dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Active Data Active Data provides a non-invasive method of improving coordination and obtaining feedback from loosely coupled systems. Specifically, we use Active Data's life cycle model to individually represent and expose the internal life cycle of the three systems used in the APS experiment: Globus, Globus Catalog and Swift; and Active Data's composition ability to represent how data moves between them. In Active Data, the model of a system's life cycle is composed of places, represented by circles and transitions; places represent all the possible states of data; transitions represent all the legal changes of states. A set of directed arcs connect places to transitions, and transitions to places. Places may contain one or several tokens; each token represents a data replica, and the current place represents the current state of the replica.
C. APS Experiment Life Cycle Model We now present the life cycle model of data in the APS experiment. We use the notation A. B to mean transition B in system A. The complete model, shown in Fig. 2 , is divided into six parts, separated by composition transitions (in red). On the left, we represent the detector with a minimal life cycle, that is a CREATED and a TERMINATED place (the only two places that are required in any life cycle model), with a transition between them; The detector's CREATED place is connected to the life cycle of a Globus transfer with a composition transition. This special transition represents the moment when Active Data records the identifier in the destination system (Globus) and maps it to the identifier in the source system (the detector). In the Globus model, a token represents a transfer task containing a directory tree with several files.
A transfer task can succeed or fail. In the case of success, the composition transition creates a new token in the shared storage. The shared storage is also a minimal life cycle. From here, two things happen: a Python script registers each directory in the completed transfer as a dataset in the Globus Catalog and annotates it with metadata extracted from the files; and another Globus transfer copies each dataset to a computing platform for analysis. The order in which these two stages occur is not important in the model; the order in which both transitions are triggered will simply indicate which happened first. On the Globus Catalog model, metadata can be added to a dataset, and all metadata can be removed at once. On the Globus transfer model, a transfer can succeed or fail. If the transfer succeeds, the Start Swift composition transition can be executed for each file in the dataset.
When the composition transition is executed, it maps the Globus task identifier to the identifier of a file from the dataset (its path in the computing cluster). These files are used as input to the Swift script, that will in turn produce an output file. Swift features a self composition: each input file is naturally linked to the output file it produced by the Derive transition. An error in the Swift script results in the output file not being created and the Failure transition being triggered. In addition to the CREATED place, each independent life cycle contains a TERMINATED life cycle that is used to indicate to Active Data that a life cycle is complete and that the associated resources can be freed.
D. Event Capture Having represented the entire model with Active Data, information from an execution of the APS experiment has to be mapped to the model. This translates to creating a token in the model for every file, transfer and dataset manipulated, and then informing Active Data of transitions, i. e. , when a system alters the state of a file, transfer or dataset. In Active Data, this is called "publishing transitions. " We use several different strategies to instrument these systems to publish Active Data transitions. The methods for instrumenting the three systems-Globus transfers, Globus Catalog and Swift-are completely non-intrusive and reusable. Depending on the system's design, we either query updates from its API or derive data transitions from log files. Only user level scripts have been modified to publish transitions after some tasks are launched.
A. Monitoring Progress To monitor progress by observing all systems in the experiment at the same time, we have developed a sample user code that records (in a log file) when the experiment lifecycle is complete, that is, when a dataset from the detector is fully processed by Swift. In order to generate this record, we need to know how many files are contained in each dataset, what dataset a file belongs to, and how many files from each dataset Swift has processed. This user code is configured to run when files are transferred from the detector (transition Detector. Start transfer) and when Swift derives an output file from an input file (transition Swift. Derive). We present pseudocode for this sample in Algorithm 1. The code keeps two counters for each dataset, recording how many files it contains, and how many have been processed.
B. Automation We now demonstrate that tasks that were previously performed manually can be easily automated using our approach. In particular, we discuss the automation of two tasks. First, when a transfer to the shared storage completes, we automatically run the Python metadata extraction script. A user code is set to run after transition Globus Transfer. End transfer is triggered; this code examines the token produced by the transition and uses its identifier to run the Python extraction script directly. Second, when the transfer of a HDF file to the compute cluster completes, we execute a specific Swift analysis script with a user code that is set to run after transition Globus transfer. Start Swift is triggered. This code is similar to the [Subscribe the handler to both transitions] ad.
C. Error Detection and Recovery We finally consider the problem of detecting and recovering from experiment-wide errors. On occasion faulty files acquired by the detector are only identified during analysis, near the end of the process. In this situation, the measures taken by users are to drop the dataset entirely and to reacquire the dataset with the same (or fixed) parameters. This procedure can take a considerable amount of time and represents a significant overhead on the experiment process. However, the surveillance framework can detect these errors immediately by observing Swift failures and automatically take appropriate recovery measures, in addition to notifying the user. The user code benefits from the high-level view of the whole process to automatically recover from errors in the same way that the user would.
