dataesr/scientific-paragraphs-categorization huggingface dataset:
zip. In the first table of Figure  4 , the evolution of run time from 10 to 1,000,000 traces (which is arbitrary chosen as upper bound of the scalability of the experiments) shows that, in practice, learning with LFkT is linear in the number of traces when the number of variables is fixed. Results show that the algorithm can handle more than one million of traces in less than 10 h. Since each trace is a sequence of five state transitions, when learning the Markov(5) system, each trace can be decomposed into 15 interpretation transitions (one 5-step, two 4-step, three 3-step, four 2-step, and five 1-step). Learning the Markov(5) program from one million traces of executions of size five requires the processing of 15 million of interpretation transitions. Learning the Markov(4) to Markov(1) programs requires to process, respectively, 14 million, 12 million, 9 million, and 5 million of interpretation transitions.
FURTHER WORKS Further works aim at adapting the approach developed in the paper to the kind of data produced by biologists. This requires connecting through various biological databases in order to extract real time series data, and subsequently explore and use them to learn gene regulatory networks. On account of the noise inherent to biological data, the ability to either perform an efficient discretization of the data or to include the notion of noise inside the modeling framework is fundamental. We will thus have to discuss the discretization procedure and the robustness of our modeling against noisy data and compare it to existing approaches, like the Bayesian ones  (Barker et al. , 2011). Regarding the model, we consider extending the methodology to asynchronous semantics. Garg et al.
The modern human is getting more and more mobile having access to online services by using mobile cutting-edge computational devices. In the last decade, the field of context-aware services had led to emerge several works. However, most of the proposed approaches have not provided clear adaptation strategies in case of unforeseen contexts. Dealing with this last at runtime is also another crucial need that has been ignored in their proposals. This paper aims to propose a generic dynamic adaptation process as a phase in a model-driven development life-cycle for context-aware services using the MAPE-K control loop to meet the runtime adaptation. This process is validated by implementing an illustrative application on FraSCAti platform. The main benefit of the proposed process is to sustain the self-reconfiguration of such services at model and code levels by enabling successive dynamic adaptations depending on the changing context.
Related Work The software adaptation in mobile and ubiquitous computing has been treated by several works in the literature  [6]. This work is focused on model-driven approaches proposed for developing and adapting context-aware services. The viewpoint of contextaware adaptation as a crosscutting concern in core service-based application is not new. It has been used by some works at model and/or code levels. To the best of our knowledge,  [7]  and  [8]  are only two works that combine MDD with AOM to develop context-aware applications at model level. However, a few works are proposed at code level. They have combined AOP  [9]  and MDD to tackle the complexity of context-aware applications development. In  [10] , Prezerakos et al. proposed to deal with core service logic and context-aware adaptation as separate concerns using a modified version of ContextUML  [11]  and aspects to encapsulate context-dependent behaviour in discrete AspectJ code modules.
In what follows we will see how this service in question is going to behave. To exploit this reflection principle for realizing the dynamic adaptation in our paper's scope, the context-aware service can be considered as an autonomic system with the MAPE-K control loop which consists to monitoring, analysis, planning and execution activities by sharing knowledge between them  [23]  (Figure  2 , M0 level). Currently, the MAPE-K suggested by IBM in 2003 represents the reference model for many self-adaptive software development works like in  [24].
Planning The selected ContextAspect model will be converted by an M2T transformation to generate FPath and FScript codes  [27] , requisite to reconfigure the running application on FraSCAti platform. The pointcut expressions of this model are transformed into FPath code allowing the navigation in the architecture of the running FraSCAti-based applications to search the impacted weaving elements. Furthermore, the advice is transformed into FScript code which is a scripting language dedicated to architectural reconfiguration of such applications  [28]. Generally, a reconfiguration (or dynamic adaptation) consists of two main steps: (1) to find the context-aware places matched by pointcut through FPath code, and (2) to weave the modifications expressed in advice and coded by FScript. The modifications' weaving will be planned in actions conforming to a Weaving metamodel and will be directed by an algorithm and rules that will not be presented here.
