dataesr/scientific-paragraphs-categorization huggingface dataset:
Software setting For each experiment presented in this section, we report the average of 6 runs. We use the Linux 3. 9 kernel, along with gcc 4. 6. 3, libgomp 3. 0 and glibc 6. For the analysis, we have selected applications often used to measure the performance of large multicores  [7, 12, 13, 33]. We evaluate 29 applications from the Parsec 2. 1 (precompiled version), the NPB 3. 3 (openMP version), the Mosbench benchmark (Streamflow allocator  [34] ), a set of X-Stream applications  [33] , and the YCSB benchmark  [11]  on Cassandra and MongoDB. For the Xen experiments, we use Xen 4. 5. We hide the NUMA topology to the guest. The dom0 virtual machine is pinned to the CPUs of node 0. As we can only select the NUMA policy for a whole virtual machine, we only run a single benchmark application per virtual machine in all the experiments.
This modification significantly improves the performance of facesim and streamcluster (see the evaluation presented in Section 5. 3. 3). The modification improves facesim by 30% and streamcluster by 55%. We also measure that, after the modification, these applications generate zero intentional context switch per second. We activate thus this optimization for Xen in the single virtual machine experiments for facesim and streamcluster. In order to make a fair evaluation, we also replace the pthread mutex and condition variable functions by a spin loop for facesim and streamcluster in Linux.
Xen+ evaluation Figure  6  reports the overhead of Xen, Xen+ and Linux, relatively to LinuxNUMA. We define LinuxNUMA as a Linux that uses MCS locks for facesim and streamcluster, and that systematically uses the best possible Linux NUMA policy for each application ( � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � ����� ������� ������������ Figure 6. Relative overhead of Linux, Xen and Xen+ as compared to LinuxNUMA (lower is better). offers a better parallelism during a DMA transfer. In Linux, a DMA buffer is allocated as a contiguous physical address space.
Xen+NUMA versus LinuxNUMA In this experiment, we compare Xen+NUMA with Lin-uxNUMA. Xen+NUMA is defined as Xen+ with its best NUMA policy for each application (see column Xen+NUMA of Table  4 ). This experiment has the goal of showing that the large overhead of Xen+ is caused by an inefficient NUMA placement and not by other virtualization overheads. In Xen+NUMA, we use the single virtual machine setting, in which both the threads and the vCPUs are pinned. In Lin-uxNUMA, we pin the threads to the physical CPUs. Figure  10  reports the overhead of Xen+NUMA as compared to LinuxNUMA. We can observe that with efficient NUMA policies, only 4 applications remain degraded by more than 50%, while 14 applications have an overhead of more than 50% with Xen+. This result shows that a large overhead of virtualization on large multicores is caused by the NUMA placement policy.
A diagrammatic representation of the hierarchical architecture of key species, background taxa and LTL groups in the PNCIMA OSMOSE model and of the interactions between these three categories of marine organisms is provided in Figure  2. PNCIMA OSMOSE simulates the life cycles of the 10 populations, from the egg stage to the terminal age, at a time step of three months. At the first time step following the production of eggs, the total number of eggs of each population is split into 120 The reference species of each background taxon (in bold) is used for obtaining mean length and weight at different stages as well as distribution maps. super-individuals called "schools" (Figure  2 ), which are distributed spatially according to input distribution maps. The distribution maps (10 Â 10 km 2 ) are density-based and obtained from geo-referenced data of both commercial fisheries and research surveys (data archives being maintained by Fisheries and Oceans Canada, Pacific Biological Station, Nanaimo, British Columbia).
If more accurate data or estimates become available, we will further validate our ecosystem model against the new information. Through the validation process, we identified optimal ways to incorporate temporal variability into LTL biomass and larval mortality via the TUMI anomalies, and in particular, we were able to reproduce the cycles in the assessed biomasses  (Forrest et al. 2015). In the same manner (i. e. validation testing), other environmental factors (e. g. PDO) and forms of functionality can be investigated to promote a more mechanistic understanding of the ecosystem and to further improve the PNCIMA OSMOSE model. The PNCIMA OSMOSE model generated good fitting to the assessed biomass time series for Herring and Cod, nevertheless, there were notable discrepancies. Aside from the possibility that single-species-based assessments do not account for some processes that an ecosystem assessment would, these discrepancies could be due to a number of reasons.
