dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall Language Architecture The MODACloudML architecture is inspired by the OMG Model-Driven Architecture (MDA)  [5] , which is a model-based approach for the development of software systems. The MDA relies on three types of models for three layers of abstractions. The closer to the system a layer is, the more technical the description. These three MDA layers, from the more abstract to the more detailed, are: • The Computational Independent Model (CIM), which describes what the system is expected to do but hides all the technical details related to the implementation of the system. • The Platform Independent Model (PIM), which describes views of the systems in a platform independent manner so that it can be mapped to several platforms at the PSM levels. • The Platform Specific Model (PSM), which refines the PIM with technical details required for specifying how the system can use a specific platform.
Requirements Model: completes and formalizes the service functional description. Business and QoS requirements can be associated to a Service or to a specific service operation. Data Model: describes the main data structures associated with the software to be. It can be expressed in terms of typical Entity Relational (ER) diagrams and enriched by a metamodel that specifies functional and non-functional data properties. QoS Model: includes information concerning expected QoS characteristics (e. g. , response time) at the application level. QoS contraints can be attached to specific application component/services. In the following we exemplify the usage of the Service Orchestration models to specify the overall architecture of the SensApp case study.
Example At the CCIM level, an application is described as a set of high level services following a Service Oriented Architecture (SOA)  [6]. The application is specified as a set of business-aligned reusable services that can be combined into high-level business processes and solutions within the context of an enterprise. Figure  3. 3 depicts a simple functional architecture of the SensApp case study specified with the MODAClouds IDE as a Service Orchestration model. SensApp  [7]  is a typical Cloud-based application that acts as a buffer between sensor networks and Cloud-based systems. On the one hand, it facilitates sensors to continuously push data while, on the other hand, it provides higher level services with notification and query facilities. The overall architecture of SensApp consists of a core service called SensApp to manage the sensors and their data coupled with a MongoDB 1  database to store sensor descriptions and meta-data as well as the measurements.
• Hosting: Represents the fact that a component uses another as execution platform. In addition, CloudML implements the type-instance pattern  [11] , which also facilitates reusability. This pattern exploits two flavors of typing, namely ontological and linguistic  [12]. Figure  3. 4 illustrates these two flavors of typing. SL (for Small Linux) represents a reusable type of VM. It is linguistically typed by the class VM (for Virtual Machine). SL1 represents an instance of the VM SL. It is ontologically typed by SL and linguistically typed by VMInstance. The transformation from CPIM to CPSM consists in: (i) adding the actual data resulting from the resolution of the constraints defined in the external component types (e. g. , actual number of cores, RAM size, storage size), and (ii) adding data required for the deployment and management of the application that are Cloud provider-specific.
Example Figure  3. 5 depicts the deployment model of SensApp at the CPIM level specified with the MODAClouds IDE. The overall system will be deployed using two different virtual machines (VMs), the first VM will host SensApp and the second the SensApp Admin. Both VMs (CloudNodeInstance and ML) have differents characteristics and are thus specified as instances of different types (SL and ML). Both SensApp and its admin, in order to be executed properly, have to be hosted in a Servlet container. In this case they are both hosted on the same type of Jetty container called JettySC. This type of relationship is depicted in the figure by arrows between blue ports. In addition, SensApp has to communicate with the database in order to store and retrieve sensors data. This type of relationship is depicted by arrows between purple ports.
Related Work In the literature several efforts aimed to offer support for designing, optimizing and managing multi-Cloud applications. In particular, several EU projects provide methodologies and tools to support the design and management of Cloud-based applications. However, to the best of our knowledge, none of them propose an integrated approach offering models that can be used for performance and cost analysis and optimisation, as well as deployment and runtime management of multi-Cloud applications. The Cloud Application Modeling Language (CAML)  [13]  is being developed within the ARTIST EU FP7 project 2  and supports the provider-independent specification of deployment topologies and their refinement into provider-specific deployment. The main focus of the ARTIST project being the migration of legacy application to the Cloud as well as the feasibility study of such migration, the language has been defined as an UML internal modeling language based on a model library and profiles.
This way, it can be directly applied on UML models, which is especially beneficial for migration scenarios where reverse-engineered UML models are tailored towards a selected Cloud environment. These CAML profiles also capture Cloud offerings from a functional and non-functional perspectives including cost aspects. In order to cover the necessary aspects of the specification and execution of multi-Cloud applications, the PaaSage project 3  adopts the Cloud Application Modelling and Execution Language (CAMEL). CAMEL integrates and extends existing DSLs, including Cloud Modelling Language (CloudML)  [8, 9] , Saloon [14, 15], and the Organisation part of CERIF [16], for specifying multiple aspects of multi-Cloud applications, such as provisioning, deployment, providers, organisations, users, and roles.
