dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion This paper presents the first systematic study to assess the ability to detect numerically modelled binary black hole data in real data taken from Initial LIGO and Virgo and recolored to predicted sensitivity curves of Advanced LIGO and Advanced Virgo in early observing runs. Building upon the work of the first NINJA project, this work, the culmination of the second NINJA project, studies the ability to do gravitational wave astronomy on a set of 60 binary black hole hybrid waveforms submitted by 8 numerical relativity groups. In this work, a set of 7 numerically modelled binary black hole waveforms were added into the recolored data. This data was distributed to analysts with no knowledge of the parameters of the systems. The unmodelled gravitational waveform search pipeline, cWB, was able to recover one of these signals with an estimated false alarm rate of 1 every 47 years.
The matched-filtered compact binary merger search pipeline, ihope, using a bank of BBH IMR waveforms, which were not calibrated against the NR signals used in NINJA-2, was able to recover 6 of the waveforms with false alarm rate upper limits ranging between 1 every 300 years and 1 every 2500 years. A range of parameter estimation codes were run on the 7 blind injections that were added to the data used in this work. Though only results from the MCMC sampler were shown, these results proved to be statistically equivalent to estimates produce by the nested sampling and multinest samplers. These results demonstrate that it will be difficult to produce precise estimate of black hole component masses and spins because of intrinsic degeneracies between these parameters in the emitted waveforms. For some of the BBH blind injections we find that a neutron-star-black-hole coalescence cannot be ruled out.
Acknowledgements The authors gratefully acknowledge the support of the  United States National Science Foundation  for the construction and operation of the  LIGO Laboratory , the  Science and Technology Facilities Council of the United Kingdom , the  Max-Planck-Society , and the  State of Niedersachsen/Germany  for support of the construction and operation of the GEO600 detector, and the  Italian Istituto Nazionale di Fisica Nucleare  and the  French Centre National de la Recherche Scientifique  for the construction and operation of the Virgo detector. The authors also gratefully acknowledge the support of the research by these agencies and by the  Australian Research Council , the  International Science Linkages program of the Commonwealth of Australia , the  Council of Scientific and Industrial Research of India , the  Istituto Nazionale di Fisica Nucleare of Italy , the  Spanish Ministerio de Economía y Competitividad , the  Conselleria d'Economia Hisenda i Innovació of the Govern de les Illes Balears , the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , the Polish.
• The OSS (Object Storage Service) is a document-oriented and schemaless database service, which provides both ease of use and flexibility. In this context, a document is a set of keyword-value pairs where the values can also be documents (is a nested model), or references to other documents (with very weak integrity enforcement). Both services are presented in the form of stateless web services (REST in API format). The data format used for communication is JSON, which is more easily readable tan XML and includes enough expression capability for the present case. The internal layer is used to deploy all management and general-purpose applications, in addition to the all services at the platform layer, such as the services offered by DoyFE. es. This layer provides a virtual hosting service with automatic scaling and functions for balancing workload.
The Data Management API for the GridRPC describes an optional API that extends the GridRPC standard. It provides a minimal subset of functions to handle a large set of data operations, among which movement, replication, migration and stickyness. We already showed that its use leads to 1) reduced time to completion of application, since useless transfers are avoided; 2) improved feasibility of some computations, depending on the availability of services and/or storage space constraints; 3) complete code portability between two GridRPC middleware; and 4) seamless interoperability, in our example between the French GridRPC middleware DIET and the Japanese middleware Ninf, distributed on French and Japanese administrative domains respectively, leading to both of them contributing to the same calculus, their respective servers sharing only data through our implementation of the GridRPC DM API.
We have extended the implementation of the library and a further integration has been made available into DIET as a back-end of its data manager Dagda. We thus present how the library is used in the International Sparse Linear Algebra Expert System GridTLSE which manages entire expertises for the user, including data transfers, tasks executions, and graphical charts, to help analysing the overall execution. GridTLSE relies on DIET to distribute computations and thus can benefit from the persistency functionalities to provide scientists with faster results when their expertises require the same input matrices. In addition, with the possibility for two middleware to interact in a seamless way as long as they're using an implementation of the GridRPC Data Management API, new architecture of different domains can easily be integrated to the expert system and thus helps the linear algebra community.
