dataesr/scientific-paragraphs-categorization huggingface dataset:
Our stop simplified model has been implemented in FeynRules, and we have employed the NloCT package to automatically generate all QCD ultraviolet and R 2 counterterms (including the Standard Model ones). The output has been validated against our analytical calculations, which constitutes a validation of the handling of new massive colored states by NloCT. Finally, the analytical results have been exported to a UFO module that we have imported into MadGraph5 aMC@NLO. For our numerical analysis, we consider scenarios where m 3 and m χ are kept free. The gL,R parameters are fixed to typical values for supersymmetric models featuring a bino-like neutralino and a maximally-mixing top squark, gL = 0. 25 and gR = 0. 06.
V. CONCLUSIONS In this Letter, we have demonstrated that a joint use of the FeynRules, NloCT and Mad-Graph5 aMC@NLO programs enables the full automation of the Monte Carlo simulations of high-energy physics collisions at the next-to-leading order accuracy in QCD and for non-trivial extensions of the Standard Model. This has been illustrated with simplified models such as those used for supersymmetry searches at the LHC. In this context, we have adopted setups that exhibit extra colored particles and non-usual interaction structures and presented the analysis of two exemplary signals with the automated tool MadAnalysis 5. In the aim of an embedding within experimental software, we have designed a webpage, http://feynrules. irmp. ucl. ac. be/wiki/NLOModels where hundreds of differential distributions are available for validation purposes, together with the associated FeynRules and UFO models.
sion frontier, the Research Executive Agency of the European Union under Grant Agreement PITN-GA-2012-315877 (MCNet) and the Theory-LHC-France initiative of the CNRS/IN2P3. CD is a Durham International Ju-nior Research Fellow, the work of VH is supported by the SNF with grant PBELP2 146525 and the one of JP by a PhD grant of the Investissements d'avenir, Labex ENIGMASS.
ACKNOWLEDGMENTS We are extremely grateful to  D. Goncalves-Netto ,  D. Lopez-Val  and  K. Mawatari  for their help with Mad-Golem. We also thank  R. Frederix ,  S. Frixione ,  F. Maltoni ,  O. Mattelaer ,  P. Torrielli  and  M. Zaro  for enlightening discussions. This work has been supported in part by the  ERC  grant  291377  LHCtheory: Theoretical predictions and analyses of LHC physics: advancing the preci-.
The remaining of the paper is organized as follows. Section 2 sheds light on the related work. We introduce our new distributed intrusion detection system based on the multi-agents technology in Section 3. We then relate the encouraging results of the carried out experiments in Section 4. Finally, Section 5 concludes and points out avenues of future work. fitting in the distributed IDS trend using ontological structure attempt to enhance the IDS accuracy and performing intelligent reasoning. Worth of mention that the first research of applying ontology within intrusion detection context was done by Undercoffer et al. [20]  in 2003. In this respect, the authors developed an ontology focused on the target (centric) and supply it within the format of the logical description language DARPA DARPA Agent Markup Language + Ontology Inference Layer (DAML + OIL).
Now, it is ready to analyze the OWL packets to detect those that correspond to known attacks. Indeed, the MisuseAgent searches for attack signatures 4  in these packets, by consulting the ontology ASO (Attack Signatures Ontology). Consequently, if there is a similarity between the OWL packets and the SWRL rules that define the attack's signatures, then the agent raises an alert to the ReporterAgent; 3. The filtered network packets are fed into an AnomalyAgent, which uses the clustering algorithm AD-Clust to detect the unknown attacks. Likewise, the agent sends an alert to the ReporterAgent, if an attack is identified; 4. Finally, the ReporterAgent generates reports and logs. OCMAS-IDS detects the known attacks through the intelligent agent Mis-useAgent, which uses an ontology to enrich data intrusions and attack signatures by semantic relationships.
