dataesr/scientific-paragraphs-categorization huggingface dataset:
Such subcomponents are large enough to treat, in terms of concurrency, in the same way as full model components. Balaji et al. (2016)  show results from experiments with the same code family as HiRAM, doing exactly that. Their results show that this extra concurrency can be exploited in a range of ways (to shorten the radiation time step or to speed up model integrations, albeit with a potential increase in resources used). However, the actual performance of the necessary changes, both in terms of the effect on simulation quality and speed, will differ with the different model approaches to information flow. Model formulations also take a range of approaches to providing diagnostic, ensemble, and I/O services, with no common practice as far as we are aware beyond some emerging community use of the XIOS I/O server (http://forge.
Both groups are already exploiting this potential to separately target different architectures (see e. g. the discussion in Sect. 3. 2 below). Both GridTools and PSyclone make use of domainspecific embedded languages (DSELs); i. e. their languages are embedded within an existing language, C++ and Fortran respectively. In both cases this choice was made as it was felt that the combination of available tools and community familiarity bought important benefits, but clearly different choices as to those benefits were made. They also differ in their original target domains. The origin of GridTools was a DSL designed for a finite-difference regular latitude-longitude local area model, whereas PSyclone was designed for a finite-element model with an irregular mesh. However, the GridTools formalism is being extended to support irregular finite-difference models and PSyclone is being extended to support regular finite-difference models.
The approach to optimisation in the two systems is also quite different. In GridTools a particular back end (optimised by an HPC expert) is chosen and the science description is mapped to that particular back end via C++ templates. In PSyclone, an HPC expert specifies a recipe of optimisations which are then used by Psyclone to process the DSL input to produce code optimised for a particular architecture. Despite these differences, both approaches rely on HPC expertise to deliver architecture-dependent performance. The DSL approach expedites the separation of concerns in such a way that computational experts can concentrate on the optimisation for given hardware, allowing different individuals to concentrate on different parts of the problem (e. g. Schulthess, 2015). The use of DSLs has also expedited the involvement of vendors who usually have insight into how best to exploit their hardware.
All three of these dwarfs have limited scalability because they involve data transfer and computations of global or regional fields, which is opposite to the nearest-neighbour principle. By contrast, parameterisations for cloud microphysics and radiation are very compute intensive but scalable, as they are applied to individual vertical columns that can be distributed across both threads and tasks in chunks aligned with the available memory. Hence, performance enhancement options need to be explored differently for each dwarf based on the range of available processor types and considering that all these processes need to be present in an Earth system model. For each dwarf, ESCAPE targets performance on existing and emerging processor technologies (specifically Intel Xeon, Xeon Phi, and NVIDIA GPGPU and a novel technique employing optical interferometry particularly suitable for Fourier transforms), but it is also targeting programming methodologies.
RegularGrid Figure  4. Object orientation and the Grid classes supported in Atlas. Specific model grids can be constructed as instances of one of these classes. For example, the current ECMWF high-resolution (9 km) octahedral reduced Gaussian grid is an instance of the StructuredGrid, but can be referred to by the name Ø1280, which it inherits from the Grid class. ern Fortran interface designed to be maintainable and nonintrusively adapt to emerging hardware technologies and programming models. This is achieved by a design with a clear separation of concerns that, for example, provides a clean interface for exploitation by a DSL and allows multiple back ends for actual field data storage: a CPU back end and a GPU back end with the capability to synchronise with each other seamlessly. The Atlas library provides a toolkit of functionalities ranging from low-level field containers to high-level mathematical operators and remapping operators.
A Spectral FunctionSpace describes a field with spectral coefficients. Additional Func-tionSpace types can be created to describe discretisation for continuous or discontinuous spectral element methods (e. g. Marras et al. , 2016). Within ESCAPE, Atlas is being used as the foundation for the evolution of the NWP and climate dwarfs. As a consequence Atlas acquired improved mesh-coarsening strategies, a GPU-capable Field data storage, and support for regional grids with projections and checkerboard domain decomposition. Clean interfaces in both Atlas and GridTools are being used to exploit Atlas memory storage to allow GridTools to compute stencil operations on unstructured meshes.
