against the inclusive search CMS-B2G-12-015  [34]  for a vector-like T quark and, as a novelty, the indirect SUSY searches of  [35] ,  [36] ,  [37] ,  [38] ,  [39]  and  [40]  (these are CMS searches only, but this is only a choice, not a limitation of the program), we provided a re-interpretation on scenarios with one or multiple top partners, the latter being pair produced via pp → Q Q+ jets, with the quarks Q having general couplings to the light and third generations of SM quarks.
The eCLs were then determined using various methods beyond the most customary and simplest approach of interpolating linearly the efficiencies. While conservative, this approach is accurate and fully model-independent. The inclusion of non-XQ searches in the data sample exploited by XQCAT proved a crucial ingredient in furthering the scope of the available experimental information, as we show that the re-interpretation of the SUSY indirect searches (e. g. , zero-lepton, one-lepton, opposite-sign and and same-sign dilepton signals) gives robust hints regarding exclusion of XQs below the TeV scale at the LHC. This statement remains true no matter the assumptions on their couplings, yielding a scope comparable and, in some cases, complementary to the one afforded by XQ direct searches. Further, their re-interpreted limits from these and from the combination of several SUSY searches (i.
As an outlook, we would like to highlight the following planned developments of XQCAT (not necessarily in chronological order): • Inclusion of further experimental searches in the efficiency database: currently, the only direct search of vector-like quarks implemented in the tool is  [34] , which is designed to identify quarks with charge 2/3, but the implementation of searches dedicated to the observation of quarks with different charges is in order and is currently under development. Furthermore, searches inspired by different BSM scenarios will be included, to test their sensitivity to specific scenarios and compare them with the sensitivity of direct searches. Of course, the most recent results by both ATLAS and CMS will be represented in the tool. • Inclusion of EW single production processes: single production can be described in a model-independent way, and through the formalism presented in  [33]  the signal generated by processes of single production can be decomposed in a limited number of independent subprocesses as in the case of pair production.
Acknowledgements DB, AB, SM and LP are financed in part through the  NExT Institute. DB, SDC  and LP would like to thank the  Galileo Galilei Institute (GGI) in Florence  for hospitality while part of this work was carried out. The work of MB is supported by the  National Fund for Scientific Research (F. R. S. -FNRS, Belgium)  under a  FRIA grant. AD is partially supported by  Institut Universitaire de France. GC and AD also acknowledge partial support from  Labex-LIO (Lyon Institute of Origins)  under grant  ANR-10-LABX-66  and from  IN2P3 Theory LHC-France  funding.
Experimental design and data collection Plots with herbaceous vegetation were selected at each site in order to minimize the variability due to the effect of soil cover on microclimate conditions, mainly on soil temperature. Areas with natural vegetation were selected for the savanna and alpine ecosystems but plots on managed grassland were selected for the forest ecosystems. 14 C-and 15 Nlabelled straw was mixed with soil (taken from the top 0-10 cm layer at each of the sites) in 14 × 15 cm porous bags. The top part of the bags had a 1 mm mesh to allow the passage of plant roots and mesofauna, and the mesh of the bottom part was 0. 1 mm to minimize losses by gravity. The soil weight per bag was adjusted to reproduce the natural bulk density for a volume of 210 cm 3. The labelled straw was obtained by growing the wheat in a labelling chamber with controlled temperature, radiation, humidity, and CO 2 concentration.
Following the death of our colleague, Pierre Bottner, we recovered most of the experimental data, except the last results for microbial biomass (MB) and inorganic 15 N, where only the first five results were available for each site. All 15 N-labelled data and 14 C-labelled data  (Pansu et al. , 2010)  are expressed as a fraction of the labelled N and C added at the start of the experiment. As the labelled inorganic N in the soil bags was very low compared to the total labelled 15 N, the total labelled 15 N was considered to be the labelled organic 15 N, the difference between the 15 N added in the straw and the organic 15 N being the production of inorganic 15 N which was assumed to have been lost mostly by root uptake, leaching, and gaseous losses through the porous soil bags. Flow diagram for the MOMOS model coupled with a soil water module and a production module; MB is the microbial biomass, VL is the labile necromass (NC), VS is the stable necromass (NC); HL is the labile humus, HS is the stable humus; see table  3  for meaning of the k parameters.