Introduction The GridRPC API  [13]  was designed to define Remote Procedure Call over the Grid. It has been widely and successfully used in middleware like DIET  [8] , NetSolve/GridSolve  [18] , Ninf  [16] , OmniRPC  [15]  and SmartGridSolve  [4]. The API concentrated on remote service executions: It standardized synchronous and asynchronous calls and computing tasks management. In June 2011, the Open Grid Forum standardized the document "Data Management API within the GridRPC"  [6]  which describes an API that extends the GridRPC standard  [13]  (GridRPC DM API). Used in a GridRPC middleware, it provides a minimal set of structure definitions and functions to handle a large set of data operations, among which movement, replication, migration, and persistence. A first prototype of the API containing the basic functions offering data management through the library has been developed, in addition to a GridRPC layer making possible to write a single GridRPC client able to invoke DIET and Ninf services transparently.
With several experiments relying on these early developments, we showed in  [7]  that the GridRPC DM API already answers most of the needs presented in  [12] , namely code portability (the exact GridRPC code written with the GridRPC DM API to manage data can be used with any GridRPC middleware), computational feasibility (because of the transparent use of remote data, a GridRPC client is not required to have data on its own system to respect the GridRPC paradigm, hence leading to the use of light client machines), performance (useless transfers can be avoided with the use of persistence). Moreover, we showed that it is even possible for two Grid middleware, managing resources across different administrative domains and using the API, to collaborate in a completely transparent manner. Although the implementation is still in progress, we show in this paper how a sparse linear algebra expert system, GridTLSE 1  , can benefit from the use of the data management provided by the API: we first study the possible performance improvements that can be achieved since expertises may use several times the same matrices; then we study how using stickyness for temporary results can lead to spare already work made in a factorization process to perform several remote solves.
State of the Art In the GridRPC paradigm, input and output data are arguments of grpc_call() and grpc_ call_async() and are transferred between clients and servers during steps (4) and (  5 ) of Figure  1. Thus, each Grid middleware managing its own built-in data, code portability is impossible, and performance can only be managed bypassing the GridRPC model, even for request sequencing  [3]. Many other issues arise  [5] , but they can only be addressed separately: one can store data on distributed file system like GlusterFS 2  or GFarm  [17]  to deal with automatic replication; Om-niRPC introduced omniStorage  [14] (3) (4)  (5)  Figure  1 : GridRPC paradigm such as GFarm and Bittorrent. It aims to provide data sharing patterns (worker to worker, broadcast and all-exchange) to optimize communications between a set of resources, but needs knowledge on the topology and middleware deployment to be useful; DIET also introduced its own data managers (JuxMEM, DTM, and DAGDA  [2, 9, 10] ), which focus on both user data management and persistence of data across the resources, with transparent migrations and replications.
GridRPC Data Management API The GridRPC Data Management standard relies on the definition of 12 functions and of the GridRPC data, which represents a Grid data, i. e. , several copies of the same data stored in different locations, and which may store a data in addition to meta-information. Among these, one can cite the dimension of the data (vector, matrix or more complicated structures), the data type to be able to make the relation with the language type (double, integer, etc). A new and special type, CONTAINER_OF_GRPC_DATA, has been introduced as a set where the user can put or get GridRPC data, and can be used with services producing a number of parameters unknown before execution. There is also a management mode for each data: strictly volatile (the system must erase the data from the storage resource once it has been used or transfered), volatile (the default behavior of the GridRPC standard), sticky (a copy of the data must be maintained on the given location), unique sticky (in addition to being maintained on the given location, that data must stay the unique copy in the system: some systems may indeed implement some fault tolerance mechanisms and would otherwise replicate the data, a behavior that some users do not want), or persistent (the user relies on the GridRPC middleware and its data manager to handle in a seamless and best possible way data management, with possible inner coupling with scheduling decisions).