For each place impacted by FPath, this weaving should go through the following actions: 1. stopping the running FraSCAti components (or composite); 2. removing all components and wires relevant to the advice script of the ContexAspect model already woven (if any); 3. adding components and wires for weaving the script of the new advice element; 4. and finally, restarting the stopped components. On the other hand and at the same time, an adaptation at model level should be made. Again, the composition The dynamic adaptation at model level can be enabled using models@runtime  [16]  in order to establish a causal connection between the model and the running application. Also, this adaptation can serve to validate the reconfigured context-aware service by automatic CPIM to CPSM to code transformations if they will be available.
Applying on the SAMU example and by an M2T transformation, the selected Evacuation ContextAspect model will be converted to FPath and FScript codes for generating the needed reconfiguration scripts to be applied on the toEvacuatePatient service. The pointcut expression is transformed into FPath code (toDisplayJP=$root/descendant-orself::*[name(. )=='toEvacuatePatient']) which selects toEvacuatePatient Service Component to be adapted without affecting toReceiveCall one, which continues its execution. Using weaving actions, the advice is as well transformed into FScript code as a reconfiguration script for weaving Evacuation ContextAspect. In Figure  6 , the script listing implements a weaving operation: Firstly, it brings the application in a quiescent state (line 2), and then creates an instance of three service components, namely: SetDepartureItinerary (line 4),.
Conclusion Context-aware services are a promising technology to construct personalised pervasive applications. Besides the importance of how to build such services, their context-aware adaptation during the execution time represents a challenge to overcome. The present article proposed a model-driven dynamic adaptation process at runtime respecting the MAPE-K control loop. This process consists to weave at model and code levels necessary adaptations specified in ContextAspect models into the running application according to dynamic context change. The aspect weaving enables to produce a wide range of context-aware services models without designing them from the beginning. An illustrative case study is implemented using the FraSCAti platform, so smoothly achieving this runtime adaptation. Our future work will be on evaluating the execution time taken by this process.
The pointcut expression is transformed into FPath code (toDisplayJP=$root/descendant-orself::*[name(. )=='toEvacuatePatient']) which selects toEvacuatePatient Service Component to be adapted without affecting toReceiveCall one, which continues its execution. Using weaving actions, the advice is as well transformed into FScript code as a reconfiguration script for weaving Evacuation ContextAspect. In Figure6, the script listing implements a weaving operation:.
Yet, there is a way to carry out systematic and reproducible tests under controlled circumstances, and to capture a large segment of the complexity of these largescale flows through relatively simple, tabletop-size experiments, based on the principle of hydrodynamic similarity. Under laboratory conditions it is possible to adjust the governing physical parameters and thus to separate different processes that cannot be studied independently in the real atmosphere. Therefore, laboratory experiments provide a remarkable test bed to validate numerical techniques and models aiming to investigate geophysical flows. This was one of the primary goals of the German Science Foundation's (DFG) priority program MetStröm. Research focuses on the theory and methodology of multiscale meteorological-fluid mechanics modelling and accompanying reference experiments supported model validation.
Experimental apparatus and procedures The laboratory experiments of the present study have been conducted in the baroclinic wave tank of BTU CS. This tank was mounted on a turntable, and was divided by coaxial cylindrical sidewalls (Fig. 1 ) into three sections. The innermost compartment (made of anodized aluminum) housed coolant pipes in which cold water was circulated. The temperature in this middle cylinder was monitored via a digital thermometer and kept constant by a thermostat with a precision of 0. 05 K. The outermost annular compartment contained heating wires and water as heat conductive medium. Here four thermometers (identical to that of the middle cylinder) provided temperature data for a computer-controlled feedback loop to maintain constant temperature (for the technical details on the applied control methods we refer to the paper of  VON LARCHER et al.
