dataesr/scientific-paragraphs-categorization huggingface dataset:
An actor progresses by taking a message out of its queue and processing it by executing its corresponding method. A method is a piece of sequential code that may send messages. We model dynamic reconfiguration by deciding the message recipients based on actor states, but we do not consider dynamic creation of actors. We may use the terms actors and objects interchangeably. This model of concurrent computation forms the basis of the programming languages Erlang  [9]  and Scala  [30]  that have recently gained in popularity, in part due to their support for scalable concurrency. However, for optimal use of both hardware and software resources, we cannot avoid leveraging scheduling and performance related issues from the underlying operating system to the application level as argued for example in  [8, 11].
For our analysis, we have chosen to use Uppaal  [49] ; this choice is however not essential and in principle other tools for timed automata can also be employed. This framework complements the work in  [57] , in which we describe how application-level scheduling policies can be implemented into a programming language on top of Java. To exemplify our approach, we model in Sect. 3. 2 a peer-to-peer system with an architecture similar to that of Skype: a centralized broker is responsible for connecting the clients (aka peers). The goal is to ensure that the system will function quickly without delays. The analysis is difficult because this is a large distributed system; furthermore, the network of the connected peers may change dynamically. As seen in Fig. 1 , a behavioral interface provides an abstract and high-level view of the actor by abstracting from the queue and method implementations.
Actors as real-time asynchronous concurrent objects We describe in this section how to use automata theory, along the lines of our previous work  [38, 40] , to describe actors. Actors in our framework specify local scheduling strategies, e. g. , based on fixed priorities, earliest deadline first, or a combination of such policies. Realtime actors may need certain customized scheduling strategies in order to meet their QoS requirements. Our approach can be easily adapted to any actor-based modeling platform, e. g. , Rebeca  [1, 63] , Creol  [42] , which in turn may provide abstractions on programs in actor-based languages like Scala or Erlang.
Hence, their approach cannot address complications like delegation of a task to subtasks. Another related work is TAXYS  [21] , where an abstract model of the environment is used for schedulability analysis. However, it is used to analyze a complete program and is not used compositionally. Interface design Our behavioral interfaces are similar to Timed Interface Automata  [24] , but the notion of compatibility is different. Alfaro et al. [24]  take an optimistic approach in which two interfaces are compatible if there is a possible way for them to work properly. This leads to a simpler theory but to implement these interfaces, one needs to adhere to these possibilities to end up with a working system. David et al. [23]  suggest to make specifications input-enabled by adding an Error state and directing every undesired behavior to that state.
The integrated tool suite will span a complete software development cycle, and will be a basis for developing safety critical real-time distributed and embedded systems. The main advantage of such a tool suite is that the designer/programmer will gain direct control on scheduling in the whole development cycle, which is in turn the key to efficiency in the software running on future multi-core and cloud infrastructures. Nonetheless, it will in practice be based on a best-effort basis as true runtime guarantees depend on the exact operating system and the hardware. A specific and interesting case where we can apply our approach is in modeling TinyOS  [18, 35]. TinyOS is an actor-based open-source runtime environment designed for sensor network and has a large user base of over 500 research groups and companies  [18].
A further example of a transposed narrative is when the narrative structure, content, and sequencing are retained, but certain sequences, scenes, and shots are expanded and extended to accommodate longer broadcast slots, in which new characters, storylines, shots, and scenes are added along with other modifications for greater cultural proximity. This is the case with the Turkish version of Monk, Galip Dervis ¸. (2) (3) Derived Narrative: The narrative structure and content has been radically modified, while the adaptation may be based not on episode similarities such as in the previous cases, but are comparable based on genre similarities. Examples of this include the German adaptation of the US Dragnet series in the 1950s. Digital tools enable the collection of temporal data within a multimodal analysis to more precisely define and delineate boundaries between, and quantify each particular typological classification.