Firstly, for Herring, preliminary analyses of historical length-at-age data suggested that the von Bertalanffy growth parameters had changed over time  (DFO, 2014) , likely caused by changes in temperature. If such temporal changes are imposed on the growth parameters of the PNCIMA OSMOSE model, the fitting may be improved. Alternatively, the effect of temperature change can be modelled at the individual physiological level (e. g. Vasseur and McCann, 2005). Secondly, although the fitting for Herring has been improved by correlating LTL biomasses with the TUMI anomalies, information on the initiation of upwelling, phytoplankton bloom, and food availability to larvae due to spatiotemporal match and mismatch is still unavailable for further improvement. It is worth noting that in the Strait of Georgia, adjacent to PNCIMA, the timing of the copepod biomass peak has shifted unequivocally earlier every year, which has caused reproductive failure among some marine bird species and may have affected the survival of juvenile fish  (Johannessen and McCarter, 2010).
Acknowledgements This research is funded by the  Strategic Program for Ecosystem-Based Research and Advice in Fisheries and Oceans Canada. We are extremely grateful to the following colleagues (listed alphabetically) who have generously provided different types of data:  Leslie Barton ,  Jennifer Boldt ,  Jaclyn Cleary ,  Kristen Daniel ,  John Ford ,  Robyn Forrest ,  Moira Galbraith ,  Chris Grandin ,  Kendra Holt ,  Jim Irvine ,  Sheena Majewski ,  John Morris ,  R. Ian Perry ,  Ian Stewart ,  Matt Thompson , and  Marc Trudel. We are very grateful to  Jason S. Link  (Editor) and two anonymous journal peer reviewers, as well as  Lisa Christensen  ( Science Advisor  at  Canadian Science Advisory Secretariat ) for providing helpful comments and edits. Y. J. S. and P. V. were supported by the  French  project  EMIBIOS (End-to-end Modelling and Indicators for BIOdiversity Scenarios ) with  Foundation pour la Recherche sur la Biodiversite(FRB , contract no.
15, 16 We have previously described a platform for the production of "off-the-shelf" CAR T cells (UCAR T cells) from unrelated third-party donor T cells by disrupting TCRa constant gene TRAC. 17 By combining the beneficial effects of both PD-1 blockade and CAR therapy, we have not only reduced the effective cost of this adoptive immune therapy but also simplified the protocol of administration as compared to a therapy that would combine the injection of both UCAR T cells and anti-PD-1 antibody. Here, we used the latest in the transcription activator-like effector nuclease (TALEN) technology to develop highly efficient and specific nucleases targeting a key T cell immune checkpoint, PD-1, to improve engineered CAR T cells' functionalities. By taking advantage of unique features of the TALE DNA targeting, its modularity, associated with the independence of the targeting modules, we aimed at replacing natural targeting modules, the repeat variable diresidues (RVDs), with appropriately chosen non-conventional RVDs (RVDs not found in the natural repertoires of TALE) to improve the specificity of targeting for multiplex genome editing.
The uprush and downrush motions are clearly visible in Figure  1a  from the optical textured surface of turbulent swash flow. In the Radon space (Figure  1b ), these motions with opposite direction show peaks at different values of , and alternate as the swash flow reverses; here negative and positive values respectively for uprush and donwrush. The incoming and outgoing motions are separated (see Figure  1a , red and blue features) by back projecting the original signal at negative and positive angles, respectively. In video timestack images, the runup is generally visible as a rapidly moving edge. Based on the RT, the runup can be then defined as the location of the maximum variance of the incoming (periods of uprush) and outgoing (periods of backwash) components. Our algorithm is implemented in Matlab, using on the Image Processing Toolbox, and is available from the authors upon request.
