dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: the  Austrian Federal Ministry of Science and Research and the Austrian Science Fund ; the  Belgian Fonds de la Recherche Scientifique , and  Fonds voor Wetenschappelijk Onderzoek ; the  Brazilian Funding Agencies (CNPq, CAPES ,  FAPERJ , and  FAPESP) ; the  Bulgarian Ministry of Education, Youth and Science ; CERN; the  Chinese Academy of Sciences, Ministry of Science and Technology , and  National Natural Science Foundation of China ; the  Colombian Funding Agency (COLCIENCIAS) ; the  Croatian Ministry of Science, Education and Sport ; the  Research Promotion Foundation , Cyprus; the  Ministry of Education and Research, Recurrent financing  Contract No.
, 2009)   (Wainer et al. , 2011). DEVS has been implemented on several platforms, such as PowerDEVS  (Kofman et al. , 2003) , which uses C++, JDEVS  (Filippi, 2003) , which uses Java, DEVSimPy  (Capocchi et al. , 2011) , based on PyDEVS  (Bolduc et al. , 2001) , which both use Python. This leads to a lack of interoperability between DEVS models. At this time, there is no standard and platformindependent representation of DEVS models, but there exist several approaches, which try to reach this goal. DEVS is composed of two kinds of models: the atomic models and the coupled models. Since they are not used in this paper, coupled models are not described here. The smallest element in DEVS is the atomic model. It is specified as follows : AM = < X, Y, S, ta, δ int , δ ext , λ > where -X = {(p,v)|p∈InputPorts, v∈X p } is the input events set, through which external events are received; InputPorts is the set of input ports and X p is the set of possible values for those input ports; -Y = {(p,v)|p∈OutputPorts, v∈Y p } is the output events set, through which external events are sent; OutputPorts is the set of output ports and Y p is the set of possible values for those output ports; -S is the states set of the system; -ta: S → R 0 + ∪ +∞ is the time advance function (or lifespan of a state); -δ int : S → S is the internal transition function; -δ ext : Q × X → S with Q = {(s,e)/s∈S, e∈[0,ta(s)]} is the external transition function; -λ: S → Y, with Y = {(p,v)|p∈OutputPorts, v∈Y p } is the output function.
Existing Non-MOF DEVS Metamodels Many approaches use XML to specify the DEVS basic elements, such as  (Mittal et al. , 2007)  which can be considered as a "hybrid" approach as it uses SOA in order to perform the simulation. DTDs are used to describe the structure of a DEVS component. A DEVS framework named SimStudio uses a similar specification language named DML  (Touraille et al. , 2010). It also has its own simulation engine called DEVS-MS. In this approach, the XML schema (and not the DTD) gives the structure of a DEVS component. This approach fully complies with OMG MDA specifications. Two DEVS meta-models were also specified using Entity-Relationship diagrams, the meta-metaformalism used by AToM 3  (Posse et al. , 2003 ) (Song, 2006).
Data Analysis All interviews were recorded and taped, and analytical tools included literal transcription, manual coding, and identification of relevant citations. We wrote a memo for each interview consisting of four main areas: (1) origin and destination of interviewee's trip and reasons of the trip?; (2) the mobile technologies they usually used in this kind of trip and in which ways; (3) interesting or unexpected quotations from the interviews, etc. , and (4) the interviewee's understanding of space and ways of socializing space and boundaries. In the first place, following our theoretical framework, these memos enabled us to understand to what extent individuals integrate their multiple environments within their devices on the one hand, and to identify the benefits and drawbacks (Chae and Yeum, 2010) they have from using mobile technologies on the other.
FeynRules FeynRules  [47] [48] [49] [50] [51] [52]  is a Mathematica package that allows the user to compute Feynman rules for a model based on quantum field theory directly from the associated Lagrangian. It can be used for any model that satisfies locality as well as Lorentz and gauge invariance. More precisely, a necessary condition for FeynRules to work correctly is that all indices appearing in the Lagrangian are contracted. Moreover, no assumption is made on the dimensionality of the operators that appear inside the Lagrangian. This feature is particularly useful in the present case, as Lagrangians for spin-3 2 particles necessarily involve operators of dimension greater than four. The output of FeynRules is generic enough to be exported into any other model format. Translation interfaces hence exist to convert the Feynman rules computed by FeynRules into a format readable by various Feynman diagram generators.