However, Adobe Premiere Pro is only able to do this for one video at a time, and can only assemble information on one aspect at a time, e. g. , primarily narrative structure. Atlas. ti, a program designed for collecting ethnographic data in print, audiovisual, and visual documents, can be used for segmenting multiple videos at the same time, and permits the segmenting and correlation of multiple modes and aspects of communication with each other. However, we have found this program, which is advertised as an instrument for qualitative analysis and works best with printed documents, to be prohibitively complicated for our purposes, especially when attempting to compile data from multiple modes and aspects of communication from the timeline. An additional weakness in the Atlas. ti program for our purposes was the inability to automatically compile the elapsed time of a particular segment into a column in an Excel table.
While the program calculates the elapsed time of the segments (whether 'quotations' or 'codes') on the timeline, this elapsed time must always be added to the file name manually in order for it to be incorporated into a column on an Excel table. Adobe Premiere Pro accomplishes this automatically through the segmenting function and is therefore easier to use. Through its 'network' function, however, Atlas. ti is, in theory, able to display and compare different kinds of visual information and their relationships to each other, something that will be demonstrated further below. Due to these limitations and the dearth of space for this article, we will dispense with a detailed description of using Atlas. ti for our comparative analyses and focus on the four other tools at our disposal. The most useful program for our work is the Multimodal Analysis (MMA) program, which, although it permits the analysis of only one single video at a time (for instance, one episode of the series), allows the application of multiple categories of data collection that none of the other programs are able to provide.
Compared to Atlas. ti, which also allows for multiple categories of collection of audiovisual data, the presentation and mapping of this data on the MMA timeline is more intuitive and better organized. Both Atlas. ti and MMA offer the possibility of user-generated categories of data collection, in the parlance of Atlas. ti these are known as 'quotations,' in MMA these are known as 'nodes. ' The keywords of each can also be user-generated in both programs, but only MMA offers a comprehensive number of default keywords called a 'library,' which can be modified or expanded according to the needs of the researcher. In our future projects, we will focus our attention on using the MMA program for our comparative analyses. In our work, the data derived from Adobe Premiere Pro, Atlas. ti, and MMA can be exported using the csv format to a Microsoft Excel  narratives are adaptations that retain the narrative content, sequencing, and structure of the source narrative with modifications of minor aspects to enhance the cultural proximity of the adapted version.
In other words, the distribution of temporal divergence (extensions, expansions, reductions, and deletions) among various different stages of the narrative structure usually shows traces and markers of the topics where the most extensive cultural re-appropriation takes place. The Multimodal Analysis (MMA) software program allows us to look at this distribution across the whole narrative. In this program, we gather our data by, first, developing catalogs and sub-catalogs that will be used to define each divergence at verbal, visual and acoustic, as well as narratological levels (see Figure  1  above). Next, using these catalogs and subcatalogs, we segment the video with nodes, which are time-stamped annotations (see Figure  2 below ). Segmenting a video clip on various aspects that include but is not limited to camera work, lighting, scoring, and dialog helps us disassemble each moment of the narrative structure into its individual modes.
At this point, the Quebecois show stops being a comedy and turns into a dramedy. Conversely, the French remain on a humoristic and almost vaudevillian tone to alleviate the drama and avoid serious societal implications. Video 5. French episode 1 Scene 32/Quebecois episode scene 21 dealing with drug issue -The French version is on the left, the Quebecois on the right. Go to the online version of this article to watch the video. Finally, the MMA Software enables a more detailed analysis as it provides a quantitative study of the camera work of the drug scene. Among other elements that can be measured, we can look at the camera angles and shot scales. Both elements are critical in filmmaking since they can add either dramatic or comic effects to the shows. Figure  10  shows that the French version contains exclusively eye level point of views (100%), which is quite neutral, while the Quebecois version uses slight high angles (22%) and low angles (26%), illustrating hierarchy and power relations between the The Quebecois show includes a significant amount of close-ups (32%) in order to focus on emotions (in this case anger and shame) and convey the tension that exists between the family members during the drug scene to the audience.