Moreover, CAMEL adds DSLs for specifying aspects such as metrics, requirements, goals, scalability rules [17, 18], security controls, execution contexts, execution histories, etc. CAMEL is designed and implemented with the Eclipse Modelling Framework (EMF)  4  on top of the Connected Data Objects (CDO) 5  persistence solution. MODAClouds and PaaSage are collaborating on the research and development of CloudML. However, PaaSage does not offer a specific approach for the designtime optimization of multi-Cloud applications. The Topology and Orchestration Specification for Cloud Applications (TOSCA) [19, 20] is a specification developed by the OASIS consortium, which provides a language for specifying the components comprising the topology of Cloud-based applications along with the processes for their orchestration.
Conclusion The MODAClouds Model-Driven Development approach relies on the so called MODACloudML which integrates a set of domain-specific languages. These languages cover the specifications of both functional and non functional aspects of multi-Cloud applications. Thanks to the three levels architecture, multi-Cloud applications can be designed in a Cloud provider-independent way thus reducing vendor lock-in before being refined with provider-specific information thus allowing to exploit the peculiarities of each provider. The images or other third party material in this chapter are included in the work's Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work's Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.
In this study, the experimental campaign presented is grounded on the use of a modified Arcan device using bonded specimen with low-edge effects (maximal stress state in the center of the adhesive). [25]  This reduction of the edge effects is allowed by a modification of the initial Arcan samples and consists in the use of substrates geometry. In the first section of the work presented, using such a device, experimental results under monotonic and creep, cyclic loadings are shown for a two-component polyurethane SikaForce®-7817 L60 MR adhesive. Then, a 3D material constitutive model is proposed for finite-element code implementation, allowing the analysis and the description of the mechanisms experimentally observed in creep in the adhesive. The second section of this document describes the assumptions made on the adhesive mechanical behavior, the components of the model and the inverse identification process of the material parameters.
The first mechanism with the shorter characteristic creep time (τ 1 < 10s) aims the definition of the loading rates influence, while the second mechanism aims the definition of creep effect. • Finally, K vp , m, R 0 and Q associated to b and a 2 are the material constants driving the visco-plastic flow and the flow direction. In order to model the bonding assemblies behavior, the constitutive equations were implemented in the Abaqus® FE code with a user material subroutine (UMAT).
Inverse identification The identification of the model using the modified Arcan test is based on an inverse process. The inverse identification method of the material parameters takes into account the non-uniform stress distribution along the adhesive overlap. [29]  This numerical method couples the numerical results, given by the Abaqus™ FE code, and the optimization software Python™ considering the non-linear optimization library NLOPT. Concerning the optimization works performed in this thesis, the algorithm used was based on an implementation of a globally convergent method of moving asymptotes (MMA) implementation. This algorithm was developed for gradient-based local optimization, including inequality constraints. The 'globally convergent' term does not mean that the algorithm systematically converges to the global minimum.
Failure criterion Using a modified Arcan device, under shear loading, the displacement at failure from the comprehensive experimental database underlined a value close to the adhesive joint thickness. This result was acceptable for both monotonic and cyclic tests for a given loading rate of 2 kN/s (Figure  9 (a)). Therefore, assuming that this observation is true, a failure strainbased criterion will be developed. In order to introduce a failure criterion, a User Output Variable (UVARM) called C f was created in the user material subroutine. This variable was calculated at each increment of the FE analysis and was defined by: with, where f eq , f H defined the equivalent and hydrostatic failure strains computed with the total strain tensor. A failure within the adhesive is considered as soon as the variable C f has raised a value above or equal to 1.
Acknowledgments The authors would like to thank  Sika Technology AG  for financing and supporting this project and supplying the adhesive material, and the  Brittany region  for financing this work. The authors are grateful to  S. Moyne  from  LBMS\ENSTA Bretagne  for his help on all the numerical aspects and  JY. Cognard  from  LBMS\ENSTA Bretagne  for opening the way for the commencement of this work.
We discuss the implications of an α-low, metal-rich population originating from the inner Galaxy, where the orbits of these stars have been significantly altered by radial mixing mechanisms in order to bring them into the solar neighbourhood. The probabilistic separation we propose can be extended to other data sets for which the accuracy in [α/Fe] is not sufficient to disentangle the chemical disc components a priori. For such datasets which will also have significant overlap with Gaia DR1, we can therefore make full use of the improved parallax and proper motion data as it becomes available to investigate kinematic trends in these chemical disc components.