The Attack Signatures Ontology (ASO) Since last few decades, Raskin et al. [16]  opened a new field, that focuses on using Ontology within information security and its advantages. In fact, ontologies present an extremely promising new paradigm in computer security domain. They can be used as basic components to perform automatic and continuous analysis based on high-level policy defined to detect threats and attacks  [10]. Moreover, they enable the IDS with improved capacity to reason over and analyze instances of data representing an intrusion  [7, 20]. Furthermore, the interoperability property of the ontologies is essential to adapt to the problems of the systems distribution, since the cooperation between various information systems is supported  [3, 7]. Within the OCMAS-IDS system, an ontology, called ASO (Attack Signatures based Ontology), is implemented, in order to optimize the knowledge representation and to incorporate more intelligence in the information analysis.
We used machines that were connected via a switch, thus forming a switched network. Moreover, we simulated attacks using the well known tool Metasploit 9  version 3. 5. 1. The simulated eight different attack types are: -attack1: DoS Smurf; -attack2: Backdoor Back Office; -attack3: SPYWARE-PUT Hijacker; -attack4: Nmap TCP Scan; -attack5: Finger User; -attack6: RPC Linux Statd Overflow; -attack7: DNS Zone Transfer; and -attack8: HTTP IIS Unicode.
ORCHIDEE terrestrial biosphere model ORCHIDEE is a global process-oriented TBM  (Krinner et al. , 2005)  and is the land surface component of the IPSL-CM5 Earth System Model  (Dufresne et al. , 2013). In this study we used the "AR5" version that was used for the IPCC Fifth Assessment Report  (Ciais et al. , 2013). The model calculates carbon, water and energy fluxes between the land surface and the atmosphere at a half-hourly time step. The water and energy module computes the major biophysical variables (albedo, roughness height, soil humidity) and solves the energy and hydrological budgets. The carbon module controls the uptake of carbon into the system and respiration following cycling of C through the litter and soil pools. Carbon is assimilated via photosynthesis depending on light availability, CO 2 concentration and soil moisture, based on the work of  Farquhar et al.
MODIS NDVI data and processing NDVI observations are derived from the MOD09CMG collection 5 (v5) surface red (620-670 nm) and near-infrared (841-876 nm) daily global reflectance products available at 5 km from the MODerate resolution Imaging Spectrometer (MODIS) on-board the NASA's Terra satellite. The reflectance data were cloud-screened and corrected for atmospheric and directional effects (related to the change of reflectance with observation geometry) following  (Vermote et al. , 2009) , and the corresponding NDVI was calculated for the 2000-2008 period. The time series were interpolated on a daily basis, in order to account for any missing values due to cloud, using a third degree polynomial and considering the 10 nearest valid acquisitions, with a maximum allowed difference of 15 days.
System description The ORCHIDEE Data Assimilation System (http://orchidas. lsce. ipsl. fr) is based on a variational data assimilation procedure that has been described in detail in previous studies using ground-based net surface CO 2 and energy fluxes  (Kuppel et al. , 2012; Santaren et al. , 2007; Verbeeck et al. , 2011). Kuppel et al. (2012)  presented the first results using a multisite (MS) version of the system at selected eddy-covariance flux tower locations, where data from all sites were used to optimise the model parameters at the same time for each PFT. As with most statistical data assimilation approaches it follows a Bayesian framework, where prior knowledge of the parameter values is updated based on new information from the observations. Assuming that the probability distribution functions (PDFs) of the model parameter and observation uncertainties are Gaussian, the optimal parameter vector x can be found by minimising the following cost-function J (x)  (Tarantola, 1987) : J (x) = 1 2 [(H (x) -y)R -1 (H (x) -y) T + (x -x b )P -1 b (x -x b ) T ], (2) where y is the observation vector, H (x) the model outputs, given parameter vector x, R the uncertainty matrix of the observations (including observation and model errors), x b the a priori parameter values (the standard values of ORCHIDEE) and P b the a priori uncertainty matrix of the parameters.