It is executed on a node of the shared storage cluster for any Swift token with a "failurecorrupted" tag. The code uses the Active Data client API to retrieve the representation of the life cycle of the faulty file, as previously shown in section V-A. The representation is used to access remote elements through their identifier. The code removes associated metadata from the Globus Catalog (which will trigger the Globus Catalog. Remove transition), removes the files from the shared storage, triggers Detector. End for the dataset and finally notifies the user via email. Finally, user code running on the detector's acquisition machine can react to the Detector. End transition being triggered by running the acquisition again.
Algorithm 1 Monitor the progress of the experiment life cycle. ad ActiveDataClient. getInstance() datasetSize [] datasetT reated [] function HANDLER(transition, isLocal, inToken, outToken) dsId "" [Count total number of files in the dataset] if transition = "Detector. Start transf er" then dsId inToken. getUid() size len(listFiles(dsId)) datasetSize[dsId] size [Count number of treated files in the dataset so far] if transition = "Swif t. Derive" then lc = ad. getLifeCycle(inT oken) createdT okens lc. getTokens("Detector. Created") dsId = createdTokens[0]. getUid() datasetT reated[dsId]+ = 1 [If done, update file] if datasetSize[dsId] = datasetT reated[dsId] then out open("aps. log") out. write("Done: " + dsId) out. close().
The idea is also to investigate the use of such approaches to specify testing procedures and related validation properties for assessing standards implementations. In this context, we propose to apply and extend the SIP methodology on business manufacturing data exchange business cases and particularly for enabling efficient data integration between ERP and MES systems. Section 2 hence provides a state of the art about enterprise modelling approaches languages and tools. Based on the literature, it also explains how the interoperability of business processes could be achieved by using models. A second sub-section of the state of the art present the used ISA-95 standard for ERP and MES systems integration. Section 3 introduce our proposal and illustrates how we managed to model different aspects of the ISA-95 standard.
For all these reasons, we propose a modelling methodology based on the TOGAF framework and the ArchiMate 5 enterprise architecture language due to its ability to model an enterprise system interrelating domain specific architectures and crossdomain relationships. The ArchiMate language divides the enterprise architecture into a business, applicative and technological layer. In each layer, three aspects are considered: active elements that exhibit behaviour (e. g. Process and Function), an internal structure and elements that define use or communicate information. The use of ArchiMate language in our methodology allows us to model different system architectures. First it is used to model the standard architecture itself and related test procedures. Secondly it used to specify business collaboration processes and their related applicative and technological chains.
However next section illustrates the way of performing this step using ArchiMate language to model ISA-95. Moreover, using models instead of text documents enables first to constitute a knowledge base of PLM standards models. These models should include a set of reusable standards-based collaboration templates of the different functional modules of the standard. A PLM standard can specify the supported business processes, as well as the product and process data models to implement in ICT systems. , we will have a multi-layer view with the modelling of the processes (organizational layer), the data models (applicative layer) and specifying the software solutions (technological layer). Cross this way, modelling the standard allow us to show the link between the different layers, the information flow between each layer.
Content and structure of the standard The ISA-95 standard is structured in five parts. The first part presents the models and terminologies for analysing and exchanging information between level 4 (ERP) and level3 (MES). The second part shows the data models in order to standardize the structure and the information flows defined by part 1. In part 3 ISA-95 defines the four operations group that it covers in manufacturing activities which are: Production, Maintenance, Quality and Inventory operations. Part 4 specifies the informational flow between the four types of operations defined in part 3. Finally part 5 standardizes the implementation format and transactions data messages which transit between ERP systems and MES systems.
Conclusion and future work This paper intends to demonstrate how the use of enterprise architecture modelling languages could contribute to better specify and prepare interoperability business cases. Extending the SIP methodology, this paper introduces a manufacturing case study for ERP and MES systems integration based on the ISA-95 standard. Some extract of the ISA-95 ArchiMate model are shown to illustrate the approach and the interest of reusing templates of these models to be re-used in different collaboration scenarios. Last section shows how the templates have been re-used and instantiated in the context of the DEKENZ ERP-MES integration scenario. The next steps will be:  To instantiate the ISA-95 object models with the concrete DEKENZ case study data (MBOM, Production schedules, etc.
).  To create the related B2MML files to understand and analyse this implementation solution in view to develop the future B2MML converters and quality checkers.  To create the related ISA-95 compliant transaction data messages.  Define the quality control procedures to assess the conformity to the standard and to verify the content of the exchanged B2MML files.  Perform the mapping with the IUT ERP data model and the MES systems data models in order to further specify and/or develop the B2MML converters.  Specify and/or develop the ISA-95 B2MML quality checkers.  Address the limitations of the ArchiMate language by proposing some extensions to the community. One of the target of this research work is to be able to simulate the collaboration on the SIP test bed and demonstrate the interest of using models to prepare, build, verify and validate enterprise interoperability with models.