MPI libraries, coupling, and I/O All methods of coarse-grained parallelisation which do not use global address spaces exploit the MPI paradigm, whether it is managing domain decomposition, coupling components, or exploiting I/O servers. Exploiting MPI directly is difficult, and models typically exploit some sort of middleware library such as Generalised COMmunications (GCOM) used by the Met Office UM or YAXT (Yet Another eXchange Tool; https://doc. redmine. dkrz. de/yaxt/html/da/d3c/concept. html, last access: 29 April 2018) under testing for use in ICON. Such tools can simplify Figure  6. The influence of using a library to manage MPI communications for domain decomposition: experiences using YAXT to replace the existing ECHAM usage of MPI in a T63L47 model. The YAXT optimised library (green) can lead to factors of 10 improvement in the time taken to do transpositions of data over simple implementations (blue) and factors of 2 over manual optimisation (red, which was effectively an aggregation of multiple transpositions into bigger messages).
In this case the two main optimisations in YAXT were avoiding additional data copies by the usage of MPI data types and reordering of messages to avoid congestion, with the latter becoming more important with higher core counts. the usage of MPI and therefore make the performance potential of data layout and data independence accessible (see e. g. Fig. 6 ). The complexity of data transpositions and MPI communications increases in the context of coupling, and these also need to be understood in terms of how best to obtain performance. Many groups are investing in coupling tools, such as the OASIS coupler widely exploited in Europe. Recognising that there is much to be learnt from these various activities, the community has held a series of workshops on coupling, which led to the identification of a range of parameters which influence coupling and can be used in a benchmarking strategy (Table  2 ).
Such information is normally provided by performance tools. Performance tools can provide the support and the freedom to the developer to view all the different levels of concurrency (from the hardware counters underpinning fine-grained concurrency to the load-balancing involved in coarse-grained concurrency). While it is possible to collect a vast amount of information by relinking entire codes to performance monitoring libraries, results are often enhanced by manually identifying hot spots and extracting additional trace information, and automatically generated call trees can be enhanced by additional instrumentation. In particular, like automatic code migration tools, the tools which generate trace files, such as the EXTRAE library underpinning the generation of trace files for PARAVER or DIMEMAS, depend on an internal understanding of the programming models in use in the target codes.
It is this understanding that allows them to automatically trap library calls and read hardware counters without code invasion, but it is precisely because of this dependence on generalised programming models that extra manual intervention can be necessary to capture domain-dependent information in trace files. Figure  7. Understanding model performance on existing machines generally requires instrumenting the model code to output "trace" information that can be analysed and visualised by a dedicated application. This information can also be used, when combined with machine information, to predict performance (issues) on alternative architectures. This figure depicts the use of PARAVER  (Labarta et al. , 2005)  for collecting trace information and DIMEMAS  (Gonzalez et al. , 2011)  for simulating performance on alternative architectures.
Related work Weather and climate computing is not alone in facing these problems. For example, the notion of DSLs as a solution has a tried and tested heritage -examples include the Kokkos array library  (Edwards et al. , 2012) , which like GridTools uses C++ templates to provide an interface to distributed data which can support multiple hardware back ends, and from computational chemistry, sophisticated codes  (Valiev et al. , 2010)  built on top of a toolkit  (Nieplocha et al. , 2006) , which facilitates shared memory programming. Arguably DSLs are starting to be more prevalent because of the advent of better tooling for their development and because the code they generate can be better optimised by autotuning  (Gropp and Snir, 2013). However, in our case, we still believe that human ex- pertise has a primary role to play, but this expertise can be better deployed by supporting DSLs than entire code bases.
We have introduced a range of such tools, libraries, and methodologies for making progress such as the identification of dwarfs, the publication of data structures, and the elements necessary to establish a comparison of coupling technologies, and to evaluate performance. Within some of that work there is a clear aim to develop and specify interfaces that can be reused in different ways (for example, the interfaces which allow GridTools to interface directly with the Atlas data structures), but many of these (and similar) projects have still mainly been developed by small groups who have not had the time, mandate, or wherewithal to publicly promulgate requests for requirements or specifications and then build to documented interfaces. Progressing at the community level will require improved methods to allow the community to discuss, specify, design, develop, maintain, and document the necessary libraries and tools.