The decomposition model MOMOS As carbon and nitrogen are closely associated in living organisms, it was assumed that the nitrogen cycle could be modelled in MOMOS-N in the same way as the carbon cycle in MOMOS-C  (Pansu et al. , 2004 (Pansu et al. , , 2010)) , using a subscript e (either C or N) to distinguish between the elements in the model. MOMOS (Fig. 1 ) was defined as a five-compartment model centred on the activity of soil microbial biomass (MB e ) that grows by assimilation of labile (VL e ) and stable (VS e ) fractions of plant necromass (NC e ) as well as labile (HL e ) and stable (HS e ) fractions of humus. The microbial mortality regulates humus formation. The only process which is considered more chemical than biological is the humus stabilization from HL e to HS e , which is in agreement with observations of  Kemmitt et al.
Assumptions and strategies tested for simulation of N flows The function f (x C,MB , x N, MB ) of equation 7 was defined in terms of η lim MB , the target value for the C : N ratio of the MB (η MB ). The aim was to test the significance of exchanges (N immobilization and N mineralization) between MB and inorganic N, as modelled in MOMOS, using data for MB-15 N and inorganic-15 N. Two assumptions were tested: 1. The MB C : N ratio remains constant throughout incubation (microorganism homeostasis): f x C, MB , x N, MB = x N, MB - x C, MB η lim MB. ( 11 ) For this function, only one parameter, η lim MB , has to be fitted for each site, all the other parameters being those fitted for 14 C simulations (Table  3 ).
Accuracy tests The significance of the MOMOS simulations compared to the mean of measured values was tested by F = n i=1 ( ȳi -ȳ) 2 /(n -1) n i=1 ȳi -ŷi 2 /(n -p) , (13) where i = 1,. n is the number of sampling occasions (n = 11), p the number of model parameters which were specifically adjusted to predict the total production of inorganic 15 N (p = 1 for assumption 1, p = 3 for assumption 2), ȳi is the measured total remaining 15 N at i, ŷi is the corresponding MOMOS predicted value with assumption 1 or 2 and ȳ is the mean of the data series for each site. The graphs in Figs. 3-5 show whether the predicted values were within or outside the confidence intervals of the corresponding data series. F tests were performed using RSS T , the residual sum of squares between the measured values and the values predicted by MOMOS for assumptions 1 and 2 (Table  4 ): F y A12 = RSS A1 RSS A2 = n i=1 ( ȳi -ŷiA1 ) 2 /(n -1) n i=1 ( ȳi -ŷA2 ) 2 /(n -3) , ( 14 ) where y A1 and y iA2 were the predicted values for assumptions 1 and 2, respectively.
Acknowledgements. This study was part of the bilateral France-Venezuela cooperation programme ECOS-NORD V07A01 (Modelling soil organic mater in Venezuelan ecosystems and its application to fertility management and carbon sequestration) and financed in part by  CDCHT-ULA  (project  C-765-95-01-B ) and  FONACIT  ( F-2002000424 ). We should like to thank the experts who contributed to the Biogeoscience discussion website http://www. biogeosciences-discuss. net/:  Nicolardot  ( Agrosup, Dijon, France ) for his constructive comments on microbial C : N ratio,  A. Hammoudi  and  O. Iosifescu  ( Institute of mathematics and modelling, Université Montpellier 2, France ) for their analysis of the MOMOS equation system,  S. D. Allison  ( University of California, Irvine, USA ) and three anonymous referees for their constructive reviews for  Biogeoscience and Biogeoscience discussion.
The Linked Data Cloud is too big to be locally manipulated by standard computers and all use-cases doesn't need to manipulate the whole cloud. To get exactly what is needed for a specific use-case, we need to obtain the specific parts from each bases of the Linked Data Cloud. This paper proposes a method to smartly extract a sub-part of the Linked Data Cloud driving by a list of resources called seeds. This method consist of extracting data starting from seed resources and recursively expanding the extraction to their neighbours.
