dataesr/scientific-paragraphs-categorization huggingface dataset:
The RCOSIM approach allows the parallelization on multi-core processors of co-simulations using the FMI standard. This approach is based on the exploitation of the co-simulation parallelism where dependent functions perform different computation tasks. In this paper, we extend RCOSIM to handle additional co-simulation requirements. First, we extend the co-simulation to multi-rate, i. e. where simulators are assigned different communication steps. We present graph transformation rules and an algorithm that allow the execution of each simulator at its respective rate while ensuring correct and efficient data exchange between simulators. Second, we present an approach based on acyclic orientation of mixed graphs for handling mutual exclusion constraints between functions that belong to the same simulator due to the non-thread-safe implementation of FMI.
It should be noted that libraries such as OpenMP and Intel TBB offer task programming features which can be used to execute multiple functions in parallel. Note that the code of each function is not parallelized, but two or more functions can be executed in parallel using this solution. However, they rely on online scheduling which may introduce high overhead and thus decreases the performance. In addition, given that information about dependence between functions is available and the execution times can be measured, we assume that offline scheduling is more efficient to achieve our goal. In this paper, we deal with the limitations of this approach that we highlighted in a recent work  [12]. Although RCOSIM resulted in interesting co-simulation speedups, it has two limitations that have to be considered in the multi-core scheduling problem in order to obtain better performances.
Performances of the Heuristics We have carried out different tests in order to evaluate our proposed approach. For both the acyclic orientation and the scheduling, we compared the execution time of our proposed heuristic with the execution time of the ILP, and the value of the objective function of the heuristic with the value of the objective function of the ILP. For ILP resolution, we used three solvers: lpsolve  [27] , Gurobi  [28] , and CPLEX  [29]. With lpsolve, we were only able to solve small instances of the scheduling problem. Gurobi was much more efficient but we obtained the best performance using CPLEX. Therefore, the results presented hereafter were obtained using CPLEX. Tests were performed on a desktop computer with a 6-core Intel Xeon processor running at 2. 7 GH and 16GB RAM.
Intel TBB flow graph We have chosen Intel TBB to implement an online scheduling because it offers a programming interface introduced in Intel TBB 4. 0, which allows easy parallelization of programs represented as graphs. It can be combined with loop parallelism supported by Intel TBB to further improve the parallelism exploitation. In Intel TBB, we distinguish between dependence graphs and data flow graphs. In dependence graphs, a dependence represents a precedence constraint between two nodes. During execution, this dependence acts as a signal to inform a node that a predecessor has finished its execution. In data flow graphs, a dependence is accompanied by data transfer from a predecessor to a node. In our implementation we used dependence graphs as explained hereafter. Intel TBB offers a wide range of classes that can be used to implement dependence graphs.
Scheduling in Intel TBB Intel TBB is based on programming with tasks instead of threads. Tasks are atomic units of execution that are allocated to threads to be executed. The objective is to make programming simpler by thinking at a higher level, i. e. specifying the potential parallelism of the program without having to handle the adaptation to the effective parallelism. The threads that run the tasks are called worker threads. The allocation is automatically done in runtime using an online scheduling algorithm known as work stealing. Each thread keeps a pool of tasks that are ready to be executed in a deque which is a double-ended queue. Elements can be pushed onto or popped from a deque from both ends. Threads are responsible for task creation, known as task spawning. When a task is spawned by a thread, it is pushed onto the deque of this thread from the top.
Implementation We used Intel TBB to implement parallel FMI co-simulation in xMOD. The first part which consists in creating the operation graph through the analysis of inter and intra-FMU dependence is the same as in RCOSIM. If the co-simulation is multi-rate, the multi-rate graph transformation is performed as well. Once the operation graph is constructed, an Intel TBB dependence graph which represents this operation graph is automatically created. The graph is of a dependence graph type because we do not manage explicitly data transfer between the different operations since the functions of the FMUs are provided in the form of binaries. Data transfer is implicitly managed by the partial order defined in the operation graph. In other terms, an operation that produces data is necessarily executed before the operation that consumes it.