The paper is organized as follows: Section 2 briefly describes the quality criteria and parameter cuts applied to obtain the final working sample, improvements on the data set, and methods to derive stellar kinematic properties used in the analysis. Section 3 describes the method used to disentangle the chemical disc components. Section 4 presents the kinematic trends for each component. We characterize the trends in mean V φ velocity for both the α-low and α-high populations, and discuss trends observed in the dispersions of all three velocity components. We also estimate the scale lengths of our chemically selected discs. In Section 5 we discuss implications for these findings, and in particular discuss possible origins of our metal-rich, α-low stars. Finally, Section 6 summarises our results.
Mean rotational velocity trends for the thin and thick disc components We find ∂V φ /∂[Fe/H] < 0 for the α-low component but ∂V φ /∂[Fe/H] > 0 for the α-high component. Quantitatively, for α-low stars, we find ∂V φ ∂[Fe/H] = (-11 ± 1) km s -1 dex -1. ( 8 ) This slope is significantly shallower than those of  Lee et al. (2011)  and  Adibekyan et al. (2013) , but using highresolution data from Gaia-ESO Recio-Blanco et al. (  2014 ) find, ∂V φ /∂[M/H] = (-17 ± 6) km s -1 dex -1 , which lies within 1σ of our value. For the α-high population, we measure ∂V φ ∂[Fe/H] = (51 ± 10) km s -1 dex -1. ( 9 ) This slope is in agreement with recent literature values. , from a highresolution sample of FGK stars from the Gaia-ESO survey. Fig. 5  shows the average V φ velocity as a function of [Fe/H] for the case where both discs are given equal weight (compare with panels c and d of Fig.
While high-resolution surveys will have a very small overlap with Gaia DR1, ∼ 3×10 5 RAVE stars are expected to be in Gaia DR1. Hence RAVE data combined with more accurate parallaxes and proper motions from Gaia DR1 should significantly sharpen, and hopefully confirm, the chemodynamical trends reported here and enable us to track more securely the extent and effect of radial migration in the Galactic discs.
ACKNOWLEDGEMENTS We thank the referee for their thorough comments and suggestions which have helped improve the quality of the manuscript. We also thank  Philipp Richter ,  Ivan Minchev ,  Friedrich Anders ,  Cristina Chiappini , and  Else Starkenburg  for their comments and helpful discussions, which have improved the quality and clarity of the text. Funding for this work and for RAVE has been provided by: the  Australian Astronomical Observatory ; the  Leibniz-Institut fuer Astrophysik Potsdam (AIP) ; the  Australian National University ; the  Australian Research Council ; the  European Research Council  under the European Union's  Seventh Framework Programme  (Grant Agreement 240271 and  321067 ); the  French National Research Agency ; the  German Research Foundation  ( SPP 1177  and  SFB 881 ); the  Istituto Nazionale di Astrosica at Padova ; The  Johns Hopkins University ; the  National Science Foundation of the USA  ( AST-0908326 ); the W.
FIRST PROTOTYPE We created a first interface implementing DRAG-AND-SNAP for the 2014 FIFA World Cup tournament (Figure  1 , left). We released it one week before the start of the first game (June 12 th , 2014) to let enthusiastics predict the outcome of the tournament. The purpose of this release was to validate the design, detect any usability flaw and collect qualitative feedback for further improvement. The prototype and its source code are available at https://github. com/romsson/ worldcup14-interactive-bracket. The prototype follows the above description of the technique and some domain specific designs (C7). The teams participating to the tournament have an initial position on the leftmost part of the screen, before they enter the group stage. The user can do a prediction by drag and dropping a team from the left to the right until it reaches a game (placeholder).
SECOND PROTOTYPE Based on the qualitative feedback we received for the first prototype, we designed and released an improved interface (Figure  4 ) for the 2015-2016 UEFA Champions League available at https://github. com/romsson/ ucl16-predictions. In this version, we improved the DRAG-AND-SNAP so that teams snap to the closest valid game (placeholder) instead of the closest prediction trajectory. We also added the possibility to reset individual teams (C4) by adding a small close button on each badge to reset it to its initial position. We also changed the timing of animations and added question marks within empty placeholders and the use of modals to explain features. Those changes aim at making the technique ecologically valid (C7), e. g. , as a standard website. Finally, we adapted the prediction space to the configuration of the UEFA Champion's League which slightly differs from the World Cup tournament structure: after the group stage, the games for every round are decided by draw, which increases the number of prediction paths for each team.
TECHNICAL NOTES We implemented the two prototypes using the JavaScript toolkit dragit  [10]  which uses D3 to handle mouse events. We used D3 for the representation of the trajectories and the snapping guides using SVG. Users can interact with the prototypes with simple mouse clicks and mouse moves, and both prototypes run in any recent web browser. As performance is key with direct manipulation interfaces, we generated an image containing all the trajectories and displayed it as background; we only highlighted user predictions using a SVG overlay (drawing each trajectory lines using SVG would have greatly impaired performance). One last technical decision was to make the prototypes stateful, which means that after the user interacts with the system, his current prediction can be recorded and retrieved wit the page's URL.