INTRODUCTION Linked Data 1 is not anymore the domain of Semantic Web enthusiasts and academics. Today the Linked Data Cloud counts more than 300 bases in different domains, of sizes varying between a few hundred to several billions statements (known as triples). As it became more widely recognized, new uses have appeared and to each use corresponds a specific part of a base, or of the whole Linked Data Cloud. When there is need to locally manipulate the Linked Data Cloud, some use-cases only need specifics sub-parts of bases of the Linked Data Cloud. Currently only base dumps allow us to manipulate locally data from the Linked Data Cloud. But dumps can not be easily exploited by human (because they contain raw data) or by a standard machine (because of their size). In this paper we propose a method to smartly extract real parts of the Linked Data according to given specifications.
Our motivation to develop such a method came from a previous work where we needed a "mini Linked Data Cloud" to evaluate a novel approach for querying a set of RDF bases. Works in similar situations  (Bail et al. , 2012; Schmidt et al. , 2011; Morsey et al. , 2011; Schmidt et al. , 2008)  used either raw dumps, or random-generated datasets, or present their datasets in theory only, without access to the tools they used. To meet our needs, we have extracted some sub-parts of (real) data from the universal DBPedia base to create different bases in accordance 1 http://linkeddata. org/ with specific domains of the Linked Data Cloud. Our contribution in this paper is to propose both a method and an implementation, driven by seed resources, to extract a focused sub-part of the Linked Data Cloud.
RDFS  (Brickley and Guha, 2004)  provides basic elements for the description of ontologies intended to structure RDF resources. RDFS allows to (i) declare types of resources (rdfs:Class) and their properties (rdf:Property) ; (ii) specify the signature of properties (rdfs:domain and rdfs:range) ; (iii) specify the inheritance links among classes (rdfs:subClassOf) or among properies (rdfs:subPropertyOf). Figure  2  describes some classes and properties, and their inheritance links. A triplestore is a specially designed database for storage and retrieval of RDF triples. In order to query such a base, SPARQL (W3C, 2013) is the most widely user language.
Linked Data The Linked Data is a movement supported by a large community, encouraging the publishing of open RDF bases respecting three best practice rules: • All resources must be represented by a dereferencable URI. • Bases must be interlinked • Each base must be publicly accessible via a SPARQL endpoint. All Linked Data interlinked bases compose the Linked Data Cloud, counting several hundred bases in various domains and sizes. But today there is no centralized authority maintaining a list of all available bases. Currently the most complete list is available on datahub 2. 1  contains statistics 3  about the repartition of Linked Data bases in major domains and their sizes as of September 2011. Table  2  contains information about 6 bases from the Linked Data Cloud. These information where produced by us in September 2013, using either SPARQL1.
Parameters To obtain a focused sub-part of the Linked Data Cloud on a chosen topic there are two parameters to configure the algorithm. For a chosen topic, the seed has to contain target resources linked to most of the relevant resources to this topic. For example, the seed of an extraction can ICAART2014-InternationalConferenceonAgentsandArtificialIntelligence Algorithm 3. 1: Extraction of statements centred on a resource s. Input be a list of individuals from a given class or the class itself (e. g. to extract some cinematographic information from DBPedia we can use the "Movie" class, or more precisely a list of all films produced by Quentin Tarantino). The choice of the maximum rank of the extraction limit the size on the extracted sub-part and also determines how much concentrated the sub-part of a base will be around a seed resource.
USE-CASE: DATA GENERATION FOR BENCHMARKING For some uses, especially for benchmarking, it is interesting to locally handle data from the Linked Data Cloud and specially only needed sub-part from this big cloud to a given use-case. There is a limited offer of benchmarks to evaluate query methods or engines on the Linked Data. Most of these benchmarks use dumps from the most known bases of the Linked Data Cloud  (Bail et al. , 2012; Schmidt et al. , 2011)  or use random generator  (Schmidt et al. , 2008). So, the choice is either using huge bases with real data or manageable bases with fake data. Using real data at an acceptable scale for a "standard" computer 4  , as base dumps often contain at least several million triples. In a previous work, to test a new approach for querying a set of distant RDF bases, as presented in  (Raimbault and Maillot.