ALOHA ALOHA  [56]  is a Python module which automatically computes Helicity Amplitude Subroutines (Helas)  [68]  associated with the interactions of a given model from its implementation in the UFO format. These routines allow for a fast numerical computation of squared matrix elements using helicity amplitudes, with which the complexity of numerical computations grows only linearly with the number of diagrams. This contrasts with trace techniques based on completeness relations applied to squared amplitudes, where the complexity grows quadratically with the number of diagrams. Support for spin-3 2 particles requires the implementation of the external wave functions  [41] , as well as additional internal objects such as the spinorial representation and default propagators. In addition, the ALOHA code has been made faster and more flexible, and new features have been introduced to support for: a user-defined propagator [required for spin- 3  2 ]; including model parameters in the analytical expressions of the helicity routines [required for spin- 3  2 ]; conjugate routines  [69]  with different incoming and outgoing spins [required for spin-3 2 ]; division operation in the analytical expressions of the helicity routines; mathematical functions (exponential, logarithm, etc.
) that have a single (scalar) argument. Furthermore, the ALOHA outputs, i. e. the helicity routines, are better optimized. Firstly, all scalar invariants are identified and computed only once. Secondly, the final expressions are written in a way minimizing the number of computing operations required by the code. These optimizations allow for a gain of a factor of two in speed, compared to the previous version of the package. The new ALOHA code has been extensively tested. First, results have been compared to the previous version of the code for large classes of models such as the Standard Model, the Minimal Supersymmetric Standard Model, Randall-Sundrum extra-dimensional models, effective theories with dimensionsix operators, etc. In each of these models, more than 6000 processes have been considered and checked.
The program has been designed to be extremely flexible and generic with respect to the physics model under consideration. To this aim, it relies both on the UFO for its model format and on the ALOHA package. The latter allows to generate all helicity routines  [68]  necessary for the evaluation of the amplitude associated with the considered process. We benefit from this flexibility and only modify the program in a minimal way in order to implement spin-  3 2 support. More into details, two minor modifications of the code have been required. First, MadGraph 5 has been upgraded to be capable to deal with arbitrary propagators (see Section 3. 2). Next, information on the number of degrees of freedom associated with a spin- 3  2 particle is now included in MadGraph 5. The FeynRules interface converting a model into a UFO library and the use of the latter within the MadGraph 5 framework have been widely tested and validated in the context of several physics applications in Section 4.
CalcHEP CalcHEP  [23, 26]  is a software package which is designed for effective evaluation and simulation of high-energy physics collider processes. The main features of CalcHEP are the computation of Feynman diagrams, integration over multi-particle phase space and event simulation at parton level. The principle attractive key-points along these lines are that it has: an easy startup even for those who are not familiar with CalcHEP; a friendly and convenient graphical user interface (GUI); the option for a user to easily modify a model or introduce a new model by either using the graphical interface or by using an external package with the possibility of cross checking the results in different gauge choices; a batch interface which allows to perform very complicated and tedious calculations connecting production and decay modes for processes with many particles in the final state.
Acknowledgements We would like to thank  Fabio Maltoni  for discussions in the early stage of the project. N. D. C. would like to thank  Nicholas Setzer  and  Daniel Salmon  for helpful discussions. N. D. C. was supported in part by the  LHC-TI  under  U. S. National Science Foundation , grant  NSF-PHY-0705682 , by  PITT PACC , and by the  U. S. Department of Energy  under grant No. DE-FG02-95ER40896. PdA, KM and BO are supported inpart by the  Belgian Federal Science Policy Office  through the  Interuniversity Attraction Pole   P7/37 , in part by the  "FWO-Vlaanderen " through theproject G. 0114. 10N, and in part by the  Strategic Research Program "High Energy Physics " and the  Research Council of the Vrije Universiteit Brussel. O. M. is a fellow of the  Belgian American Education Foundation.
His work is partially supported by the IISN Mad-Graph convention 4. 4511. 10. B. F. has been partially supported by the  Theory-LHC-France initiative  of the  CNRS/IN2P3 , by the  French ANR   12 JS05 002 01 BATS@LHC  and by the MCnet  FP7 Marie Curie Initial Training Network. C. D. is supported by the  ERC  grant " IterQCD ". C. G. is supported by the  Graduiertenkolleg "Particle Physics at the Energy Frontier of New Phenomena ". Y. T. is supported by the  Korea Neutrino Research Center  which is established by the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIP)  (No. 2009-0083526 ).
The advantage of HARMONICA is that it offers the possibility to match the QoS needs and to guarantee a minimal bandwidth for best-effort traffic. However, the manner of finding the optimal increment or decrement in the value of channel access parameters remains the main limitation of this AC model. Dennis and Tim  [17]  proposed an admission control algorithm for the 802. 11e EDCA that considers the dynamic wireless network conditions such as the number of active sessions and the parameters adopted for these sessions. The authors argued that by predicting the achievable throughput of the data sessions and avoiding channel overloading, the QoS of existing sessions can be maintained. For that aim, they integrated some basic ideas of EDCA into their admission control algorithm and extend its capability to offer bandwidth guarantees, rather than providing a relative prioritized service.
