dataesr/scientific-paragraphs-categorization huggingface dataset:
The remainder of this paper is organized as follows. Section 2 describes the simulation setting, the reference datasets, and the factor analysis. Section 3 presents results for the spatiotemporal patterns of the model uncertainties and factor analysis. Finally, Sections 4 and 5 provide a discussion and conclusions, respectively. This global process-based model of the land surface describes the complex links between the terrestrial biosphere and the water and the energy and carbon exchanges between the land surface and the atmosphere  (Krinner et al. , 2005). The used version in the IPSL-CM6 ESM for the CMIP6 simulations  (Boucher et al. , 2020) , which is known as tag 2. 0, was previously described in many papers  (Raoult et al. , 2019; Boucher et al. , 2020; Cheruy et al. , 2020; Tafasca et al.
, 2020) , and we only summarize its main features in this paper, with some details on the related parametrizations to the four studied ECVs. The land cover is described with 15 plant functional types (PFTs), including one for the bare soil, as seen in the full list in Table  2 , and they can all coexist in each grid-cell, where the taken fractions here are from the CMIP6 datasets  (Boucher et al. , 2020). For each PFT, the transpiration serves as a coupling flux between the water, energy budget, and photosynthesis process, which derive the evolution of the biomass and LAI owing to the generic equations with the PFTspecific parameters  (Krinner et al. , 2005). Evapotranspiration (ET) is controlled by the energy and water budget via a bulk aerodynamic approach, where four parallel fluxes are distinguished: sublimation, interception loss, soil evaporation, and transpiration.
This so-called reinfiltration fraction linearly decreases from 1 to 0 in totally flat grid-cells, where the mean grid-cell slope exceeds 0. 5%  (Ducharne et al. , in prep). For the CMIP6, the https://doi. org/10. 5194/hess-2020-438 Preprint. Discussion started: 13 October 2020 c Author(s) 2020. CC BY 4. 0 License. ORCHIDEE does not include the irrigation effect on the soil moisture, ET, and vegetation growth, although the model can simulate this anthropogenic pressure  (Xi et al. , 2018). The snow processes are described using a 3-layer scheme of intermediate complexity  (Wang et al. , 2013) , in which the snow albedo and insulating properties depend on the snow density and age. The ORCHIDEE 2. 0 also includes a revised parameterization of the interplay between the vegetation and the snow albedo, and the optimized parameters match the remote sensing albedo data from the MODIS sensor, distinguishing the visible and near-infrared (NIR) bands  (Boucher et al.
Surface soil moisture The SSM product provided by European Space Agency Climate Change Initiative (ESA CCI)  (Liu et al. , 2012)  was used as a reference. It is a merged product comprising multiple SSM data derived from various passive and active microwave satellites (i. e. , SMMR, SSM/I, TMI, AMSR-E, Windsat, SMOS, AMSR2, AMI-WS, ASCAT-A, and ASCAT-B) providing a long-term (1979-2015) SSM dataset with 0. 25° resolution. The CCI-SSM product has been evaluated extensively against in situ observations (e. g. ,  Al-Yaari et al. , 2019b) , and their accuracy has been reported as being relatively high compared to that of other existing products such as SMOS-L3, LPRM, and AMSR2  (Ma et al. , 2019). Because it includes low-quality data flags for snow, dense vegetation, and radio-frequency interference (RFI)  (Oliva et al.
, 2012) , we applied data screening following  Al-Yaari et al. (2016). We screened out all the pixels where the provided uncertainty was larger than 0. 06 m 3 /m 3 (volumetric water content). Next, any data records in which the SSM was not in a valid range (either >0. 6 or <0. 0)  (Fernandez-Moran et al. , 2017; Dorigo et al. , 2013)  were excluded. Finally, to exclude any areas covered by snow or dense vegetation and other unreliable regions, we kept only those areas in which the quality flag was zero (fine-quality pixels). The screened dataset was then aggregated into 0. 5° × 0. 5° and monthly time steps. This screening process removed 3. 6% of all the original pixels. We performed an initial check on the time series of the global average of CCI-SSM and found an artificial trend therein that depended on the availability of the observation data (Supplementary Fig.