, 2013) , we needed a mini-Linked Data Cloud where each base is composed with real data on a specific domain, is small enough to be processed in our experimental environment, and has a SPARQL endpoint. We present here the experience of an extraction use-case we used in this previous work, according to the method presented in Section 3. To evaluate our approach we needed to evaluate different method of querying the Linked Data Cloud. For practical reason we chose to only use DBPedia, the biggest multidomain base in the Linked Data Cloud, to have the same ontology for every base. Even with the same ontology we aimed to keep the "structural" differences between each base as those between each base of the Linked Data Cloud. These differences resided in the instantiation distribution in each class for each base, i.
e. in each specialized domain, there is more individuals of the classes representatives of the domain subject (e. g. Animal in Life Science, Document in Publication, etc. ) than others. We extracted with 2 as maximum rank, 10 different bases for our tests 5. The seeds were chosen to represent some specific domains of the Linked Data Cloud: • The Cat class (in the "Life Sciences" domain) • The Scientific Journal class (in the "Publications" domain) • The Legal Case class (in the "Government" domain) • The Potato individual (in the "Life Sciences" domain) • The Moon individual • The singer Elvis Presley (in the "Media" domain) • The city of Paris (in the "Geographic" domain) • The city of Berlin (in the "Geographic" domain) • The list of all films realized by Quentin Tarantino (in the "Media" domain) • The list of all books written by Emile Zola (in the "Media" domain) Table  3  shows the distribution of class instantiations for each extracted base compared to their source DB-Pedia 6.
CONCLUSIONS We have presented a method for extracting targeted sub-part of RDF bases, driving by a list of selected resources called seed. This method non-naively extracts parts of a RDF base by recursively expanding the information on each selected resource and its neighbours. This method can be used to create a dataset with real data for benchmarking query approach on several RDF base, as it is needed through the Linked Data Cloud. In future works we plan to use this method in a process to create benchmarks for the Linked Data by using statistics gathered during the extraction to automatically suggest associated queries. Furthermore this method could be used as a tool in user interface for the generation of RDF base summaries.
With the increasing popularity of scientific workflows, public repositories are gaining importance as a means to share, find, and reuse such workflows. As the sizes of these repositories grow, methods to compare the scientific workflows stored in them become a necessity, for instance, to allow duplicate detection or similarity search. Scientific workflows are complex objects, and their comparison entails a number of distinct steps from comparing atomic elements to comparison of the workflows as a whole. Various studies have implemented methods for scientific workflow comparison and came up with often contradicting conclusions upon which algorithms work best. Comparing these results is cumbersome, as the original studies mixed different approaches for different steps and used different evaluation data and metrics.
Approaches for structural workflow comparison, on the other hand, can be applied without such backing human-provided textual knowledge. Yet, they have to assess a workflow's functionality from the information contained in its DAG structure and the modules it is composed of. As another source of data, provenance represents concrete execution traces of workflows. Such traces would allow, for instance, to take execution parameters and runtime information into account as an additional means of comparison. However, specialized provenance databases have just started to emerge (e. g. , in the ProvBench initiative  [1] ), and we are not aware of any workflow repository also containing real execution traces. Which approach to scientific workflow comparison provides best results, and how different aspects of workflows contribute to their functional similarity is still an open question.
There have been numerous studies investigating both annotational  [11, 36, 17]  and structural  [34, 33, 36, 4, 18, 17, 38]  approaches, but their comparison is hindered by a number of factors. Firstly, the process of scientific workflow comparison entails several steps from comparison of single modules to comparison of whole workflows  [35, 4]  -each of which may be treated differently in the methods considered. This makes it hard to determine how single aspects contribute to workflow similarity, and which approach to a specific step of the comparison process provides best results. Secondly, the evaluation of a proposed method is often done by manual inspection of the methods concrete output, or on a proprietary dataset, both hampering repeatability. To compare multiple methods and configurations it is necessary to have a method-independent, gold-standard corpus to evaluate on.
A FRAMEWORK FOR SCIENTIFIC WORKFLOW SIMILARITY The functionality of a scientific workflow is determined by the data processing modules it is composed of, and how these modules are connected by datalinks. While we are ultimately interested in comparing whole workflows, each module represents a distinct functional entity in its own right. As all previous work, we make the reasonable assumption that modules are deterministic and two identical instances of a module are functionally equivalent. Yet, any two different modules may carry the same or very similar functionality. For instance, two different web-services, or a web-service and a locally invoked script, may be functionally equivalent. Thus, to compare scientific workflows wrt their functionality, similarity has to be established on two levels: the level of single modules and the level of whole workflows.