If backpressure pushes the flow back to the source and all alternate paths are exhausted, the received flow is rejected. ProbeCast works well in small network density; however, the scalability in terms of network density and mobility is not guaranteed and the interference issue is not considered. In  [21] , Pagani and Rossi proposed an end-to-end probing call multicast admission scheme, named MCAMP. In MCAMP, a source node, before transmitting data stream, floods probing packets to check the bandwidth availability along a multicast tree. Only the receivers take part in the admission control decision by sending an accept/refuse notification to the source based on the received quality. Three priority levels among packets are used in MCAMP: real time, probe, and best effort. The level 2 (probing packets) does not affect existing QoS flows.
FuzzyQoS uses fuzzy thresholds to adapt the traffic transmission rate to the dynamic conditions. By monitoring the rate of change in queue length (variation rate) in addition to the queue length, FuzzyQoS provides a measure of queue state. Furthermore, by using explicit rate congestion notification, FuzzyQoS can make source nodes more responsive to sudden changes in the network traffic volume. The performance evaluation has shown that FuzzyQoS can achieve stable end-to-end delay under different network conditions. However, FuzzyQoS does not deal well with route failures. While searching for a new route, it reduces the data rate of affected sessions. This implies that FuzzyQoS can only support real-time applications with elastic throughput requirements. In  [8] , Valaee and Li proposed a distributed call admission controller using a service-curve provisioning method, which reflects the status of network and depends on the number of active nodes, their activity index, and the back-off procedure used for contention resolution.
In INORA, routing information, modeled as an acyclic-directed graph rooted at the destination node, are assumed to have already been discovered by TORA. When a flow request arrives, the data packets are automatically admitted and the INSIGNIA component attempts to set up soft-state reservations. The data packets follow a directed graph set up by TORA. If an intermediate mobile device detects that it has insufficient available resources (e. g. , by comparison to the channel idle time ratio (CITR)) or its queue is full beyond a certain threshold level, it notifies the previous device on the path. This device then attempts to route the session via different downstream devices. If all of the intermediate nodes' resources are sufficient to support at least the session's minimum required throughput, reservations are set up along the path, as in INSIGNIA.
MAC-coupled vs. MAC-decoupled AC models An AC model is MAC-coupled if it uses information directly given by the MAC layer for estimating the residual channel capacity. Some previous described works (e. g. , DACME) assumed an EDCA MAC scheme, but they can also operate with a non-QoS-aware MAC like the basic 802. 11 distributed coordination function (DCF). In MANETs, as observed in  [9] , contention-free MAC schemes such as code division multiple access or time division multiple access are complex to implement due to the dynamically changing network topology and the lack of centralized control. Note that almost all earlier routing-coupled and routing-decoupled AC models are based on DCF, for example, IFCAC, PAC, HRAC, StAC, CACP, ACSCQS, MACMAN, AAC, etc. On the other hand, the AC models which are decoupled from the MAC layer (e.
Alternatively, one could consider statistical approaches to cope with the impossibility of addressing these issues when using mechanistic models. However, datasets with observed injury profiles, cropping systems and production situation are scarce and statistical approaches are thus even more unlikely to succeed than mechanistic modelling approaches. As an alternative, a generic modelling framework, called IPSIM for Injury Profile SIMulator is proposed. It is deliberately simple in the way mechanisms are represented because the system being described, i. e. the agroecosystem, is far too complex for a truly mechanistic representation. It is based on a simple qualitative hierarchical aggregative approach to represent the effects of various factors affecting injury profiles. This paper presents the basic principles of IPSIM, describing its implementation in a software program and providing an example of its specification for a given crop.
Implementation of IPSIM with a Software Program IPSIM was developed using the DEX method, and is implemented with the DEXi software (  [40] , http://www-ai. ijs. si/ MarkoBohanec/dexi. html). DEX is a method for qualitative hierarchical multi-attribute decision modelling and support based on a breakdown of a complex decision problem into smaller and less complex sub-problems. This tool is generally used to evaluate and analyse decision problems  [27, 29, 41]. In this study, it is used for the first time to develop a simulation model that represents the behaviour of an agroecosystem and which quality of prediction can be assessed. The modelling framework has the following features  [40]. The sub-problems are hierarchically structured into a tree of attributes that represents the ''skeleton'' of the model.
Simulation Results The DEXi software computed the aggregated attribute values of the model presented in the first sub-section of the ''results''. In the same production situation, the three cropping practices led to contrasting injury profiles. In the absence of estimates of potential yield losses caused by these injury profiles, it is difficult to provide direct recommendations for cropping practices adaptations. However, these simulations enable a diagnosis in terms of pest development for the three simulated systems. The intensive system led to IP4, i. e. a medium final injury level for highly endocyclic pests associated with a low final injury level for slightly endocyclic pests (Figure  5 ). For this system, the model suggests that a better management of primary inoculum of the pathogen responsible for eyespot injury should be considered.