Data writing and reading is done through shared memory and is hidden from the developer. It follows from this that data flow graphs provided by Intel TBB are not suitable for representing such co-simulations because they require explicit management of data transfer between the nodes. The creation of the dependence graph is done as follows: First, a graph is created and then nodes and edges are added to this graph. A node is created for each operation and added to an array that stores all the created nodes. The first node that is created is a source node which has no predecessor. This node becomes a predecessor of all the nodes that have no predecessor in the operation graph. The body of this node contains the initialization of the co-simulation. Then, for each operation in the operation graph, a function node is created.
In our solution, it directly impacts the size of the dependence graph which has to be scheduled. In order to handle this limitation, we suggest, if the communication step cannot be harmonized, to split the co-simulation senario in several ones and apply RCOSIM principles on each part. The partition choice would be made for reducing the HS of each part. Thanks to our approach, it is easy to find how many cores are sufficient to reach the maximum speed up for each sub-scenario. Then the number of cores could be partitioned, one partition for each scenario. At execution, data exchange between the different co-simulation parts would have to be sequenced by adding synchronization mechanisms. We do not deny that our solution have a cost in terms of computation time, even if the proposed heuristics offer an interesting trade-off between optimality and computation time.
Cluster redshifts from X-ray data alone will be only available for a small fraction of sources and only to a precision of ∆z/(1 + z) = 0. 1  (Borm et al. 2014 ), which we demonstrate in our work here is a factor ∼20 worse than what is achievable with state of the art optical imaging data. The Multi-Component Matched Filter Cluster Confirmation Tool (MCMF;  Klein et al. 2018) , is designed for use on large scale imaging surveys such as the Dark Energy Survey (DES;  Abbott et al. 2016)  to do automated confirmation and redshift estimation for large surveys like eROSITA. In this work we use MCMF to confirm clusters detected in the ROSAT All-Sky Survey  (RASS, Truemper 1982 ) over 5000 deg 2 using DES imaging data. More precisely, we use the proprietary DES Y3A2 GOLD catalog, which is a valueadded version of the catalog recently published with the DES DR1 dataset  (Abbott et al.
2018) , to investigate ∼20000 candidates from the second ROSAT All-Sky Survey source catalog (2RXS) presented in  Boller et al. (2016). As described in detail in our pilot study  (Klein et al. 2018) , MCMF uses a red sequence (RS) galaxy technique together with an X-ray prior and matched random pointings to obtain redshifts and exclude chance superpositions. This paper is structured as follows. In Section 2 we describe the dataset used in this work, and in Section 3 we outline the cluster confirmation method. The application of the confirmation method and the properties of the resulting cluster catalog are described in Section 4. The conclusion of this paper appears in Section 5. Throughout this paper we adopt a flat ΛCDM cosmology with Ω M = 0. 3 and H 0 = 70 km s -1 Mpc -1.
Masking and background estimation To measure the cluster richness one has to account for the area within r 500 that is not covered by useful imaging data and for the number of galaxies not related to the cluster. As mentioned in Section 2, we use the bad region and foreground flags provided by the GOLD catalog. This and the imaging coverage of the DES survey can cause holes in our dataset, which need to be accounted for. Similar to our pilot study, we use the local source density to identify regions with no data. For that we first obtain 2D histograms of the sources within a radius of 0. 5 • , our default local cutout region from the source catalog. The bin size is chosen such that it contains 16 sources on average. We obtain 2D histograms with various rectangular shaped bins keeping the bin area constant.
The caveat of using imaging data compared to the other probes is the noise of the estimators (based on a few dozen galaxies compared, for example, to thousands of X-ray photons) and its susceptibility to line of sight projections. Moreover, in comparison to X-ray imaging, optical imaging estimators are not able to distinguish between two clusters in a pre-collision or post-collision state. However, the combination of optical and X-ray data will allow us to study merging clusters in all phases of merging. Despite the fact that estimators based on broadband photometry should be more prone to projections and more noisy than X-ray based estimators,  Wen & Han (2013)  reported that their estimator reaches a success rate of 94% on a test sample of 98 clusters with known dynamical state. For our work we adopt the set of dynamical state estimators based on  Wen & Han (2013) , adapting them somewhat to the available dataset.