Note, that in order to enable the standard initialization procedures at the end parameter points Ω min and Ω max , the initial value Ω i was set smaller than Ω min or larger than Ω max , when required. However, no data acquisition took place at these "outof-range" parameter points. The infrared camera was mounted above the middle of the tank and was fixed in the laboratory frame (not co- rotating). In every ∆t = 2 s, 640 × 480-pixel thermographic snapshots were taken, providing a precision of around 0. 03 K for temperature differences. The obtained temperature fields can be considered surface temperature patterns, since the penetration depth of the applied wavelength into water is measured in millimeters. The captured snapshots were acquired and stored by a computer, where they were converted to ASCII arrays (by organizing the temperature values from all pixels into matrix format) for further evaluation.
, 2014). To trigger the formation of baroclinic waves, low amplitude random perturbations were added to the temperature field, with a maximum amplitude of δT pert = 0. 03|T b -T a |. This 3d simulation took further 10800 s, so that the waves could fully develop. A subsequent integration time of 7200 s (2 hrs) at maximum was used to record the data analysed in the present work. For further information on this initialization method, we refer to  BORCHERT et al. (2014). (ii) Spin-up simulations: In these cases an initial angular velocity Ω i and a final angular velocity Ω f > Ω i were chosen. The time evolution of the angular velocity Ω(t) was then computed according to the formula: Ω (t) = Ω i + Ω f -Ω i 2 1 -cos π t τ , t ≤ τ Ω f , t > τ , (2. 8) where τ means the spin-up or spin-down period.
HiFlow 3 HiFlow 3 is a multi-purpose C++ finite element software providing tools for efficient and accurate solution of a wide range of problems modeled by partial differential equations (PDEs), cf. HEUVELINE (2010);  HEUVELINE et al. (2012). It follows a modular and generic approach for building efficient parallel numerical solvers and introduces parallelity on two levels: coarse-grained parallelism by means of distributed grids and distributed data structures, and fine-grained parallelism by means of platform-optimized linear algebra back-ends (e. g. GPU, Multicore, Cell, etc. ). Further information about this open source project can be found on the project's website http://hiflow3. org/. For the baroclinic wave tank scenario the governing equations (2. 4) to (2. 6) were considered in cylindrical coordinates in a non-rotating frame, thus the Coriolis term of (2.
5; 8. 0; 10. 5; 20. 0 rpm), to enable the same sequential simulation process as described for cylFloit. The data from these points, however, were not evaluated in the present study. In the case of the HiFlow 3 simulations, letters "b" and "p" denote the basic and perturbed states obtained for the given rotation rate, respectively. In the "p"-runs additional azimuthal random perturbation was added to the initial condition (described in the previous section). In the cases of #2 and #3, the perturbed initial state led to a solution different from the basic state, but no such behavior was found for #1 and #4. This is in qualitative agreement with the laboratory results, since all of these metastable states were found within the hysteretic regime. It is to be noted, that the LESOCC2 and HiFlow 3 models exhibited m = 2 at #2 in their spin-up and perturbed series, respectively, besides the (experimentally verified) m = 3 mode.
EULAG and INCA always converged to one of the experimentally detected states within the regime of baroclinic instability (#2 to #4). For the data point #1 close to the critical transition point, INCA found m = 2, and EULAG showed an irregular pattern with fluctuating amplitudes at m = 2 and m = 3 (denoted with 2 -3I in Table  6 ), see also VON LARCHER and D ÖRN-BRACK (2014) in the present issue. These findings are seemingly in contradiction to the axially symmetric solutions of the rest of the models. It is important to remark, however, that the exact experimental value of Ω crit is hard to determine. At Ω = 2. 26 rpm the flow in the laboratory tank was clearly axially symmetric, and at the next measured data point (Ω = 3. 19 rpm) the first baroclinic wave pattern with m crit = 2 has already emerged.