Such a taxonomy will cover at least the following: tools for exposing mathematical algorithms for implementation on a sphere (domain-specific languages), tools for describing and using data models for the variables in those algorithms (including stencils for computation), mathematical libraries such as fast Fourier and spherical transforms, solvers which can exploit specific data models and algorithms, interpolation and regridding libraries, embedded and stand-alone visualisation tools, -exchange libraries (for problems ranging from domain halo exchanges to 3-D field exchange between highlevel components), fully fledged couplers (e. g. OASIS) and frameworks (e. g. ESMF), -I/O servers (such as XIOS), data assimilation tools such as minimisers and adjoint compilers, clock, calendar, time-stepping, and event-handling libraries (events such as at "do at first time step" or "do every 3 h"), testing frameworks, performance and debugging tools, and domain-specific tools for automatic code documentation.
Acknowledgements. This paper reports European research funded by the following  FW7  and  H2020  research and innovation projects:  IS-ENES2  under grant agreement  312979 ;  ESCAPE  under grant agreement no. 671627 ; and  ESIWACE  under grant agreement  675191. The authors acknowledge useful conversations with and input from  Venkatramani Balaji ,  Terry Davies ,  Peter Fox ,  Rich Loft ,  Nigel Wood , and  Andy Brown  and the input of other participants at the "Crossing the Chasm" meeting, in particular  Jean-Claude Andre ,  Joachim Biercamp ,  Antonio Cofiño ,  Marie-Alice Foujols ,  Sylvie Joussaume ,  Grenville Lister ,  Alastair Mckinstry ,  Annette Osprey ,  Øyvind Seland , and  Manuel Vega. Edited by: Steve Easterbrook Reviewed by: two anonymous referees.
structure of tropical and subtropical forests in the long term. 2. We compiled forest structural features (stem density, basal area, mean canopy height and maximum tree size) for plants ≥10 cm in diameter at breast height from published forest inventory data (438 plots ≥0. 1 ha, pooled into 250 1 × 1-degree grid cells) located in dry and humid forests. We computed maps of cyclone frequency and energy released by cyclones per unit area (power dissipation index, PDI) using a high-resolution historical database of TCs trajectories and intensities. We then tested the relationship between PDI and forest structural features using multivariate linear models, controlling for climate (mean annual temperature and water availability) and human disturbance (human foot print). 3. Forests subject to frequent cyclones (at least one TCs per decade) and high PDI exhibited higher stem density and basal area, and lower canopy heights.
However, a pantropical test for these invisible, medium-to long-term effects  (Lugo, 2008)  on forest structure is still lacking. The availability of high-resolution historical databases of TCs trajectories and intensities such as the International Best Track Archive for Climate Stewardship (IBTrACS;  Knapp, Kruk, Levinson, Diamond, & Neumann, 2010)  allows to objectively quantify the cyclone regime at any location (e. g. ,  Schreck et al. , 2014). The frequency or the intensity of TCs (i. e. , the maximum sustained wind speed) is often used to estimate the level of disturbance induced by TCs in a given area. However, it has been suggested that this level of disturbance (or the destructiveness) is better described by the energy transferred by TCs to the land surface, as this parameter combines the frequency, intensity, size and translation speed of TCs  (Camargo & Sobel, 2005; Emanuel, 2005).
| Structural features We compiled the location, stem density (number of stems per ha), basal area (the total cross section area in m 2 /ha, which is a good proxy of above-ground biomass; e. g. ,  Slik et al. , 2010) , maximum size (maximum DBH, diameter at breast height, cm), mean canopy height (m) and maximum height for plants with stem diameter ≥10 cm at ~1. 3 m above the base (DBH) from published inventories for plots ≥0. 1 ha in size  (Ibanez et al. , 2018). Relevant literature was identified using key word searches in ISI Web of Science (https://webofknowledge. com/) and Google Scholar (http://scholar. google. com/). Our final dataset was derived from 88 scientific papers published between 1983 and 2017. Montane forest plots (as defined by authors of source data, i. e. , "pre-montane," "lower-montane," "montane" and "upper-montane") were not considered in the analysis  (Aiba and Kitayama, 1999; Clark et al.