These studies will further test the performance of MCMF in selecting a clean sample of clusters from what is initially a highly contaminated X-ray source catalog, but they will also test the underlying 2RXS catalog and its usability for cosmological studies. We view these studies as useful precursors to the use of MCMF to characterize future X-ray and SZE selected cluster catalogs from eROSITA  (Predehl et al. 2010 ) and SPT-3G  (Benson et al. 2014)  using the new multiband datasets from Euclid  (Laureijs et al. 2011)  and LSST  (Ivezic et al. 2008). cluster nearby and that the redshift of both ABELL clusters are identical, we conclude that the MCMF redshift is likely correct and that the published redshift of ABELL 3736 is likely taken from the same galaxies as for ABELL S0919. Applying a 150 matching radius with an additional cut on the maximum of the redshift discrepancy of ∆z < 0.
ACKNOWLEDGEMENTS We thank  Th. Boller ,  M. Freyberg  and  H. Brunner  from the MPE high energy group for helpful conversations. We acknowledge the support of the  Max Planck Gemeinschaft Faculty Fellowship program  and the  High Energy Group at MPE. Furthermore , we acknowledge the support of the  DFG Cluster of Excellence "Origin and Structure of the Universe" , the  Transregio program TR33  " The Dark Universe ", and the  Ludwig-Maximilians-Universität. Funding for the DES Projects has been provided by the  U. S. Department of Energy , the  U. S. National Science Foundation , the  Ministry of Science and Education of Spain , the  Science and Technology Facilities Council of the United Kingdom , the  Higher Education Funding Council for England , the  National Center for Supercomputing Applications  at the  University of Illinois at Urbana-Champaign , the  Kavli Institute of Cosmological Physics at the University of Chicago , the  Center for Cosmology and Astro-Particle Physics at the Ohio State University , the  Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University ,  Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico  and the  Ministério da Ciência, Tecnologia e Inovação , the  Deutsche Forschungsgemeinschaft  and the  Collaborating Institutions in the Dark Energy Survey.
The DES data management system is supported by the  National Science Foundation  under Grant Number  AST-1138766. The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2012-39559 ,  ESP2013-48274 ,  FPA2013-47986 , and  Centro de Excelencia Severo Ochoa   SEV-2012-0234. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP7/2007-2013) including  ERC  grant agreements  240672 ,  291329 , and  306478. AFFILIATIONS.
Capacity building activities and the role of PIRATA in a future Tropical Atlantic Observing System that is presently being optimized are also described. Plain Language Summary Long data records are essential for improving our understanding of the weather and climate, their variability and predictability, and how the climate may change in the future in response to anthropogenic greenhouse gas emissions. Climate variability in the tropical Atlantic Ocean has strong impacts on the coastal climate in particular and, consequently, the economies of the surrounding regions. Since 1997, the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) program has maintained a network of moored buoys in the tropical Atlantic in order to provide instantaneous high-quality data to research scientists and weather forecasters around the world.
, 2008; Servain et al. , 1998). PIRATA is maintained by a close collaboration between institutions in the United States (National Oceanic and Atmospheric Administration  [NOAA] ), Brazil (Instituto Nacional de Pesquisas Espaciais, with contribution of the Diretoria de Hidrografia e Navegacão  [DHN] ), and France (French Institut de Recherche pour le Développement  [IRD]  and Meteo-France). These institutions established a formal partnership in 2001 through a Memorandum of Understanding to provide long term support for PIRATA. Other national observing initiatives in the tropical Atlantic have been closely cooperating with PIRATA activities including for example the German GEOMAR Tropical Atlantic Program, which allows for a coordinated development of the tropical Atlantic observing system. PIRATA is based around an array of moored buoys providing meteorological and oceanographic data transmitted in real time, disseminated via Global Telecommunication System (GTS) and Global Data Servers.