Finally, URI being a description of both the location and the protocol that must be used to access a data registered in the system, one can thus find two lists of URIs, describing where each data can be transfered from or has to be transfered to, and a list of management modes, each of which being applied to the corresponding data whose URI is given in the output URI list. The API is driven by 12 functions: grpc_data_init() initializes a GridRPC data with information describing the previously mentioned characteristics. GridRPC data referencing input parame-ters must be initialized with identified data before being used in a grpc_call(). grpc_data_getinfo() lets the user access information like transfer status, location, etc. grpc_data _transfer() writes data to output locations (additional ones, with their corresponding management mode, can be given) from input locations (a list of additional input URIs can also be provided).
Consequently, some broadcast/multicast mechanisms can then be implemented in the GridRPC data middleware in order to improve performance. grpc_unbind_data() may be used when a client does not need the handle on the GridRPC data anymore, but to explicitly erase a data on storage resources and free the GridRPC data, a call to grpc_free_data() must be performed. In order to communicate a Grid data between grid users, the GridRPC data management API proposes two functions, grpc_data_load() and grpc_data_save(). The last functions of the API are related to inner management, to be able to address in-memory data from an URI, and to get and put GridRPC data in a container of GridRPC data, as mentioned previously. This work relies on the implementation of the GridRPC standard, and evaluates its integration, and its performance both in terms of possibilities and measurements.
Integrating GridRPC Data Management into GridTLSE 4. 1 GridTLSE The expert site GridTLSE 5  for linear algebra aims at providing tools and softwares for sparse matrices (matrices with a higher ratio of zero components versus nonzero ones). The site provides user assistance to evaluate and choose the best solver for given problems and helps to set the appropriate values of the input parameters that control the efficiency of the selected solver (Sparse Direct Solvers currently available in GridTLSE are MA48, MA49, MUMPS, SuperLU, UMFPack). The GridTLSE project uses a high level component semantic description to manipulate sparse linear algebra services (factorization, orderings, linear solves, etc. ). Figure  2  presents the different layers of the GridTLSE platform: to provide an easy access to these services, GridTLSE uses the scenario concept which is a graphical description of the task workflow to perform (Geos).
A SED can also serve as the interface and execution mechanism for either a stand-alone interactive machine or a parallel supercomputer, by directly and independently dealing with its batch scheduling facility. The third component of the DIET architecture, the agent, facilitates the service location and invocation interactions of clients and SEDs. The DIET deployment is structured hierarchically for improved scalability, the hierarchy of agents being composed of a single Master Agent (MA) (the entry point) and several Local Agents (LA), providing higher-level services such as scheduling and data management. Figure  3  shows an example of a multi-hierarchy DIET architecture. DIET relies on DAGDA  [10]  to perform its data management. DAGDA can make implicit and explicit replication, can optimize transfers by selection the most convenient source.
Results Apart from Aster_feti009a_2. 1 whose timings are too small to be really meaningful, results depicted in Figure  5  are really interesting. Similar experiments were conducted in  [11] , but results are slightly different here, because of a network bandwidth whose performance has increased and DAGDA which relies on the GridRPC data management library. Indeed, apart from the expertise using FLUX-2M, for which DAGDA shows a real improvement as long as a matrix is used at least twice in an expertise, we can see that the cost to register a matrix in the data management layer is higher than expected: for Aster_perf001a and NICE20MC the expertise (2 GridRPC calls) finishes in the same range of time for TC0, TC1 and TC2, but actually the two calls for TC1 finish nearly at the same time. Of course, TC2 involving an additional local transfer, it conducts to a slightly worse time to complete the expertise.