Solving the integrability constraints Even with the improved method of subsection 4. 2, starting at weight 5 the size of the linear system encoding the integrability constraints grows to such extent that its solution becomes the most important computational challenge of the bootstrap programme. Let us now discuss the strategy we adopted for addressing this challenge, which required an efficiency beyond the capabilities of standard scientific software such as Mathematica or Matlab. After (or even before) partially reducing the integrability constraints in the form of subsection 4. 2 according to the discussion of subsection 4. 3, we may bring them to a more standard form by grouping all elements of the matrices of unknown coefficients C (1) , C (2)  into a column vector X, such that eq. (4.
Acknowledgments We thank  B. Basso ,  S. Caron-Huot ,  L. Dixon ,  A. Sever  and  P. Vieira  for comments on the draft, and acknowledge having benefitted from stimulating discussions with  J. Golden ,  E. Sokatchev  and  A. Volovich. The work of GP was supported by the  French National Agency for Research (ANR)  under contract StrongInt ( BLANC-SIMI-4-2011 ). The work of MS was supported by the  U. S. Department of Energy  under contract  DE-SC0010010. MS is also grateful to the  CERN theory group  for their hospitality and support. Open Access. This article is distributed under the terms of the Creative Commons Attribution License ( CC-BY 4. 0 ), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.
e. φ T k M φ l = δ kl , so from equations (4) and (  5 ) the total strain energy can be derived in the form : E def ν = 1 2 n ν=1 ω 2 ν q 2 ν (8) If the damping is assuming to be hysteretic; i. e. C = η ω K (9) where η is the unknown loss factor. One can easily show that at frequency ω ν , the modal damping q ν can be expressed in fonction of ξ ν by : q ν = -j φ ν f ω 2 ν η ν = -j φ ν f 2ω 2 ν ξ ν (10) Where : η ν = 2 * ξ ν. Equation (10) leads to the amplitude q ν , from which one can determine the energy dissipated in each joint. Hence, the energy dissipated in all joint leads to the estimated value of the total damping factor : ξ joints ν = 1 4π E diss E def (11) 3 Advantages and limitations of the energetic method The energetic method proposed has been implemented on a demonstrator as a model in Matlab.
Testbed based on SELinux Policies To validate our approach, a proof-of-concept has been developed based on Lil CMS (Lil CMS -The Easiest Content Management System, 2014). Lil CMS is a simple content management system that allows users to remotely change the content of a Web server. The contents in Lil CMS are stored in plain text files. Lil CMS is easy to integrate with any traditional LAMP (Linux, Apache, MySQL and PHP) Web server. Our proof-of-concept, available at  (Belhaouane et al. , 2015) , allows us computing the set of metrics described in this paper, upon our adapted version of Lil CMS (assumed to be administered via SELinux policies on a Web server). Our proof-ofconcept provides some representative SELinux (Security Enhanced Linux  (McCarty, 2004; Mayer et al. , 2006) ) policies, described according to the different SELinux access control models, and with the purpose of putting in practice the metrics defined in Section 4.
CONCLUSION We introduced a series of metrics that evaluate the comprehensive complexity of understanding an access control policy. We started by describing a concrete example about the policy complexity concept. We then formalized basic concepts of authorizationbased access control models, and presented our definition of policy complexity. Based on our definition, we provided a set of metrics to evaluate such a characteristic. The provided metrics were validated via a content management system (CMS) policy example. The approach has also been validated on a real environment using a general-purpose CMS controlled by SELinux (Security Enhanced Linux) policies. The results indicate that our metrics give a real estimation of the complexity of policies. Our metrics provide a tool for evaluating the complexity of understanding a policy by administrators.
This overrepresentation is something quite common for top selling artists and big names of the musical industry, but it is rather unusual for a "regional roots music" 13 artist such as Keith Frank. If we take a closer look at Frank's activity on these different websites, we can see that they all serve a specific purpose but that they are not strictly separated: there is a certain fluidity in the circulation of information and digital data shared on the websites. The ReverbNation, Facebook and Twitter accounts are thus interconnected, and so is the mobile application with Twitter and Facebook. Yet, all the websites are not used in the same way or with the same frequency. Between 2003 and 2009, Myspace used to be a fixture for musicians who wanted to share their music and build a strong fan base but the site has been steadily declining since 2009, partly because of the growing popularity of Facebook and its neat, easy to use and (then) ad-free interface.