Global evaluation with MODIS NDVI The global simulations were evaluated with the same MODIS NDVI data that were used at the site level for the optimisation, following the protocol of  Maignan et al. (2011). The following metrics were used for evaluation of both the prior and posterior simulations. The correlation between the normalised simulated fAPAR and MODIS NDVI weekly time series. The bias (in days) between the modelled and observed SOS and EOS dates (model -observations) were also examined so as to investigate the impact on the timing of the phenology more directly (a positive bias indicates the model date is later than the date calculated from the observations). The above metrics were calculated for each grid cell. Following this a global median value was calculated, as well as a median correlation per PFT.
Neither the SS or MS optimisations are able to reduce the model-data misfit by forcing an earlier start of senescence at any of the sites. As there is no discernible improvement after the MS optimisation of the NC4 PFT, the posterior parameters are not used in the further analysis of global changes to the gross primary productivity (GPP), GSL or trends. It is likely that the phenology models that are used in water limited ecosystems (TrBR and NC4) need revising (see discussion in Sect. 4. 4).
Validity of the posterior parameters and optimised phenology models As data assimilation schemes are expected to result in a reduction in the prior data-model misfit it is useful to assess if the posterior parameter values are indeed realistic, which is conditioned on the prior range as well as the interaction (correlation) between the parameters. However validating these values remains difficult, as there is no database that corresponds directly to phenology model parameters, which may have a different meaning in different models. Leaf lifespan (LL) however can more easily be measured and therefore can be found in the literature and in plant trait databases  (Kattge et al. , 2011; Wright et al. , 2004). Although leaf lifespan is not the same as the critical age for senescence parameter (L agecrit ), the leaf lifespan should be similar if not lower than L agecrit , and thus serves as a benchmark to a certain degree.
Reich (1998)  gave 4-6 months for coldtemperate and boreal broadleaved and needleleaved deciduous trees, similar to the mean values for the same functional types in the TRY database  (Kattge et al. , 2011). These values encompass the range of optimised values for temperate and boreal broadleaved (TeBD and BoBD) PFTs (160 and 240 days, respectively, Table  2 ). The posterior value of L agecrit for the boreal needleleaved (BoND) PFT was lower than this range at 90 days, and was at the lower boundary of the parameter range. The TRY database gives a mean value of 3. 85 and 1. 68 months for C3 and C4 grasses, respectively  (Kattge et al. , 2011). These values are higher than the posterior value of the C3 grasses (60 days) and lower than the posterior value for C4 grasses (166 days). The values of L agecrit for NC3 and BoND PFTs were therefore too low and it is also notable that the values of T senes were also at their upper bounds for these two PFTs.
Acknowledgements. This work was supported by the  CARBONES  project within the  European Union 's  7th Framework Program for Research and Development. N. MacBean  warmly appreciates the helpful comments on the manuscript provided by  M. De Kauwe  and  M. Disney  for discussions about elicitation. The authors are grateful to the  ORCHIDEE  project team for their support, and for the computing resources and assistance provided by the  IT  staff at LSCE. Finally we would like to thank the reviewers and editor for their useful suggestions for improving the manuscript. Edited by:  T. Keenan.
Data availability The MODIS MOD09CMG collection 5 surface reflectance data are freely available to download from the Land Processes Distributed Active Archive Center (LP DAAC) data portal (https://lpdaac. usgs. gov). The authors wish to thank M. Jung for providing access to the GPP MTE data, which were downloaded from the GEOCARBON data portal (https: //www. bgc-jena. mpg. de/geodb/projects/Data. php). www. biogeosciences. net/12/7185/2015/ Biogeosciences, 12, 7185-7208, 2015.