Limitations of the Approach Like any other model, the predictive quality of IPSIM should be assessed prior to its use. This highlights the urgent need to collect data in commercial fields describing the input and output variables of IPSIM (i. e. cropping practices, soil and climate, field environment, injury profiles), along with the social, economic and environmental performances of the monitored agroecosystems. It is important to also add measurements of state variables characterising the crop status (e. g. biomass per area unit, Leaf Area Index) in order to better describe important state variables of the agroecosystem for other possible future analyses of the created datasets. However, due to the lack of datasets containing a description of injury profiles, the confidence that users may have in IPSIM models could also be enhanced by comparing simulation outputs with their own expertise to identify any mismatches.
All the information contained within IPSIM models is held in the hierarchical trees and the associated aggregating tables. One of the consequences of this specificity of models developed with the IPSIM framework is that, once developed, the predictive quality of the models can be enhanced easily using experimental datasets by modifying aggregating tables, and, if need be, the structure of the model. The possible injury profiles that IPSIM models can simulate are numerous. However, observations tend to show that the diversity of injury profiles encountered in commercial fields is much less than the structure of IPSIM models can generate. This results from two mechanisms. First, pests can interact directly (through facilitation, predation, competition for the same ecological niche) or indirectly (though modification of the biotope).
This option was tried when developing IPSIM structure, but led to too complicated a structure, the effect of single cultural operations being overlooked among the numerous levels of the tree. In addition, datasets with a description of cropping practices and injury profiles are extremely scarce. The requirement of additional variables describing the crop status (e. g. in terms of phenology, architecture, biomass, Leaf Area Index) would also lead to greater difficulties in developing IPSIM models and in evaluating its predictive quality. We recommend to develop models with no more than 7 final injury levels for a single pest. The lack of precision of IPSIM models could be seen as a drawback as compared to quantitative epidemiological models. Firstly this is because these latter models address a much simpler system: a single pest, rather than an injury profile.
This is certainly not a problem to predict the consequences of technical options in a given production situation, but could hamper the linkage with dynamic models as suggested earlier. This limitation could easily be overcome by associating the level of final injury predicted by IPSIM models with generic dynamics. In order to do so, exponential, monomolecular, logistic, Gompertz, or Richards models  [52]  could be used with generic parameters chosen to represent the qualitative ordinal different injury levels predicted by IPSIM models. The choice of qualitative variables to describe agroecosystems is relevant for several reasons. Firstly, farmers generally rely on a qualitative perception of their environment to make decisions. This suits the formalism of IPSIM. Secondly, because of the complexity of the system, few datasets are available to describe its components, i.
e. the production situation, cropping practices and the injury profile. Using qualitative variables enables one to gather and use various existing datasets that were not acquired for the development of IPSIM models. For instance, datasets from diagnoses of commercial fields or even from experiments may not have used the same severity scale for a given disease. The use of qualitative classes allows data from different origins and/or with different precision to be combined. It is possible to associate interval classes with qualitative attributes. For instance, the 7 levels ''nil''; ''very low''; ''low''; ''medium''; ''high''; ''very high''; and ''maximum'' can be transformed into [0]; [0-20]; ; ; [60-80]; [80-100]; [100] intervals of percentage of diseased foliage respectively, if one wants to compare these outputs with observed severities of a disease for instance.
Thus, data acquired on various scales can still be combined to strengthen the dataset used to estimate the predictive quality of the model or to improve the aggregating tables. Table  5  presents the equivalence between features of models developed within the IPSIM framework and more common quantitative simulation models. Input attributes of IPSIM models can be nominal, ordinal or interval variables, unlike quantitative simulation models, which require only interval input variables. The state variables (aggregated attributes) of IPSIM models, including output variables, are ordinal. However, if need be, output variables of IPSIM models can be transformed into interval variables. This transformation can be performed by associating each possible ordinal value with a quantitative value (e. g.
static final value of an injury level, or quantitative intervals) or with an injury dynamic. The relationship between variables is described by a tree in aggregative qualitative models, whereas quantitative models use equations. The DEXi software  [40]  provides a table with the respective weights of input and aggregated attributes on the value of the root node (main output). This table can be seen as an equivalent to a simple sensitivity analysis to input variables for quantitative models, prior to more detailed ones  [53]. It is notable that IPSIM models have no parameters. The equivalents of parameters that specify relationships among variables in quanti-tative models are the aggregating tables. The proportion of situations correctly simulated is a criterion that can be used to characterise the agreement between values simulated with an IPSIM model and observations.