Thus, at present (2018) there are 10 T-Flex systems operating in the array. In March 2017, a new CO 2 Carbon Interface Ocean Atmosphere sensor was implemented at 6°S-8°E, as an IRD contribution to the "Optimising and Enhancing the Integrated Atlantic Ocean Observing Systems" program, funded by the European Horizon 2020 research and innovation project (AtlantOS; https://www. atlantos-h2020. eu/). As a GEOMAR contribution to AtlantOS, the number of O 2 sensors was doubled by implementing three O 2 sensors with real-time data transmission at 21°N-23°W and 12°N-23°W. Internally recorded data are still collected with two O 2 sensors at 4°N-23°W, which is planned for implementation into the real-time data transmission in the future. The IRD contribution to AtlantOS also allowed the implementation of additional current meters at 10-m depth at three sites (8°N-38°W, 0°N-35°W, and 0°N-10°W) and temperature/conductivity sensors at 5-and 10-m depth at 0°N-10°W.
The CO 2 network was further maintained during the European projects FP7 CARBOCHANGE  (2011) (2012) (2013) (2014) (2015)  with additional support from IRD and Institut National des Sciences de l'Univers. These observations are part of the research infrastructure of Integrated Carbon Observation System. A new CO 2 sensor has been installed on the T-Flex buoy at 6°S, 8°E in March 2017, funded by the European H2020 AtlantOS project. This buoy is located ~200 km off the Congo River mouth, aiming at better understanding the impact of large rivers on the CO 2 distribution in the tropical Atlantic. The mooring at 6°S, 10°W is located south of the seasonal ACT and is affected by its migration from June to September  (Lefèvre et al. , 2008). It is also located above the mid-Atlantic ridge and subject to internal waves that increase surface fCO 2 and nutrients in the mixed layer  (Parard et al.
Important aspects of this data set are that all original PIRATA data that pass quality control and do not require bias correction are retained without modification and detailed error estimates are provided for each daily-averaged oceanic and atmospheric parameter. The terms in the mixed layer heat and temperature budgets are calculated and provided, with error bars, as part of the ePIRATA data set. In addition to its value for upper-ocean and climate research and model validation, ePIRATA presents a framework for assessing the value of additional PIRATA sensors for reducing uncertainties in upper-ocean temperature and salinity, mixed layer depth and currents, and mixed layer heat and temperature budget components. To address the relevance of thermistor chains on free-drifting buoys and evaluate the potential future use of such material equipped with other physico-chemical parameters (such as salinity or dissolved oxygen), an analysis has been carried out in the framework of the EU AtlantOS program to revisit data collected by such equipment, some deployed in 2006 and 2015 during PIRATA cruises in the Gulf of Guinea  (Rousselot et al.
The most depleted isotopic values, signature of dilution processes, were recorded during periods of strong convective activity in the Gulf of Guinea. These data sets also allowed demonstrating the impact of tropical upwellings and mesoscale SSS patterns on water vapor composition, suggesting that high-resolution isotope sampling can identify advection processes, even if salinity variations are small  (Benetti, Steen-Larsen, et al. , 2017; Benetti, Reverdin, et al. , 2017). Recently,  Trolliet et al. (2018)  carried out a study using PIRATA buoy solar irradiance measurements for satellite product validation, as a continuation of the study by  Boilley and Wald (2015). The data sets comprise the reanalyses MERRA-2 and ERA-5 and three satellite-derived data sets, HelioClim-3v5, SARAH-2, and CAMS Radiation Service v2, and are compared to qualified measurements of hourly irradiance made at five buoys of the PIRATA network for the period 2012-2013.
The reanalyses often report cloud-free conditions while actual conditions are cloudy and reciprocally, actual cloudless conditions as cloudy. The medium and high-level clouds exhibit more bias than the low-level clouds. The reanalyses poorly correlate with the optical state of the atmosphere derived from the measurements. The actual irradiance field is spatially distorted by reanalyses, especially for MERRA-2. Performances are similar between the three satellitederived data sets. They correlate well with the optical state of the atmosphere and reproduce well the dynamics of the solar irradiance. The three data sets exhibit overestimation with the lowest biases reached by CAMS Radiation Service v2. The bias of HelioClim-3v5 is fairly similar from one location to the other, which means that the actual spatial gradients are well reproduced.