The horizontal resolution obtained from the video data varied from less than 0. 01 to 0. 06 m along the cross-shore transect. The ability of the RT method to detect runup is tested against concurrent LiDAR measurements over a 20-minute period obtained during the Mataquito experiment. The raw LIDAR measurements represent reflection of the laser beams from the beach topography as well as the water surface, without any distinction between the two. Water level is computed as water surface elevation above the bed, computed as the minimum level measured over the 20-min period. The runup edge is detected using a water level threshold, typically around 0. 05-0. 08 m  (Blenkinsopp et al. , 2016; Vousdoukas et al. , 2014; Almeida et al. , 2015) , to cope with noise inherent to LiDAR measurements. Here, a 0.
22 (ssGBLUP_F). The fourth method was ssGBLUP with the metafounder (ssGBLUP_M), using γ estimated by GLS since it turned out to be an accurate method to estimate Ŵ (see the Results section). The four methods used the following inverse relationship matrices: PBLUP: A -1 ; ssGBLUP: H -1 = A -1 + 0 0 0 G -1 a -A -1 22 where G a is as in  [34]  and A -1 is constructed ignor- ing inbreeding  [36] ; ssGBLUP_F: same as ssGB-LUP, with A -1 correctly constructed; ssGBLUP_M: H (γ )-1 = A (γ )-1 + 0 0 0 G -1 -A (γ )-1 22 where G = (M -J)(M -J) ′ /s with s = n/2 (see the "Methods" section) and A (γ ) is as in  [1]. More details are given in "Appendix". For computation, we used blupf90  [37]. In the case of ssGBLUP_M, we constructed H (γ )-1 with own software and then used the option user_file in blupf90 (http://nce.
e. adding more genotyped individuals to the database does not change the existing relationships) and they do not depend on pedigree depth, which by construction is always limited and, in animal breeding, often heterogeneous. Our approach is in fact very similar to using IBS as measure of identity. We used a genomic relationship matrix G = 2(M -J)(M -J) ′ /n , whereas the matrix of IBS is G IBS = G/2 + 11 ′ (see proof in "Appendix"). In GBLUP with associated variance component estimation, when all animals are genotyped, using a model with G IBS or the G matrix proposed here yields identical EBV, as the term ½ in G/2 gets absorbed into the variance component and the constant 11 ′ gets absorbed into the fixed part of the linear mixed model  [7, 46]. However, matrix G rather than G IBS must be used in ssGBLUP_M because G IBS is not compatible with pedi- gree relationships.
Abstract. The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario  and a climate change projection , together with coupled transient runs. A total of 20. 4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1. 5 PB of output data have been produced on Super-MUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany.
About 140 TB of postprocessed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1. 0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate -specifically the Madden-Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with highresolution simulations) or stochastically (in low-resolution simulations).
, 2014)  and the ATHENA  (Kinter III et al. , 2013)  projects. Climate SPHINX was made possible by a considerable amount of computing time provided by PRACE (the Partnership for Advanced Computing in Europe) and data storage from EUDAT (the collaborative pan-European infrastructure providing research data services). We were granted 20 million core hours during a single year at SuperMUC, the IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching near Munich, Germany. Storage of data produced by Climate SPHINX is secured by the EUDAT pilot project DATA SPHINX (DATA Storage and Preservation of High-resolution climate eXperiments), which provides a widely accessible archive for medium-term storage to facilitate data access and discovery. DATA SPHINX is managed by CINECA (the largest Italian computing centre) and at present hosts 140 TB of data generated by Climate SPHINX.
In this paper we describe in detail the important technical aspects of this project and highlight some preliminary scientific results on the impact of increased resolution and stochastic parameterisations on climate simulations. Model configuration and tuning are presented in Sect. 2, while the experimental set-up is described in Sect. 3. Section 4 is devoted to detail the technical configuration. An overview of results and concluding remarks are reported in Sects. 5 and 6, which is followed by the "Data availability" section.