Limitations of message passing programming model The current vision on exascale system is at the moment to exploit distributed memory parallelism, and therefore the message passing model is likely to be used at least partially. Moreover the most popular system implementation of the model, MPI, has been shown to run with millions of cores for particular problems. MPI is based upon standard sequential programming languages, augmented with low-level message passing constructs, forcing users to deal with all aspects of parallelization, including the distribution of data and work to cores, communication and synchronization. MPI primarily favors static data distribution and is consequently not well suited to deal with dynamic load balancing. However, it has been shown that the all-to-all communication algorithms used in the message passing models are not scalable (most commonly used implementations often assume a fully connected network and have dense communication patterns) while all-to-some, one-sided or sparse communication patterns are more reliable.
Limitations of heterogeneous programming Clusters of heterogeneous nodes composed of multi-core CPUs and GPUs are increasingly being used for High Performance Computing due to the benefit in peak performance and energy efficiency. In order to fully harvest the computational capabilities of such architectures, application developers often employ a combination of different parallel programming paradigms (e. g. OpenCL, CUDA, MPI and OpenMP). However, heterogeneous computing also poses the new challenge of how to handle the diversity of execution environment and programming models. The Open Computing Language  [60]  introduces an open standard for general-purpose parallel programming of heterogeneous systems. An OpenCL program may target any OpenCLcompliant device and today many vendors provide an implementation of the OpenCL standard.
An OpenCL program comprises a host program and a set of kernels intended to run on a compute device. It also includes a language for kernel programming, and an API for transferring data between host and device memory and for executing kernels. Single node hardware design is shifting to a heterogeneous nature. At the same time many of today's largest HPC systems are clusters that combine heterogeneous compute device architectures. Although OpenCL has been designed to work with multiple devices, it only considers local devices available on a single machine. However, the host-device semantics can be potentially applied to remote, distributed devices accessible on different compute nodes. Porting single-node multi-device applications to clusters that combine heterogeneous compute device architectures is not straightforward and in addition it requires the use of a communication layer for data exchange between nodes.
Innovative programming for heterogeneous computing systems In recent years, heterogeneous systems have received a great amount of attention from the research community. Although several projects have been recently proposed to facilitate the programming of clusters with heterogeneous nodes  [54-59, 68, 69] , none of them combines support for high performance inter-node data transfer, support for a wide number of different devices and a simplified programming model. Kim et al. [56]  proposed the SnuCL framework that extends the original OpenCL semantics to heterogeneous cluster environments. SnuCL relies on the OpenCL language with few extensions to directly support collective patterns of MPI. Indeed, in SnuCL the programmer is responsible to take care of the efficient data transfers between nodes.
In that sense, end users of the SnuCL platform need to have an understanding of MPI collective calls semantics in order to be able to write scalable programs. Also other works have investigated the problem of extending the OpenCL semantics to access a cluster of nodes. The Many GPUs Package (MGP )  [69]  is a library and runtime system that using the MOSIX VCL layer enables unmodified OpenCL applications to be executed on clusters. Hybrid OpenCL  [68]  is based on the FOXC OpenCL runtime and extends it with a network layer that allows the access to devices in a distributed system. The clOpenCL  [59]  platform comprises a wrapper library and a set of user-level daemons. Every call to an OpenCL primitive is intercepted by the wrapper which redirects its execution to a specific daemon at a cluster node or to the local runtime.
dOpenCL  [55]  extends the OpenCL standard, such that arbitrary compute devices installed on any node of a distributed system can be used together within a single application. Distributed OpenCL  [54]  is a framework that allows the distribution of computing processes to many resources connected via network using JSON RPC as a communication layer. OpenCL Remote  [58]  is a framework which extends both OpenCL's platform model and memory model with a network client-server paradigm. Virtual OpenCL  [57] , based on the OpenCL programming model, exposes physical GPUs as decoupled virtual resources that can be transparently managed independent of the application execution. An innovative approach to program clusters of nodes composed of multi-core CPUs and GPUs has been introduced through libWater [71], a library-based extension of the OpenCL programming paradigm that simplifies the development of applications for distributed heterogeneous architectures.