Conversely, the French version does not contain any close-ups but features medium close-ups (44%) or wider shots that downplay the anxiety and remain on a humoristic tone. This is just an example of the work that can be done with the MMA software, but many other parameters can also be studied under the light of multimodality and the program seems promising for future study. To conclude, the combination of Final Cut Pro™, Excel ™ and MMA ™ software prove to be efficient tools to analyze and compare TV series and their transcultural adapted versions in the context of transmutation. Final Cut Pro™ provides a new outlook on narrative structure re-organization as well as time management of scenes. The Excel chart connects the time management of scene with the topics they deal with and the MMA software provides an in-depth analysis of the style difference between both shows.
Contrary to the victim in the Stahlnetz episode, i. e. , the 'good guy' Emmanuel Fischer (Albert Kriwat), Jocko Harris is contextualized as the 'bad guy,' while the actual portrayal of both victims revealed surprising similarities in the mis-enscene. Differences lie in the contextualization, Friday's moral rejection of Harris in Dragnet, while the sympathetic nurse in Stahlnetz urged caution and empathy with Emmanuel Fischer. This can be seen in the dual video clips of both segments of Dragnet (video on the left, audio on the right) and Stahlnetz (video on the right, audio on the left), created with Adobe Premiere Pro™ and converted with Camtasia™. Using the video editing software, and especially Adobe Premiere Pro TM for segmenting the narrative structure and content for derived narratives such as the case for the German Stahlnetz episode and the US Dragnet example can uncover quantitative data related to the specific realizations of each version.
Abstract. Grassland management type (grazed or mown) and intensity (intensive or extensive) play a crucial role in the greenhouse gas balance and surface energy budget of this biome, both at field scale and at large spatial scale. However, global gridded historical information on grassland management intensity is not available. Combining modelled grass-biomass productivity with statistics of the grassbiomass demand by livestock, we reconstruct gridded maps of grassland management intensity from 1901 to 2012. These maps include the minimum area of managed vs. maximum area of unmanaged grasslands and the fraction of mown vs. grazed area at a resolution of 0. 5 • by 0. 5 •. The grassbiomass demand is derived from a livestock dataset for 2000, extended to cover the period 1901-2012. The grassbiomass supply (i.
The observationdriven trends of management intensity were found to be the dominant driver explaining the positive trend of NBP across Europe (36-43 % of the total trend with all drivers;  Chang et al. , 2016). That study confirmed the importance of management intensity in drawing up a grassland carbon balance. However, the national-scale management intensity and the identical history maps between 1901 and 1960 in that study carried several sources of uncertainty  (Chang et al. , 2015a). It implies that long-term history of large-scale gridded information on grassland management intensity is needed. The HYDE 3. 1 land-use dataset  (Klein Goldewijk et al. , 2011)  provides reconstructed gridded changes of pasture area over the past 12 000 years. Here, "pasture" represents managed grassland providing grass biomass to livestock.
This reconstruction is based on population density data and countrylevel per capita use of pasture land derived from FAO statistics  (FAOSTAT, 2008)  for the post-1961 period and assumed by those authors for the pre-1960 period. It defines land used as pasture but does not provide information about management intensity. To our knowledge, global maps of grassland management intensity history are not available. Recently,  Herrero et al. (2013)  garnered global livestock data to create a dataset with gridded grass-biomass-use information for year 2000. In this dataset, grass used for grazing or silage is separated from grain feed, occasional feed, and stover (fibrous crop residues). A variety of constraints have been taken into account in creating this global dataset, including the specific metabolisable energy (ME) requirements for each animal species and regional differences in animal diet composition, feed quality, and feed availability.
This grass-biomass-use dataset provides a starting point for constraining the amount of carbon removed by grazing and mowing (i. e. the target of grass-biomass use) and is suitable for adoption by global vegetation models to account for livestock-related fluxes. The major objective of this study is to produce global gridded maps of grassland management intensity since 1901 for global vegetation model applications. These maps combine historical NPP changes from the process-based global vegetation model ORCHIDEE-GM  (Chang et al. , 2013 (Chang et al. , , 2015b) )  with gridded grass-biomass use extrapolated from  Herrero et al. (2013). First, ORCHIDEE-GM is calibrated to simulate the distribution of "potential" (maximal) harvested and grazed biomass from mown and grazed grasslands respectively.