Average temperature variance As a measure of the overall spatial thermal variability in the azimuthal direction, the (spatial) standard deviation of the mid-radius temperature profile was determined at each time instant. Next, the (temporal) average of these values -denoted by σ -was calculated for the whole quasi-stationary part of the given (either experimental or numerical) run. The obtained values are shown in Fig. 8  notation Table  6 : Dominant wave numbers of the "benchmark" data points, as obtained in the experiment and by the numerical models. Arrows ↑ and ↓ mark spin-up and spin-down initial conditions, if applicable. b marks the basic and p denotes the perturbed initiation states in the HiFlow 3 simulations. Note, that ∆T = 8 K was set constant for all the measurements, therefore the rotation rate Ω was the only variable "environmental" parameter.
as a function of the rotation rate Ω. In the graphs corresponding to those numerical simulations, where the onset of baroclinic instability was captured, this "phase transition" manifests itself in the form of a marked jump in σ. Note, that EULAG and INCA found dominant modes of non-zero m already at the benchmark point #1, therefore in their graphs no such jump is present. Since the basic state is axially symmetric and the analyzed data were extracted from a circular contour of a constant radius r mid , it is trivial that the numerical models give practically zero variance in this regime. However, due to random temperature fluctuations, the laboratory experiments (green and red curves for the spinup and spin-down series, respectively) showed considerably larger (yet, minimum) values of σ in this regime.
Fig. 6a ). In this sequence also a similarly sharp drop of σ can be observed at Ω = 10 rpm, which is not accompanied with the change of the dominant wave number m = 3. However, as it will be demonstrated in the next subsection, this decrease coincides with a similarly sharp change in the drift rates of the baroclinic waves, thus implying a certain state transition, even though not in terms of m. Despite the qualitative similarity, the cylFloit and INCA runs (blue, magenta and orange curves) systematically overestimate σ by around a factor of 2. This, however, may well be the consequence of the fact that the temperature fields of these models are extracted from the height level of z = 10 cm (whereas D = 13. 5 cm). The plotted data from LESOCC2 and EULAG (brown and black data points) on the other hand were extracted from the uppermost (surface) grid level.
This observation underlines the extreme sensitivity of the studied system to the upper boundary condition, and thus gives a broader context to our comparisons with the numerical results, which now follows. The c m values, obtained from the cylFloit data are shown with magenta and blue curves in Fig. 10a  and c , representing the drift rates in the spin-up and spin-down series, respectively. Also, the results from the "from scratch" series (always initiated from the stable m = 0 state) are plotted with a blue graph. Figure  10b  and d  show the drift rates found in the LESOCC2 (spin-up and spin-down), INCA and EULAG simulations. The general decreasing trend of drift rates were captured by the investigated models, and the drift rates of cylFloit, INCA and LESOCC2 are in fairly good agreement with each other, yet, neither the experimentally obtained, nor the thermal wind-type scaling was reproduced by them.
The results presented in this paper have clearly demonstrated that the relatively simple rotating annulus arrangement indeed provides a remarkable test bed to verify and tune numerical methods aiming to model large-scale atmospheric flows. The authors think that the presented pool of experimental and numerical data and the applied evaluation methods and "test quantities" will also prove useful benchmarks for similar studies in the future. grant Ac71/4-2. The INCA team (S. R. and S. H. ) was supported by DFG grant HI1273-1, and the computational resources were provided by the HLRS Stuttgart, under grant TIGRA. The EULAG numerical data were generated using resources of the Department of Mathematics and Computer Science, Freie Universität Berlin, Germany. Computing time for the results obtained with LESOCC2 (C.
Acknowledgements This work has been funded by the  German Science Foundation (DFG)  and is part of the  DFG priority program MetStröm  ( SPP 1276 ). The experimental team (U. H. , M. V. , K. D. A. and C. E. ) is grateful for the technical help of  H. -J. Pflanz ,  R. Stöbel  and  Y. Wang  throughout the period of the whole  MetStröm program. The Hiflow 3 team (M. B. , T. B. and V. H. ) gratefully acknowledges the computing time granted by the  John von Neumann Institute for Computing (NIC)  and provided on the supercomputer JUROPA at  Jülich Supercomputing Centre (JSC). The work of the HiFlow 3 team was partially supported by grant  HE4760/3-3  (DFG). The cylFloit team (S. B. and U. A. ) were also financed through DFG.