libWater aims to improve both productivity and implementation efficiency when parallelizing an application targeting a heterogeneous platform by achieving two design goals: (i ) transparent abstraction of the underlying distributed architecture, such that devices belonging to a remote node are accessible like a local device; (ii ) access to performance-related details since it supports the OpenCL kernel logic. The libWater programming model extends the OpenCL standard by replacing the host code with a simplified interface. libWater also comes with a novel device query language (DQL) for OpenCL device management and discovery. A lightweight distributed runtime environment has been developed which dispatches the work between remote devices, based on asynchronous execution of both communications and OpenCL commands.
libWater runtime also collects and arranges dependencies between commands in the form of a powerful representation called command DAG. The command DAG can be effectively exploited to improve the scalability. For this purpose a collective communication pattern recognition analysis and optimization has been introduced that matches multiple single point-to-point data transfers and dynamically replaces them with a more efficient collective operation (e. g. scatter, gather and broadcast) supported by MPI. Besides OpenCL-based approaches, also CUDA solutions have been proposed to simplify distributed systems programming. CUDASA  [66]  is an extension of the CUDA programming language which extends parallelism to multi-GPU systems and GPU-cluster environments. rCUDA  [61]  is a distributed implementation of the CUDA API that enables shared remote GPGPU in HPC clusters.
Current HPC systems need to scale up by three orders of magnitude to meet exascale. While a sharp rise in the number of nodes of this magnitude is not expected, the critical growth will come from the intra-node capacities. Fat nodes with a large number of lightweight heterogeneous processing elements, including accelerators, will be common in the ultrascale platforms, mixing parallel and distributed systems. In addition, memory per core ratio is expected to decrease, while the number of NUMA nodes will grow to alleviate the problem of memory coherence between hundreds of cores  [16]. On the software side, weak scaling of applications running on such platforms will demand more computation resources to manage huge volumes of data. Nowadays MPI applications, most of which are built using the bulk-synchronous synchronization model  [11]  as a sequence of communication and computation over the interchanged data stages, will continue to be important, but multi-physics and adaptive meshing applications, with multiple components implemented using different programming models, and with dynamic starting and finalization of such components, will become common in ultrascale.
Data-centric approaches are needed for describing data in the system and apply the computation where such data resides  [12, 15]. Another critical challenge for MPI to support exascale systems is the resilience, a crosscutting issue affecting the whole software stack. Current checkpointing/restart methods are insufficient for future systems under a failure ratio of a few hours and in the presence of silent errors, and traditional triple modular redundancy (TMR) is not affordable in an energy efficient manner. New techniques of resilient computing have been proposed and developed, also in the MPI context  [5, 6]. One proposal for increasing resilience to node failures is to implement malleable applications, able to adapt their execution to the available resources in the presence of hardware errors, and avoiding the restart of the application  [7].
Alternatives to MPI come from the Partitioned Global Address languages (PGAs) and High Productivity Computing Systems (HPCS) programming languages. PGAs programming models provide a global view of data with explicit communication as CAF  [38]  (Co-Array Fortran), or implicit communication as UPC  [40]  (Unified Parallel C). However, static scheduling and poor performance issues make them currently far from replacing the well established and successful MPI+X hybrid model. Moreover, OpenMP presents problems with nested parallelism and vertical locality, so the possibility of MPI+PGAs has been, and continues to be evaluated  [8, 22]  to provide a programming environment better suited to the future platforms. HPCS languages, such as a Chapel  [63]  and X10  [64] , provide a global view of data and control.
Runtime requirements for data-intensive applications Developing data-intensive applications over exascale platforms requires the availability of effective runtime systems. This subsection discusses which functional and non-functional requirements should be fulfilled by future runtime systems to support users in designing and executing complex data-intensive applications over large-scale parallel and distributed platforms in an effective and scalable way. The functional requirements can be grouped into four areas: data management, tool management, design management, and execution management  [39]. Data management. Data to be processed can be stored in different formats, such as relational databases, NoSQL databases, binary files, plain files, or semi-structured documents. The runtime system should provide mechanisms to store and access such data independently from their specific format.