With no technical skills required and for $149 a month, Mobile Roadie provides users with basic tools to build and customize an app (template, icons, features, analytics, etc) and it most importantly takes care of the App Store's complex approval process. 20 Even if Mobile Roadie's entry package ($149 a month; the pro version, with unlimited features and a personal account manager is $799 a month) is affordable, it still represents a substantial investment for Frank and this is why the application is heavily advertised on his social networking accounts with several Facebook posts a month luring the fans to download the app with the promise of exclusive content and gifts: Got that ZydecoBoss App? Download the FREE ZydecoBoss mobile app to your iPhone or Android phone by searching the app store for "Keith Frank".
Numerical method The numerical simulations were carried out using the computational fluid dynamics code Fluent  (Fluent™, 2001, website) , coupled with external subroutines (userdefined functions, UDFs) developed in C++ to implement Peppley's chemical model. A segregated solver, second-order upwind discretization scheme with steady and implicit linearization was used, see  Patankar (1980). Pressure-velocity coupling is achieved by the SIMPLE algorithm. The grid independence was examined in preliminary test runs, with the operating conditions given by  Peppley et al. (1992a-b) , as presented in table 2. The mesh size effect was evaluated for the methanol steam reforming reactor at a temperature of 553 K, with number of grid cells 75600, 84000 and 94700. The fraction mole deviations of CH 3 OH and H 2 for grids 75600 and 84000 are 5% and 26%, and for the grids 84000 and 94700 are 0.
Spherical Deconvolution (SD) is a class of multi-compartment reconstruction technique that can be implemented using both parametric and nonparametric signal models  [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49]. SD methods have become very popular owing to their ability to resolve fiber crossings with small inter-fiber angles in datasets acquired within a clinically feasible scan time. This resolving power is driven by the fact that, as opposed to model-free techniques that estimate the diffusion Orientational Distribution Function (ODF), the output from SD is directly the fiber ODF itself. Among the different SD algorithms, Constrained Spherical Deconvolution (CSD)  [39, 40]  has been received with special interest due to its good performance and short computational time.
To compare the relative performance between the SD methods based on Gaussian and non-Gaussian noise models, and their respective implementations including the TV regularization, the different algorithms were applied to several synthetic phantom datasets which had been contaminated with noise patterns mimicking the Rician and nc-χ noise distributions produced in multichannel scanners. To the best of our knowledge, this is the first evaluation of such methods in a scenario where Rician and nc-χ noise are explicitly created as a function of the number of coils, their spatial sensitivity maps, the correlation between coils, and the reconstruction methodology used to combine the multichannel signals. As a final analysis, the new method is also applied to real multichannel diffusion MRI data from a healthy subject.
For the SoS reconstruction, magnitude images were generated as: 2 1 n SoS k k S S = = ∑ , (15) where k S stands for the magnitude of k S. Notice that Eq. (  15 ) is the conventional SoS image combination and not the covariance-weighted variant. We have followed this approach in order to simulate the effect of any remaining residual correlation ρ present in real systems (we have assumed a ρ = 0. 05)  [63]. In the SMF reconstruction, magnitude images were generated as: 1 n SMF k k k S S C = = ∑ , (16) with simulated sensitivity maps depicted in. These sensitivity maps have been previously used in  [73]. It should be noted that different scanner vendors can implement different SMF and SoS variants. In this work we have used the variants given in  [55]  for datasets acquired without undersampling in the k-space, i.
Settings for the evaluation algorithms Both RUMBA-SD and dRL-SD methods were implemented using in-house developed Matlab code, applying the same dictionaries H created from the signal generative model given in Eqs. (  1 )-(  3 ). These used M = 724 fiber orientations distributed on the unit sphere, with a mean angular separation between adjacent neighbor vertices of 8. 36 degrees, and a standard deviation of 1. 18 degrees. To assess the effect of using dictionaries with optimal and non-optimal diffusivities, two different dictionaries were created and applied to the datasets described in subsections 1 (i. e. , fiber bundles with different inter-fiber angles) and 2 (i. e. , fiber bundles with different volume fractions). The first dictionary was generated by using the same diffusivities employed in the synthetic data, whereas the second dictionary was created from diffusivities estimated in regions of parallel fibers (outside the fiber crossing area) by means of a standard diffusion tensor fitting on the noisy data (i.
Real brain data Diffusion MRI data were acquired from a healthy subject on a 3T Siemens scanner (Erlangen) located at the University of Oxford (UK). The subject provided informed written consent before participating in the study, which was approved by the Institutional Review Board of the University of Oxford. Whole brain diffusion images were acquired with a 32-channel head coil along 256 different gradient directions on the sphere in q-space with constant b = 2500 s/mm 2. Additionally, 36 b = 0 volumes were acquired with in-plane resolution = 2. 0 x 2. 0 mm 2 and slice thickness = 2 mm. The acquisition was carried out without undersampling in the k-space (i. e. , R=1). Raw multichannel signals were combined using either the standard GRAPPA approach or the GRAPPA approach with the adaptive combination of the SMF available in the scanner, giving SoS and SMF-based datasets respectively.