, 2019). Given this issue, the LSM-and satellite-based SSM were compared with statistically normalized values rather than absolute values of SSM (e. g. ,  Polcher et al. , 2016). Therefore, a spatiotemporal normalization (Equation  1 ) was applied to each co-masked dataset to eliminate systematic biases among the datasets and make the comparison reliable (Supplementary Fig. S5B ): SSM norm = SSM-SSM σ SSM , (1) where SSMnorm is the normalized SSM, and SSM and σ SSM are the mean and standard deviation, respectively, of all the available SSM sampled along spatial and temporal dimension during the period. https://doi. org/10. 5194/hess-2020-438 Preprint. Discussion started: 13 October 2020 c Author(s) 2020. CC BY 4. 0 License.
LAI We used the global LAI dataset of  Zhu et al. (2013) , referred to hereinafter as LAI3g, which is based on a neural-network algorithm in conjunction with the third-generation Global Inventory Modeling and Mapping Studies (GIMMS 3g) and Moderate-resolution Imaging Spectroradiometer (MODIS) LAI product, with an original spatial resolution was 0. 5° and half-monthly temporal resolution. Considering the common period among LAI3g and the coupled/forced simulations, we selected 1987-2009 as the comparison period, and we resampled all data at 0. 5° spatial resolution and aggregated them into monthly time steps.
Precipitation In addition to the four ECVs, we also evaluated the simulated precipitation because it is the primary factor that influences the hydrological variables  (Qian et al. , 2006; Decharme & Douville, 2006). In addition, we used the GPCC dataset, which was also used to construct the meteorological forcing of the offline ORCHIDEE simulation. This gridded product at 0. 5° provides monthly precipitation derived from quality-controlled observed precipitation from world-wide stations  (Schneider et al. , 2014). As this is the forcing data, the precipitation output in the forced simulation is identical to the reference. Therefore, the model evaluation regarding the precipitation was only conducted for the coupled simulation. https://doi. org/10. 5194/hess-2020-438 Preprint. Discussion started: 13 October 2020 c Author(s) 2020.
Data processing For consistency between the observed and simulated data, we subjected the former to aggregation or resampling toward the 0. 5° spatial resolution and monthly time steps for each variable, as described above. Due to the presence of data gaps in the reference data sets, which are either because of the acquisition issues or the quality control and data screening, we masked the simulated datasets to match the spatial-temporal data availability of the corresponding reference data. For the SSM, the dense snow regions (with a snow water equivalent exceeding 48 mm) in the simulated data were further excluded so as to avoid unreliable comparisons with uncertain references. Also, co-masking was performed after the spatiotemporal resampling, followed by statistical normalization (only for the SSM).
Factor analysis To reveal features of the simulations in detail, the accuracy criteria were evaluated against various landscape/meteorological factors (Figure  1 ), namely PFT, LAI, irrigation, precipitation, slope, snow, and ET. For each factor, time series were averaged temporally to make only one global map (i. e. , the classification criteria were applied on long-term basis). The value of each factor was classified into a specific number of levels (classes), which were used as ordinal scales. Each factor was classified as given in Table  2 , and each factor is described in detail below: 1) For PFT, we used the input dataset that is used in ORCHIDEE. This includes fractional coverages in each pixel of 15 PFTs. We created a dominant PFT map by picking up the PFT class that have maximum fractional coverage for each pixel.
, 2009) , (ii) the input data required in machine-learning prediction, and (iii) the heterogeneous distribution of ground stations. Because the latter potential issue is particularly important for hardly accessible regions such as tropical and mountainous areas, progress in the data coverage of the FLUXNET network is desirable. Although ET products derived from satellite data  (Miralles et al. , 2011; Zhang et al. , 2010; Zeng et al. , 2014)  can also be used, unlike the other variables (SSM, LAI, and albedo), the retrieval of ET is not done directly from the satellite observations but depends largely on the process-oriented models. Therefore, in addition to the uncertainties in the satellite observations themselves, such products have uncertainties that arise from ancillary data (e. g. , atmospheric conditions, land cover) required in the model, as well as from imperfections in the model structure/parameterization (preliminary comparison among the different data sources can be found in Supplementary Fig.