| Cyclones The frequency and energy released by TCs were computed over the 1981-2016 period from the IBTrACs database, an exhaustive and global database (https://www. ncdc. noaa. gov/ibtracs/, see  Knapp et al. , 2010). We assumed these values, derived from a 35-year period, to be representative of the relative TC regime in different regions over the Holocene on a global scale. Although some geomorphological records in TC-prone regions show centennial to millennial variations in local TC frequency and intensity during the Holocene, these variation are pseudo-cyclic and do not indicate long-term trends (e. g. ,  Burn & Palmer, 2015; Haig, Nott, & Reichart, 2014; Nott & Forsyth, 2012; Toomey, Donnelly, & Woodruff, 2013). Instead, global patterns of TCs across different regions vary less on a centennial scale but respond more strongly to major changes in global climate over longer time-scales, such as the Pliocene glaciations (e.
g. ,  Yoo, Galewsky, Camargo, Korty, & Zamora, 2016). The IBTrACs database provides information on the position, maximum sustained wind speed and translation speed (an indicator of the duration of disturbance impact, as slower moving cyclones imply longer presence of damaging winds) of each inventoried cyclone at 6-hr intervals. We used these data to calculate the average frequency and energy released by TCs was computed as a proxy of the level of TC disturbance. For each grid cell with a spatial resolution of 1° latitude × 1° longitude (≈12,345 km 2 at the equator, Figure  1 ), the energy released by TCs was computed as the power dissipated by friction in the surface layer  (Emanuel, 2005)  following  Menkes et al. (2016)  and  Vincent et al. (2012). At each point, the maximum 10-min sustained wind speeds at 10 m above the ground (V) was extracted from the database.
| Analysis All analyses were performed using R. 3. 3. 0 (R Core Team, 2016). All explanatory variables (except TC frequency and PDI) were extracted on 10-min (~340 km 2 ) spatial resolution maps. To avoid pseudo-replication, we averaged structural and climatic features within 1 × 1 degree grid cells (i. e. , the spatial resolution of the PDI maps) and forest type (dry and humid forests). We used a total of 438 plots (355 in humid forests, 83 in dry forests) that were pooled into 250 grid cells (203 in humid forests, 47 in dry forests, Figure  1  and Figure  S1. 2). Multicollinearity was <0. 5 (Spearman's rho) for all explanatory variables (Table  S1. 1). We first tested whether structural features differed between areas experiencing low-(freq. <0. 01 TC/year, i. e. , less than one TC per century is expected based on the 35-year IBTrACs dataset), medium-(0.
4). We used the MuMIn r package  (Bartoń, 2016)  and the dredge function to generate different sets of models representing all possible combinations and subsets of explanatory variables. We then selected the best models based on the Akaike information criterion (ΔAIC <2 from the best models,  Bunnefeld & Phillimore, 2012). Explanatory variables were centred and scaled before fitting models to allow a fair comparison of their respective effects.
| D ISCUSS I ON Therefore, forecasted changes in the intensities, sizes and trajectories of TCs are likely to have important impacts on the structure of tropical and subtropical forests  (Kossin, Emanuel, & Vecchi, 2014; Mei, Xie, Primeau, McWilliams, & Pasquero, 2015). Our results explained a considerable proportion of the variation in stem density and canopy height in our dataset, suggesting that TCs are important large-scale disturbances that have long-term impacts on forests structure. Our analysis did not capture edaphic and topographic variation known to impact forest structure from local to regional scales, which makes the strong relationship we found between cyclone and forest structure even more remarkable. Indeed, soil depth, nutrient availability and topographical position are known to affect forest structure (e.