The only valid baseline would be to ask users in advance what their predictions would have been and if the interface was helpful. However, we claim that a user's prediction can sometimes be progressively formed as he or she is using the interface, and thus cannot be known in advance. To capture distant user activity, we used logging, a nonintrusing mechanism to capture in-the-wild users activity. This approach has been shown efficient to learn about users behaviors  [5]. Enthusiasts can interact with the system without any interruption or distraction related to the experiment. We instrumented the technique with a server-side mechanism to record key interactions from each visitor. Table  1  shows the list of events we recorded. We did not record every mouse moves as it would lead to too much data and would probably capture noise over signal.
DISCUSSION AND CONCLUSION The feedback we collected from sport enthusiasts who used the interface validates the design of our DRAG-AND-SNAP-based interface for making tournament predictions. We observed fully completed predictions from scratch and their sharing on forums. Based on both quantitative and qualitative data, we found that sport enthusiasts can effectively use a direct manipulation interface to perform predictions and share insights. Among the data we collected, we only analyzed successful predictions: we have discarded the unfinished ones. Exploring incomplete predictions data in more details would inform us on design issues that should be addressed to increase the rate of predictions completion. In the future, we plan to investigate in particular the reasons for visitors drop, as these may be diverse and difficult to hypothesize.
For instance the Pléiades-HR 1A & 1B and the Sentinel-2 geometric calibrations are based on this block matching algorithm. Over the years, it has become a common tool in ground segments for in-flight monitoring purposes. For these two kinds of applications, the two-dimensional search and the local sub-pixel measure without regularization can be essential. This tool is also used to generate automatic digital elevation models, for which it was not initially dedicated. This paper deals with the QPEC / Medicis algorithm. It also presents some of its CNES applications (in-orbit commissioning, in flight monitoring or digital elevation model generation). Medicis software is distributed outside the CNES as well. This paper finally describes some of these external applications using Medicis, such as ground displacement measurement, or intra-oral scanner in the dental domain.
INTRODUCTION QPEC / Medicis is a two-dimensional sub-pixel disparity measurement algorithm. It was created by CNES (Centre National d'Etudes Spatiales) in the frame of its optical earth observation missions. This tool can work on a real images pair, but also on a complex images pair (a Synthetic Aperture Radar images pair for instance). This block-matching algorithm is divided into a pixelwise step and a sub-pixel refinement step, which are described hereafter. This software is more versatile than most of the computer vision methods found in literature, as it can search the disparity in onedimensional, but also in two-dimensional. Its 2D search ability is not evaluated here, as most benchmarks are made of stereorectified images pairs. But its one-dimensional sub-pixel accuracy is evaluated on a benchmark, called Bergerie1, whose ground truth accuracy is better than a hundredth of a pixel.
ACKNOWLEDGEMENTS The authors would like to thank  Lionel Moisan  for allowing us to use the "Bergerie1" benchmark. We thank  Ana-Maria Rosu  for kindly giving us the input images and the parameter file used to generate the published Medicis preliminary tests results. We thank  Veronique Querbes  for the information on Condor®. We are grateful to  Hélène Vadon ,  Claudie Casteras ,  Damien Pichard ,  Guillaume Laurent ,  Carole Amiot  and  Jonathan Guinet  for their past or present work on QPEC / Medicis.
For 48 census pulsars such fits are being published for the first time. Typically, the choice between single and broken power-laws, as well as the location of the spectral break, were highly influenced by the spectral coverage of the available flux density measurements. In particular, the inclusion of measurements below 100 MHz appears essential for investigating the lowfrequency turnover in the spectra for most of the census pulsars. For several pulsars, we compared the spectral indices from different works and found the typical spread of values to be within 0. 5-1. 5, suggesting a prevailing underestimation of spectral index errors in the literature. The census observations yielded some unexpected individual source results, as we describe in the paper. Lastly, we will provide this unique sample of wide-band, low-frequency pulse profiles via the European Pulsar Network Database.
Census observations resulted in full-Stokes datasets spanning the frequency ranges of LO-FAR's high-band antennas  (HBA,  and, for a subset of pulsars, the low-band antennas  (LBA,. The information recorded can be used for investigating the emission properties of about 150 pulsars, with the possibility of both average and single-pulse analyses. Based on census data, the properties of the ISM can also be explored using dispersion measures (DMs), scattering times, and rotation measures (RMs). This paper presents the first results of the high-band part of the census project (analysis of the LBA data is deferred to subsequent work). Sect. 2 describes the sample selection, observing setup and the initial data processing for the HBA data. In Sect. 3 we discuss the source detectability versus scattering time and DM, and discuss the DM variation rates obtained by comparing census DMs to the values from the literature.