Conclusions This paper has presented an in-depth evaluation of four interlinked essential climate variables (namely surface soil moisture, evapotranspiration, leaf area index, and albedo) simulated by ORCHIDEE land surface model under different simulation modes (either forcing by WFDEI or coupled with LMDZ). Statistical evaluation was conducted using various reference-data sources (ESA CCI, upscaled FLUXNET, GIMSS 3g and MODIS products), and factor analysis was conducted against various landscape factors (namely plant functional type, leaf area index, irrigation, precipitation, slope, snow water https://doi. org/10. 5194/hess-2020-438 Preprint. Discussion started: 13 October 2020 c Author(s) 2020. CC BY 4. 0 License. equivalent, and evapotranspiration). Although ORCHIDEE consistently represented the spatiotemporal patterns of each essential climate variable in general, some issues were found relating to water cycles and their different consequences between the forced and coupled simulations.
Considerable and/or very fast snowmelt in spring in the boreal region may result in the underestimation of albedo in such regions, which can affect energy balance and water cycles. The different results between the forced and coupled modes stress the importance of model evaluation under both modes to determine each potential error source in model simulation. Table  1. Overview of the selected reference datasets and analysis period (The last column provides the percent of the land pixels in the global maps with the observed values. A smaller amount of the SSM data was available in comparison with the others as a result of the relatively strict quality control. For the ET and LAI, no data were available in the extremely arid regions. ).
Acknowledgments This research was supported by the  Japan Society for the Promotion of Science (JSPS KAKENHI ; Grant number  16J00783 ) and by the  Centre national d'études spatiales (CNES , under the program  Terre Océan Surfaces Continentales et Atmosphère ). It benefited from the  ESPRI (Ensemble de Services Pour la Recherche  l'IPSL) computing and data center (https://mesocentre. ipsl. fr),and it was supported by  CNRS ,  Sorbonne Université ,  Ecole Polytechnique  and  CNES  and by other national and international grants. The SMOS and CCI products were obtained from the  "Centre Aval de Traitement des Données SMOS" (CATDS)  and the  European Space Agency (ESA) , respectively. The  CMIP6  project at the IPSL used the HPC resources of the  TGCC (Très Grand Centre de calcul du CEA)  under the allocations  2016-A0030107732 ,  2017-R0040110492 , and  2018-R0040110492  (project  gencmip6 ), which were provided by the  GENCI (Grand équipement national de calcul intensif).
When the program starts and the thread pool is first initialized, each thread will start its own cycle when the task queue is empty. In this situation each thread acquires the lock on the queue (not everyone at the same time, of course), and then it tries to pick a task from the queue. Since the latter is empty, the thread skips all operations without doing anything, and the thread blocks on the condition variable waiting for someone to put a task in the queue and wake it up 1. • Every time the SOLVE function adds a task to the queue, each thread of the thread pool that in that moment is waiting for the condition variable to become signalled is released. The first thread which manages to acquire the lock proceeds within its main cycle, and it picks the task from the queue. Even it this task should no longer be executed by any other thread, the only entity allowed to remove it from the queue is the one that put it in the queue.
We built our portfolio starting with the following code versions: • Versions (mc1 and mc2), are the original versions (sequential and parallel, respectively) implemented by McCreesh et al. [3] , in C++. • Version 1 (v1) is a sequential re-implementation of the original code in C language. This code constitutes the starting version for all our implementations, as it is more coherent with all following requirements, modifications, and choices. • Version 2 (v2) is the C multi-thread version described in Section III-A. It logically derives from mc1 and v1. • Version 3 (v3) is an intermediate CPU single-thread implementation that removes recursion and decreases memory usage. It is logically the starting point for comparison for the following two versions. • Version 4 (v4) is a CPU multi-thread implementation based on the same principles of the following CUDA implementation.