Plots were classified as dry or humid forest plots following authors' classifications, with tropical dry forests being identified by the common occurrence of drought-deciduous canopy species. For each plot, we extracted the corresponding TC frequency and energy released by TCs (PDI). We also computed the MAT, mean annual precipitation (MAP), annual potential transpiration (PET) and a water availability index as MAP-PET. MAP and MAT where extracted from the WorldClim 2. 0 database (http://www. worldclim. org/version2, Fick & Hijmans, 2017)  and PET from the CGIAR-CSI database (http:// www. cgiar-csi. org/, Trabucco & Zomer, 2009).
N OWLED G EM ENTS T. I. was funded by the Agency for Economic and Environmental Development (DDEE) of the North Province of New Caledonia and G. K. was supported by an Alexander von Humboldt fellowship. We thank all people who contributed to collecting plot data. We are notably most grateful to the late Alwyn H. Gentry, the Missouri Botanical Garden, and collectors who assisted Gentry in several studied islands. We thank two anonymous reviewers and Peter Bellingham for helpful comments on the manuscript. AUTH O R S' CO NTR I B UTI O N S T. I. , G. K. and P. B. conceived of the idea; T. I. , G. K. , T. W. G. , G. R. and P. B. compiled the forest inventory data; C. M. and M. L. computed the tropical cyclone maps; T. I. , G. K. and M. M. analysed the data; T. I. and G. K. interpreted the results and drafted the paper.
Red dots represent C:G > T:A mutations, and black dots represent the other types of mutations. For Circos A, only the mutations that occur in genes present in the Cancer Gene Census list from the COSMIC database are displayed, with the number of recurrent mutations in these genes in parentheses. (B) For the epigenetic drivers/modifiers, only the mutations that occur in the epi-driver or the epi-modifier gene lists are displayed. Blue gene names correspond to genes that are only involved in epigenetic processes, and purple gene names correspond to genes that are involved in epigenetic processes and that are present in the Cancer Gene Census list. The total number of recurrent mutations occurring in each of these genes is also displayed in parentheses. https://doi. org/10. 1371/journal. ppat.
The UV irradiation protocol was based on the data described in  [40]  and to mimic the situation in humans. For instance, the maximum dose of the UV irradiation protocol, 450 mJ/cm 2 , corresponds to 50 minutes of solar exposure in July in Paris. The tumour incidence (tumour bearers/group) was recorded weekly. Tumours were identified first macroscopically and by histological diagnosis. After 30 weeks, or earlier if the tumour reached the ethically allowed maximal size, the animals were sacrificed and H&E-stained sections of dorsal skin were used for histological diagnosis.
Exome analysis The quality of the raw reads was estimated with FastQC software (version 0. 11. 5, http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Reads were mapped to the GRCm38 Mouse reference genome (ftp://hgdownload. cse. ucsc. edu/goldenPath/mm10/) using Burrows-Wheeler Aligner (BWA, http://bio-bwa. sourceforge. net/) version 0. 7. 15 and producing a BAM file. The following GATK Best Practice Recommendations were applied to the BAM files to improve variant detection quality. Picard (version 2. 4. 1, https://broadinstitute. github. io/picard/) SortSAM was used to sort and index BAM files, and the AddOrReplaceR-eadGroups tool was used to replace all read groups with a single new read group. The duplicate reads were marked with the MarkDuplicates tool from Picard, and the newly produced BAM file was indexed with the BuildBamIndex tool.
GATK (version 3. 6. 0, https://software. broadinstitute. org/gatk/download/) RealignerTargetCreator was used to determine the position concerned by local realignment, and IndelRealigner was used to perform local realignment around these sites. The GATK BaseRecalibrator tool was used to detect systematic errors in base quality scores. Dbsnp and dbindel (version 142) for the mm10 reference genome was downloaded from the Sanger website (ftp://ftp-mouse. sanger. ac. uk/REL-1505-SNPs_Indels/) and considered as input. Lastly, the index of the output BAM file was created with Picard BuildBamIndex, and GATK PrintReads was used to write out sequence read data. The quality of the alignment was estimated with Qualimap (version 2. 0. 2, http://qualimap. bioinfo. cipf. es/). Then the variant calling was done with Mutect (version 1.