Conclusion and future work We have presented the first evaluation of the benefits obtained by the extension of the GridRPC API OGF standard concerning data management, i. e. , the GridRPC Data Management GFD-R-P. 186 OGF standard, in the context of real computations required for a sparse linear algebra expert system, GridTLSE. We have conducted several experiments. The experiments for validation showed us that the proposed solution is interesting if the same matrix is used more than a couple of times in an expertise. Of course, in a production environment, some gains would rapidly be obtained since different expertises are generated by GridTLSE, and they may use the same input matrices. But some additional work on the architecture of the solution, i. e. , the integration of GridTLSE and DIET, together with the use of the data management, can improve the solution by overlapping given operations and thus reducing the time spent to register a new data in the system.
METHODS Data. We required that forest monitoring plots provided unbiased samples of all living trees within the plot boundaries, and that the trees had undergone two trunk diameter measurements separated by at least one year. Some plots sampled minimally disturbed old (all-aged) forest, whereas others, particularly those associated with national inventories, sampled forest stands regardless of past management history. Plots are described in the references cited in Supplementary Table  1. Our raw data were consecutive measurements of trunk diameter, D, with most measurements taken 5 to 10 years apart (range, 1-29 years). D was measured at a standard height on the trunk (usually 1. 3-1. 4 m above ground level), consistent across measurements for a tree. Allometric equations for 16% of species required, in addition to consecutive measurements of D, consecutive measurements of tree height.
41) (a tutorial and the computer code are available through http://ctfs. arnarb. harvard. edu/Public/CTFSRPackage/files/tutorials/growthfitAnalysis). The sampler works by updating each of the parameters in sequence, holding other parameters fixed while the relevant likelihood function is used to locate the target parameter's next value. The step size used in the updates was adjusted adaptively through the runs, allowing more rapid convergence  40. The final Markov chain Monte Carlo chain describes the posterior distribution for each model parameter, the error, and was then used to estimate the posterior distribution of growth rates as estimated from the model. Priors on model parameters were uniform over an unlimited range, whereas the parameters describing the standard deviation were restricted to.
We assessed this possibility in two ways. First, our conclusions remained unchanged when we compared results for the 53% of species that came uniquely from single large plots with those of the 47% of species whose data were combined across several plots. Proportions of species with increasing mass growth rates in the last bin were indistinguishable between the two groups (97. 6% and 95. 8%, respectively; P 5 0. 40, Fisher's exact test). Additionally,.
6%) (P 5 0. 44, Fisher's exact test). Thus, our finding of increasing G with tree size is not affected by the minority of species that have at least one tree exceeding the maximum value of D used to calibrate their associated allometric equations. A bias that could inflate the rate at which G increases with tree size could arise if allometric equations systematically underestimate M for small trees or overestimate M for large trees  43. For a subset of our study species we obtained the raw dataconsisting of measured values of D and M for individual trees-needed to calibrate allometric equations, allowing us to determine whether the particular form of those species' allometric equations was prone to bias, and if so, the potential consequences of that bias. To assess the potential for allometric bias for the majority (58%) of species in our data set-those that used the empirical moist tropical forest equation of ref.
Log-transformation additionally resulted in skewed growth rate residuals. Dropping trees with G # 0 caused several species to fall below our threshold sample size, reducing the total number of species analysed to 381 (Extended Data Fig. 2 ). Growth in the absence of competition. We obtained published equations for 41 North American and European species, in 46 species-site combinations, relating species-specific tree diameter growth rates to trunk diameter D and to neighbourhood competition  [45] [46] [47] [48] [49]. Setting neighbourhood competition to zero gave us equations describing estimated annual D growth as a function of D in the absence of competition. Starting at D 0 5 10 cm, we sequentially (1) calculated annual D growth for a tree of size D t , (2) added this amount to D t to determine D t 1 1 , (3) used an appropriate taxon-specific allometric equation to calculate the associated tree masses M t and M t11 , and (iv) calculated tree mass growth rate G t of a tree of mass M t in the absence of competition as M t 1 1 2 M t.