The Concept of Energy One part of the SemanticHPST project is dedicated to the concept of energy. Our aim is to create an ontology of energy for researchers working in the field of HPST as well as for science teachers. For researchers, the ontology aims at making available a methodical body of knowledge that allows previously unseen connections to be made. For example, correspondence between two authors or the presence of a specific term or concept in a text will allow researchers to put forward hypotheses regarding the emergence of an idea or the cross-fertilization of ideas. For teachers, the ontology aims at acting as a resource, allowing educators to find historical information relevant to school curricula as well as ideas for specific activities to carry out in the classroom. The content consists of reference texts in the field of HPST, contemporary scientific texts and a database of historic scientific instruments and documents.
These elements led to the structure of an initial mind map. -Finally, based on this mind map (created with Docear), we used Protégé software to create a first draft overview of the project. The next steps require documenting these three steps in detail to refine the data and then build the ontology. During the stages the project carried out so far, various problems have been identified that must be resolved. One of the main problems concerns the modeling of time. How can an event be modeled? Moreover, how can knowledge be modeled in a way that avoids immobilizing the knowledge? How should knowledge be contextualized? What approach should be adopted when modeling concerns a concept or an object? How can a coherent and logical body of content be created and how can its coherence be assessed? It is clear that the question of time as well as how to approach the treatment of objects and works are issues to be investigated in the semanticHPST project.
This platform will be mainly based on the Semantic Web and Linked Data approaches (RDF Triple Store, ontologies, OWL 2, RDFS, SPARQL, etc. ). Nevertheless, the platform will also provide access to non-semantic resources. A network of ontologies dedicated to HPST will be designed to meet the interoperability and open access requirements for corpora. Some existing ontologies and standards will be reused and integrated in the ontology network, like CIDOC-CRM, FRBRoo, FRSAD, Dublin Core, etc. and those available at LOV (http:// lov. okfn. org/dataset/lov/). In this paper, we focus our attention on the resource retrieval problem that we can divide into two different aspects : advanced search in HPST corpora and harvesting distributed corpora. The former focuses advanced search functionalities in a single corpus.
Advanced Search in HPST Corpora In order to perform advanced searches in a HPST corpus, we have to build intelligent digital corpus: corpus with primary and secondary sources having semantic metadata (RDF Triples) and their corresponding ontologies using a fragment of OWL (actually, RDFS will be sufficient for the following examples). These ontologies are domain ontologies related to the corpus. A domain ontology for Henri Poincaré letters has already been designed. Finally, some tools will have to be developed for answering some of the queries. This section presents the advanced search using the query examples Q 3 -Q 6 introduced in Sect. 2. 2. Q 3 requires some additional data and knowledge to get satisfactory answers, as stated in Sect. 2. 2. In particular, if the annotation file contains the following RDFS triples: (letter1 isSentBy rodolphe radau) (rodolphe radau rdf:type Geodesist) (Geodesist rdfs:subClassOf Astronomer) then the execution of the following SPARQL query on an engine supporting RDFS Q 3 = select ? where {? isSentBy ?a.
It was firstly dedicated to the technology-enhanced learning domain  [12]. The core part of the toolkit can be reused for HPST. It fulfills the needs of researchers in HPST, that is to say it enables us to federate distributed sources and tools. SMOOPLE has semantic services which are in charge of managing incorporated semantic models, extracting and storing the data produced on social media tools, making and answering to semantic queries against one or several distributed sources (federated queries). The Semantic Web server (semantic services) is based on Jena 2. When the social media tools do not have a triple store and a SPARQL endpoint, content and corresponding semantic metadata can be extracted on the fly from social media applications, by means of plugin (similar to sioc export) and stored in a RDF repository.