Present-day SST and SIC Given that both FSA and PDA simulations are atmosphereonly runs, a special effort has been taken to provide reliable SSTs in order to fully exploit the high resolution. For PDA, SSTs have been obtained from the daily SST and SIC HadISST2. 1. 1, a pentad-based dataset with a resolution of 0. 25 • × 0. 25 • for SSTs  (Kennedy et al. , 2017 ) and 1 • × 1 • for SIC  (Titchner and Rayner, 2014). These are bilinearly interpolated onto the required reduced Gaussian grid for each resolution: climatologies for SST and SIC for the 1979-2008 period can be seen in the upper panels of Fig. 1. A number of inconsistencies are found between the landsea mask of IFS and of HadISST2. 1. 1; these are due to slightly different coastlines and a different representation of the lakes. For the different coastlines, linear extrapolation from HadISST2.
1. 1 has been performed. For the interior (i. e. lakes), a methodology similar to the one used in ERA20CM dataset  (Hersbach et al. , 2015)  has been adopted: 1-month lagged 2 m temperatures from the ERA-Interim monthly climatology of 1979-2008 are used as SST. Where the temperature is below zero, SIC is set to one, otherwise it is left at zero. This is interpolated in time on a daily basis and in space on the needed grid to create a smoothed seasonal cycle for lakes.
1. 1 SST has been detrended point by point to provide a set of anomalies with realistic variability. Second, the monthly seasonal cycle of the difference between the CMIP5 EC-Earth RCP8. 5 ensemble mean over 2038-2068 (10 members) and the CMIP5 EC-Earth historical ensemble mean over 1979-2008 (10 members) has been computed. This provides for each grid point the average expected SST increase from the present-day to the future period according to a GCM (Global Circulation Model), as a function of calendar month. To account for changes in SST during the FSA period, for each grid point the average trend in SST from the CMIP5 EC-Earth RCP8. 5 integrations for 2038-2068 was also extracted. All CMIP5 EC-Earth data were bilinearly interpolated in space on the HadISST2. 1. 1 grid and linearly in time to daily frequency.
Finally, a new Step1HadISST dataset has been created combining the detrended HadISST2. 1. 1 (expressing the high-resolution daily variability), the average daily change of CMIP5 EC-Earth (from RCP8. 5 and historical, express-ing the expected average temperature increase) and the linear trend of the CMIP5 EC-Earth RCP 8. 5 (expressing the expected future trend in SST). The methodology used here, which shares the main characteristics with the method developed by  Mizuta et al. (2008) , is sketched in Fig. 2. However, the Step1HadISST reconstruction misses an important element; there is no information on the sea ice cover in the future. To account for this, we took data from the CMIP5 EC-Earth simulations as a reference for SIC. CMIP5 EC-Earth simulations show a considerable cold bias in SST with respect to HadISST2.
The lack of information about historical SST under sea ice results in undefined SST at these points using the methodology outlined above. We define these as "bare point". For bare points, we want to make use of the model variability, but we do not want to have inconsistent SST at the boundaries (i. e. where bare points border the Step1Hadisst dataset). Initially, we perform a linear extrapolation for bare points for Step1HadISST SSTs -which gives us a measure of the average SSTs at the bare points. However, these extrapolated values are missing a realistic spatial variability. We then mask the bare points also in the SST field of the CMIP5 EC-Earth ensemble member "r8i1p1", and we subsequently linearly extrapolate new values. We then subtract from the original field of CMIP5 EC-Earth ensemble member "r8i1p1" these new extrapolated values, in order to obtain an anomaly field, which includes the spatial and temporal variability of the SST field over the bare points given by CMIP5 EC-Earth "r8i1p1".