• Version 5 (v5) is the GPU many-thread implementation, described in Section III-B. It is based on v3 and v4. In the sequel, we will refer to the portfolio tool itself with vP. Notice that while the original version mc1 and mc2 were written in C++, all our versions have been written in C language but v5 which is written in CUDA. Note that versions v3 and v4 have been developed to explore the feasibility of new approaches in developing a maximum common subgraph algorithm in CUDA environment, not to obtain the best possible performances. For example, v4 is drastically slower than all other versions, but it represents the possibility to run the algorithm implemented in version 3 on more threads, which are completely independent from one another. Indeed the same system is used on a wider scale in version 5, which is significantly faster than v4, although they make use of the same approach.
A. DATA-SETS DESCRIPTION We tested our code using the ARG database of graphs provided by Foggia et al. [8]  and De Santo et al. [7]. The ARG database is composed by several classes of graphs, randomly generated according to six different generation strategies with various parameters settings. The result is a huge dataset of 168 different types of graphs and a total of 166. 000 different graphs  3. For our purposes, we selected only a subsection of the data-set, containing graphs pairs already prepared to have maximum common subgraphs of specific dimension. In particular we used the mcs10, mcs30, mcs50, mcs70 and mcs90 categories, i. e. , graphs pairs with maximum common subgraphs corresponding to 10, 30, 50, 70 and 90% of the original graphs size. For each of these categories several generation strategies are present, such as bound-valence graphs (bvg) or graphs generated using 2D, 3D or 4D meshes with different parameters.
Starting from this subset of the ARG database we considered two sub-categories of graphs pairs: Small ones (with less than 40 vertices, 2,750 pairs), and medium (from 40 to 50 vertices, 240 pairs). Graphs larger than 50 vertex are not considered as it has been impossible to solve any problem in less than 1,000 seconds. All graphs are manipulated as undirected and unlabelled to generate the following results.
Such instances, in the small set database are about 260, and they are the first growing part of the plot, up to the step. Then when instances begin to have larger solutions, and thus also the GPU is used to solve them, a gap is formed due to the need of copying data to and from the GPU. For this reason instances that make use of the GPU can not run for less than about 20 milliseconds, and this determine the gap in the graphic. From there on, performances increases much faster than any other version, actually going to almost equalize CPU parallel versions in number of timeout instances, even with a much slower start. For the smaller set parallel CPU versions (both the original one in C++ and our C implementation) are the best performing implementations. The CUDA implementation, even if not competitive for very easy instances, is faster then the C sequential versions for run-times higher than one second.
Fig. 10  indicates the most divergent instructions inside the body of the kernel function discovered by Nsight 4. Note that the worst one, the one at line 325, corresponds to the branch where the bound of the current branch is computed and the branch is eventually pruned. When the tree branch is not pruned, the body of the main loop of the kernel function is executed to compute new label domains and new solutions. On the contrary, when the branch is pruned, the code jump to the next main iteration. The overall behavior is then significantly different. All other relevant branches with high divergence (around 20%), are again inside the main loop of the kernel function. This lead to overall high percentage of branch divergence in the core of the kernel function, limiting a lot the maximum instruction throughput reachable by the kernel.
Then, we develop new heuristics to reorder the adjacency matrix, trying to isolate matrix blocks and make them upper triangular, to smooth the heavy-tail phenomenon dealing with dead-ends, and to restart the process with automatic restarts. These algorithms, albeit not more efficient than the original ones from an average point of view, allow a significant performance improvement for specific graph pairs. Finally, we propose a portfolio approach, integrating several local search algorithms as component tools. In this portfolio, a python interface orchestrate many CPU versions with one GPU procedure, exploiting modern hardware resources at their best. We demonstrate that the portfolio approaches is beneficial in the domain, and CUDA versions may worth further analysis. (c)  FIGURE 11:  Running times for different heuristics (reported on the y-axis) implemented within the sequential version (reported on the x-axis) denoted as v3 and taken as reference version.