Acknowledgements We thank the hundreds of people who have established and maintained the forest plots and their associated databases;  M. G. Ryan  for comments on the manuscript;  C. D. Canham  and  T. Hart  for supplying data;  C. D. Canham  for discussions and feedback;  J. S. Baron  for hosting our workshops; and  Spain's Ministerio de Agricultura ,  Alimentacion y Medio Ambiente (MAGRAMA)  for granting access to the Spanish Forest Inventory Data. Our analyses were supported by the  United States Geological Survey (USGS) John Wesley Powell Center for Analysis and Synthesis , the  USGS Ecosystems and Climate and Land Use Change  mission areas, the  Smithsonian Institution Global Earth Observatory-Center for Tropical Forest Science (CTFS) , and a  University of Nebraska-Lincoln Program of Excellence in Population Biology Postdoctoral Fellowship  (to N.
G. B. ). In addition, X. W. was supported by  National Natural Science Foundation of China  ( 31370444 ) and  State Key Laboratory of Forest and Soil Ecology  ( LFSE2013-11 ). Data collection was funded by a broad range of organizations including the  USGS , the  CTFS , the  US National Science Foundation , the  Andrews LTER  ( NSF-LTER DEB-0823380 ), the  US National Park Service , the  US Forest Service (USFS) , the  USFS Forest Inventory and Analysis Program , the  John D. and Catherine T. MacArthur Foundation , the  Andrew W. Mellon Foundation ,  MAGRAMA , the  Council of Agriculture of Taiwan , the  National Science Council of Taiwan , the  National Natural Science Foundation of China , the  Knowledge Innovation Program  of the  Chinese Academy of Sciences, Landcare Research  and the  National Vegetation Survey Database (NVS) of New Zealand , the  French Fund for the Global Environment and Fundacion ProYungas.
Redshift distribution In GOODS-S we rely on a large spectroscopic database, 4 including more than 3000 sources  (Cristiani et al. 2000; Croom et al. 2001; Bunker et al. 2003; Dickinson et al. 2004; Stanway et al. 2004a,b; Strolger et al. 2004; Szokoly et al. 2004; van der Wel et al. 2004; Doherty et al. 2005; Le Fèvre et al. 2005; Mignoli et al. 2005; Vanzella et al. 2008; Popesso et al. 2009; Santini et al. 2009;  3 Calculated through SED-fitting. See Section 3 for details. 4 Publicly available at: http://www. eso. org/sci/activities/ garching/projects/goods/MasterSpectroscopy. html Balestra et al. 2010; Cooper et al. 2012; Kurk et al. 2013). We used this to enrich the existing spectroscopic database of the MUSIC catalogue  (Grazian et al. 2006; Santini et al. 2009 ) replacing MU-SIC photometric redshifts with the up-to-date spectroscopic ones in the case of "secure" redshift determination (according to the scale adopted by  Balestra et al.
FITTING BROAD-BAND SED S In this section we present the SED-fitting analysis performed for the 4343 PACS sources with optical-to-MIR counterparts and either spectroscopic or photometric redshift. Each observed SED has been fitted making use of the MAGPHYS code 6  (da  Cunha et al. 2008 ) and a modified version of this code presented by  Berta et al. (2013).
SED-fitting with MAGPHYS MAGPHYS is a public code which uses physically motivated templates to reproduce the observed SEDs from the ultraviolet to [L O • ] (REDUCED) -2 -1 0 1 2 3 4 Log SFR [M O • yr -1 ] (FULL) -2 -1 0 1 2 3 4 Log SFR [M O • yr -1 ] (REDUCED) Figure 2. Comparison between the values obtained with the full (x-axes) and reduced (y-axes) MAGPHYS library of templates for three different integrated parameters: stellar mass; infrared (8-1000 µm) luminosity due to star formation; and star formation rate. The full grid uses all the available templates of galaxy SEDs encoded in MAGPHYS, whereas the reduced grid accounts for a small fraction of them resulting from a random selection (see text for details). Red triangles are the best-fit values of each parameter as taken from the GOODS-S sample.