This final field is then added to the linearly extrapolated Step1HadISST SST. Hence, for each day, the SSTs for bare points are given by the EC-Earth CMIP5 RCP8. 5 ensemble member "r8i1p1" SST minus extrapolated EC-Earth CMIP5 RCP8. 5 SSTs plus extrapolated Step1HadISST. The methodology to obtain this specific SST reconstruction is illustrated in Fig. 4. This provides a pattern of SSTs physically consistent with SICs; indeed, it avoids unrealistic values of SSTs in the proximity of the polar cap during winter and -using the CMIP5 EC-Earth data -it provides a reasonable distribution of SSTs in summer, where in the future scenario the sea ice coverage in the Northern Hemisphere often disappears. Moreover, there is no discontinuity at the border with Step1HadISST. The new dataset is defined as FutureHadISST2.
At the end of each simulation leg, a script aimed at post-processing is launched; the script handles both the spectral and reduced Gaussian data from IFS and extracts and converts the requested variables from the default ECMWF format to a user-friendly, CMOR-like format on regular Gaussian grid. With this automatic procedure, more than 140 TB of post-processed data has been produced. A significant reduction of the data volume was obtained making use of the NetCDF-4 Zip format. Monthly (MON), daily (DAY) and 6 h (6HRS) data for different subsets of variables have been produced. More than 50 fields have been stored at monthly frequency. In order to further reduce the space requirements, daily and 6 h threedimensional (3-D) fields have been degraded to the spectral resolution of T255. Additional data at 3 h frequency have been stored for the Euro-Cordex domain (3HRS-CDX) and for a sub-domain including India, Tibet and Pakistan (3HRS-ITP).
Results overview In this section we present a brief overview of the preliminary findings of the Climate SPHINX project. Considering that the number of diverse climate aspects that could be analysed in such a large dataset is large, we decided to present hereafter only a few selected features of the mean climate and its variability. For all the results presented -if not specified differently -the complete set of ensemble members available for the present-day climate (i. e. PDA experiments) has been used.
Mean climate Although a detailed analysis of the mean climate in all the simulations performed would be excessively long to be included in the present work, we introduce a couple of figures showing the sensitivity to resolution and stochastic physics parameterisation of the climatology of precipitation (Fig. 5 ) and 200 hPa zonal wind (Fig. 6 ). We compare the ensemble mean average fields of a low-resolution version (T255) with a high-resolution one (T799), in both its deterministic and stochastic configurations. Data have been interpolated on a common 2. 5 • × 2. 5 • grid. The precipitation model bias -shown in Fig. 5 , with respect to Global Precipitation Climatology Project (GPCP) dataset  (Huffman et al. , 2001)  -is especially strong in Indian Monsoon region, with an excess of precipitation from the Indian Ocean to the western Pacific.
At rain rates near 30 mm day -1 , the simulated frequencies are between about 35 and 50 % of the frequency in GPCP. At higher rain rates, the frequency differences between TRMM and GPCP become comparable in size to or larger than the differences between the modelled frequencies and the observational datasets. We do not know of a reason to strongly prefer one dataset over the other; therefore, we consider the model bias to be uncertain at these rain rates. The frequency of rain rates above 30 mm day -1 in the T159 and T255 models are below about 40 % of that in GPCP. At T511, T799 and T1279 the relative frequency difference compared to GPCP and TRMM decreases as the rain rate increases, and becomes comparable to that in GPCP in the 60-65 mm day -1 interval, though still much smaller than that in TRMM.
Therefore, increasing the model resolution from T159 to T511 improves the simulated frequency of heavy-rainfall events compared to observational datasets, with the further improvements caused by increasing the resolution to T799 or T1279 being considerably smaller. Figure  7c , d show the same data for the stochastic PDA runs. Stochastic physics has a similar effect at all resolutions. Frequencies of rain rates between 5 and 15 mm day -1 are reduced by about 10 % compared to those in the deterministic models, reducing the model bias. Frequencies above about ∼ 20 mm day -1 are substantially increased, by a larger factor at larger rain rates, up to a factor of ∼ 2. 5 at rain rates around 60 mm day -1. This reduces the difference from GPCP and TRMM up to rain rates of 45 mm day -1 at all resolutions.