Model description ORCHIDEE is a process-based ecosystem model developed for simulating carbon fluxes, and water and energy fluxes in ecosystems, from site level to global scale  (Krinner et al. , 2005; Ciais et al. , 2005; Piao et al. , 2007). ORCHIDEE-GM  (Chang et al. , 2013)  is a version of ORCHIDEE that includes the grassland management module from PaSim  (Riedo et al. , 1998; Vuichard et al. , 2007a, b; Graux et al. , 2011) , a grassland model for field-level to continental-scale applications. Accounting for the management practices such as mowing, livestock grazing and organic fertilizer application on a daily basis, ORCHIDEE-GM proved capable of simulating the dynamics of leaf area index, biomass, and C fluxes of managed grasslands. ORCHIDEE-GM version 1 was evaluated and some of its parameters calibrated, at 11 European grassland sites representative of a range of management practices, with eddy-covariance net ecosystem exchange and biomass measurements.
, 2013 (Chang et al. , , 2015b)). The global TRY database gives SLA values for C4 grasses, of 0. 0192 m 2 g -1 dry matter (DM) (0. 0403 m 2 per g C with a mean leaf carbon content per DM of 47. 61 %;  Kattge et al. , 2011). Thus, we have set the value of SLA max = 0. 044 m 2 per g C for C4 grasses in ORCHIDEE-GM to fit the mean value from the TRY estimate, as we did previously for C3 grasses  (Chang et al. , 2013). The parameter Vc max 25 cannot be directly measured, but it is usually derived from A/C i curves in C3 or C4 photosynthesis models (C3:  Farquhar et al. , 1980; C4: Collatz et al. , 1992) , where A is the leaf-scale net CO 2 assimilation rate and C i the partial pressure of CO 2 in leaf intercellular spaces. Several researches provide observation-based estimates of Vc max 25  (Feng and Dietze, 2013; Verheijen et al.
, 2013 ; range of 24-131 µmol m -2 s -1 for C3 grasses and of 15-46 µmol m -2 s -1 for C4 grasses). Based on these estimates, we keep the value of Vc max 25 = 55 µmol m -2 s -1 previously calibrated in Europe for all C3 grasses and set Vc max 25 = 25 µmol m -2 s -1 for C4 grasses. These values may reflect neither differences in nitrogen and phosphorus availability between locations nor adaptation or species changes within a C3 or C4 grassland, but they are within the range of observations made under different conditions and consistent with values used by other terrestrial ecosystem models (Table  S1  in the Supplement). All other parameters of ORCHIDEE model are kept the same as in Trunk. rev2425. The parameter settings for grassland management module are in consistent with that in ORCHIDEE-GM v1  (Chang et al.
Grassland management intensity and historical changes Figure  1  briefly illustrates the procedures of combining model output, grass-biomass-use data, and grassland area data to reconstruct grassland management intensity maps. This section presents the procedures of the reconstruction in detail. Table  1  lists all variables shown in this section, including their abbreviations, units, related equations, and data sources. Herrero et al. (2013)  established a global livestock production dataset containing a high-resolution (8 km × 8 km) gridded map of grass-biomass use for the year 2000. In this study, this dataset is extrapolated annually over 1901-2012 to constrain the grass-biomass consumption in ORCHIDEE-GM v3. 1. Assuming that grass-biomass use for grid cell k in country j and year m (GBU m,j,k , unit: kg DM per year) varies proportionally with the total ME requirement of domestic ruminants in each country, GBU m,j,k can be calculated from its value of the year 2000 given by  Herrero et al.
Grassland NPP observation database NPP is a crucial variable in vegetation models and it is essential that this variable is properly validated. High-quality measurements of grassland NPP are scarce, partly due to the difficulty of measuring some NPP components such as fineroot production  (Scurlock et al. , 1999 (Scurlock et al. , , 2002)). An updated version of the  Luyssaert et al. (2007)  database comprising non-forest biomes  (Campioli et al. , 2015)  was used here. This database contains a flag indicating managed or unmanaged to each site and provides mean annual temperature, annual precipitation, and downwelling solar radiation based on site measurements from the literature, CRU database  (Mitchell and Jones, 2005) , MARS database (http://mars. jrc. ec. europa. eu/mars/About-us/AGRI4CAST/Data-distribution/ AGRI4CAST-Interpolated-Meteorological-Data), or World-Clim database  (Hijmans et al.