Data collection from free-ranging green turtles The fieldwork was carried out from February 2018 to May 2019 in Grande Anse d'Arlet (14°50 0 N, 61°09 0 W), Martinique, France. We deployed CATS (Customized Animal Tracking Solutions, Germany) devices for periods ranging from several hours to several days on free-ranging immature green turtles. A CATS device comprises a video-recorder (1920 × 1080 pixels at 30 frames s -1 ) combined with a tri-axial accelerometer, a tri-axial gyroscope and a TDR (electronic supplementary material, figure  S1 ). The maximum battery capacity was considered to provide a recording capacity of 18 h of video footage and 48 h for other data. These devices were programmed to record acceleration and angular velocity (gyroscope) at a frequency of 20 or 50 Hz according to the recording capacity of the logger (the 50 Hz data were subsampled at 20 Hz using a linear interpolation to homogenize the sample).
785 kg) led to the remote release of the device several hours later. Devices were recovered by geolocation of an Argos SPOT-363A tag (MK10, Wildlife Computers Redmond, WA, USA), which was glued to the CATS device, with a goniometer (RXG-134, CLS, France). Instruments were deployed on 37 individuals, but complete datasets including video, acceleration, gyroscope and depth values were only recovered for 13 individuals (electronic supplementary material, table  S1 ).
Processing of video data and behavioural labelling The video footage was watched to identify the various behaviours and determine their starting and ending times to the closest 0. 1 s. Acceleration, angular velocity and depth data corresponding to each behavioural phase were visualized using R software (version 3. 5. 3) and the package rblt (figures 1 and 2;  [33] ). The 46 resulting behaviours were clustered into categories according to their similarities (the definition of the various behaviours is available in electronic supplementary material, table  S2 ). We retained seven main expressed categories for the multi-sensor signals, namely 'Breathing', 'Feeding', 'Gliding', 'Swimming', 'Resting', 'Scratching' and 'Staying at the surface'. All other behaviours were very infrequent and were grouped in an eighth category labelled 'Other'.
Segmentation of the multi-sensor dataset The automatic identification of the labelled behaviours from the multi-sensor signals required the segmentation of the dataset into homogeneous behavioural bouts. We started by relying on the depth data to distinguish the dives, defined as depths exceeding 0. 3 m for at least 5 s, from the surface periods. We attributed the surface periods to either 'Breathing' or 'Staying at the surface', according to whether the turtle remained at the surface for less or more than 6 s, respectively. We then distinguished between the various possible diving behaviours by using a changepoint algorithm, the pruned exact linear time (PELT) algorithm (R package changepoint;  [35] ), in which the 'pen. value' parameter, which corresponds to the additional penalty in the cost function for each additional partition of the data, can be manually adjusted.
e. 42 variables for each segment. Such a number of variables may be characterized by numerous correlations. However, machine learning algorithms are less sensitive than royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 7: 200139 classical regression methods to correlation in the explanatory variables. Nevertheless, for a simpler interpretation purpose, we looked for some possible reduced set of variables that may reach the same accuracy as the full dataset, but we did not find any convincing one. As the focus was more on predictability than interpretability (as is usual the case in machine learning), we kept all the 42 variables.
Validation of the automatic behavioural inferences To estimate the ability of our procedure to correctly infer the behaviours of sea turtles based on acceleration, angular velocity and depth data, we repeatedly performed 2/3 : 1/3 splits of the sample of 13 individuals, with nine individuals retained for the learning phase and the remaining four individuals used to validate the outcome. From the 715 possible combinations, we retained the 376 combinations in which 'Feeding' and 'Scratching' were not under-represented in the training dataset (i. e. when more than 60% of total feeding and scratching segments were present, i. e. 898 and 714, respectively). Nevertheless, the number of 'Feeding' and 'Scratching' segments was much lower than those attributed to 'Resting' and 'Swimming' (11 168 and 10 293 segments, respectively).