PIRATA Data Use for Numerical Model Validation PIRATA buoy and cruise data are used extensively for model validation. Most of the results summarized above (equatorial circulation, mixed layer processes, TIWs, deep jets, etc. ) are based on numerical models validated with in situ and other measurements including PIRATA data (e. g. ,  Brandt et al. , 2016; Cintra et al. , 2015; Hormann & Brandt, 2007; Imbol Kongue et al. 2017; Jouanno et al. , 2011 Jouanno et al. , , 2013;; Kolodziejczyk et al. , 2009 Kolodziejczyk et al. , , 2014;; Silva et al. , 2009; Von Schuckmann et al. , 2008). One other example was the shipboard ADCP data from the Brazilian maintenance cruises for the period 1998-2005, which were used to validate 10. 1029/2018EA000428 Earth and Space Science previous numerical simulations that predicted the existence of the northern branch of the NECC  (Urbano et al.
g. ,  Lehodey et al. , 2015 ; see also www. seapodym. org). Observing System Simulation Experiments were carried out with the SEAPODYM midtrophic level component to analyze the biomass distribution of micronekton in the Eastern tropical Atlantic and to assess the benefit of using different observation networks in data assimilation procedures. Such experiments clearly demonstrated that due to its localisation and its sampling scheme during cruises, the PIRATA network is one of the best observation networks for data assimilation in SEAPODYM-midtrophic level  (Delpech et al. , personal communication, November 8, 2017) , proving that PIRATA may also be relevant for biological research.
More CO 2 measurements, 10. 1029/2018EA000428 Earth and Space Science particularly in the western and central parts of the PIRATA array, will link the program to the international SOLAS project (www. solas-int. org), to the regional (and primarily coastal) initiatives on ocean acidification (OA) studies. These studies include Brazilian OA network (www. broa. furg. br) and Latin American OA network (www. laoca. cl), and the global initiatives, Global Ocean Acidification Observing network (GOA-ON), and Surface Ocean CO 2 Atlas. Also, in order to document surface OA trends, it is necessary to monitor at least two measurable parameters of the marine CO 2 system, that is, some combination of fCO 2 /pCO 2 , total CO 2 (TCO 2 ; aka dissolved inorganic carbon), pH, and total alkalinity. At present, there are available commercially autonomous sensors for fCO 2 /pCO 2 and pH that are already used in combination by NOAA through their OA buoys, for example, the RAMA Bay of Bengal Ocean Acidification mooring.
Conclusion Since 1997, PIRATA partners have established and efficiently sustained a moored buoy network in the tropical Atlantic to support research, operational analyses and forecasting, and climate assessments. PIRATA has vastly increased the amount of data available from the tropical Atlantic, not only from the moorings themselves but also from the cruises that allow for the collection of specialized shipboard data and for the deployment of Argo floats, Global Drifter Program drifters, and XBTs that help build other components of the ocean observing system. PIRATA data have proven to be valuable for constraining oceanic and atmospheric reanalysis products, for satellite validation, and for the development of new in situ and/or satellite/in situ data products. PIRATA data and data products are readily available through the Global Data Centers (Coriolis/IFREMER in France and NDBC/NOAA in the USA) and from other dedicated Web databases (PIRATA, TACE, PREFACE, and others).
g. ,  Legler et al. , 2015). The moorings and ships used in the program have in addition served as valuable platforms of opportunity for other programs to study, for example, ocean mixing, ocean biogeochemistry, oxygen variability and carbon cycle processes, and marine animal behavior. PIRATA also provides opportunities for collaborative studies involving "piggyback" operations sponsored by other groups or organizations, such as sediment trap deployments from PIRATA cruise to study downward particles fluxes or deep T/S measurements on moorings as promoted by the OceanSITES program. PIRATA has long history of working in collaboratively and welcomes further involvement of self-supported activities that are consistent with PIRATA's broad mission and technically feasible. Studies are presently going on to determine the complementarity of data sets for reanalysis of T, S, current, and other fields.