, 2005). Three additional datasets used in this study present NPP measurements from 30 sites across China  (Zeng et al. , 2015; Y. Bai, personal communication, 2015)  and 16 sites across western Siberia  (Peregon et al. , 2008; with   savanna; Fig. S4 ). Duplicate observations from the same site year were averaged and considered as a single entry. NPP measurements with different management (managed or unmanaged) at the same site were considered as identical observations. In total, 270 grassland NPP measurements were compared to the simulation of ORCHIDEE-GM v3. 1 for the grid cell, corresponding to each site and for the time period of observation. Depending on the status of measured grassland (unmanaged or managed), modelled NPP from unmanaged or managed grassland is used for comparison. Modelled NPP over managed grassland accounts for the NPP from mown and grazed grassland and their corresponding fractions.
Grassland GPP from MODIS products The MOD17A3 dataset (version 55;  Zhao et al. , 2005; Zhao and Running, 2010 ) -a MODIS product on vegetation production -provides the seasonal and annual GPP data at a spatial resolution of 1 km from 2000 to 2013. To obtain the grassland GPP from the MOD17 dataset, we first extract the MOD17 GPP at 1 km resolution over grassland grids in the MOD12Q1 dataset. Here, the grassland in the MOD12Q1 dataset includes the "open shrubland", "savanna", and "grassland" in the Boston University's UMD classification scheme. The extracted annual and seasonal MODIS GPP was then averaged and aggregated to 0. 5 • × 0. 5 • spatial resolution to be comparable to model output.
5 • × 0. 5 • (available from 2007 to 2012). SIF-GPP is calculated by a SIF-GPP linear model adjusted from  Guanter et al. (2014)  (SIF-GPP = -0. 1 + 4. 65 × SIF (V26); see Supplement Sect. S6 for detail). To reduce the contamination of SIF by non-grassland PFTs, we restrict the model-data comparison to grassland-dominated grid cells, defined as those with grassland cover in the MOD12Q1 dataset (Sect. 2. 5. 2) is larger than 50 %.
Historical changes in the area and productivity of managed grassland The global minimum area of managed grassland (A managed-gm ) is of 6. 1 × 10 6 km 2 in 1901 and increased to 12. 3 × 10 6 km 2 in 2000 (Table  3 ; Fig. 5 ) -an increase of www. biogeosciences. net/13/3757/2016/ Biogeosciences, 13, 3757-3776, 2016 The potential harvested biomass from mown grassland (Ycut) and the potential biomass consumption over grazed grassland (Ygraze) are 10-year averages for the period  1901-1910 (1900s) and 1991-2000 (1990s) , representing the productivity at the beginning and at the end of the 20th century respectively. c Ruminant numbers (in units of livestock unit, LU) are calculated based on the total metabolisable energy (ME) requirement by all ruminant. The ME requirement by all ruminants is based on ruminant numbers from statistics (for 1961-2012; data derived from FAOSTAT, 2014) and literature estimates  (for 1901-1960 ; data derived from  Mitchell (1993 Mitchell ( , 1998a, b) , b)  and available in HYDE database at http://themasites.
To avoid the strong impact of other land-cover types (e. g. crop and forest) to the seasonal cycle, we only consider r seasonal for grid cells with grassland covering more than 50 % of total land in the MOD12Q1 dataset. real and temperate regions of the Northern Hemisphere well (r seasonal >0. 8; Table  4 ). In the band from 60 • S to 30 • N, relatively low average r seasonal correlations are found both with MODIS-GPP and SIF (ranging from 0. 55 to 0. 71). However, note that the r seasonal between the two remote sensing GPP related products is relatively low for grassland between 60 • S and 30 • N, particularly between 0 and 60 • S (Table  4 ).