dataesr/scientific-paragraphs-categorization huggingface dataset:
GNTRING is used to transfer page identifiers retrieved by GNTADDR. The resulting tool is named RINGADDR. These tools were evaluated with raw ping-pong data transfers, sending datasets of different sizes and waiting for the acknowledgment signal. For each size, we ran hundred transfers and we computed averages. Ring buffers have a size of 1 MB (256 pages). These tests were conducted on a hexacore CPU (AMD Phenom II X6 1090T 3. 2 GHz) with 8 GB of RAM. We used Xen v4. 3 hypervisor. The virtual machine has 2 GB of RAM and two CPU cores. Ubuntu v3. 2 is the operating system for all domains. We consider TCP transfer mechanism as our reference as it is the standard communication mechanism between machines. Firstly, we observe that XenSocket is more efficient than TCP, thanks to its internal ring buffer which overlaps read and write operations.
Low Power SoC New embedded platforms, where CPU and GPU are shipped together on the same die (SoC) and share the same memory space, offer two main new perspectives. The first one is a capability to only transfer data owner grant between CPU and GPU, avoiding costly data duplication. The second one is to perform the same amount of workload on a low-power platform, expecting comparable performances with much less energy: new TegraK1 SoC's platform, which have equivalent computation capability (4 ARM cores + 192 GPU cores), but needs only 11W compared to test-platform (50W for CPU + 188W for GPU). In embedded systems (smartphones, tablets), the low power processors market is mainly dominated by ARM SoC. ARM architecture is RISC, so executable code is little bigger (20%) but core hardware architecture is less complex than x86 counterparts.
Globally ARM cores need less energy than x86 core equivalents. ARM Cortex CPUs, and MALI GPU companion, are distributed as system on chip (SoC) design in order to be implemented by external founders but they are free to mix different kind of CPU and GPU cores on chips. SoC technologies enable CPU and GPU but also memory and network blocks to be closely coupled on one chip. This technology could throw away two main hardware bottlenecks: • Memory space becomes shared between CPUs and GPU. This improvement can eliminate time consuming data duplication between processor and accelerator. • Hardware interfaces (PCI/PCIe) are replaced by direct links (routed lines), more efficient and less power consuming than external chipset (north or south bridge). Anyway, this technology imposes some limitation, essentially about the lack of flexibility: • Memory space is not expandable: current accelerators embed no more than 12 GB.
Discussion According to  [55] , we estimate that processing servers represent around 70% of total datacenter energy consumption, while connection links and switches account for 30%. It means that efforts to reduce energy consumption must focus on computing servers and the same trend should be followed for data-center servers. Cooling is also a major energy consumer, but is not taken into account here. Its impact can be considered as a ratio penalty, linearly correlated with power of computing and networking components. Green 500 and Green Graph 500 are mainly relevant in the domain of scientific computing, but what about most of data centers? One fact is that GPU accelerators can be now considered as valuable accelerators but they still need a larger adoption, especially in industrial and business applications.
One of the most common usages of data centers is hosting enterprise information (ERP, CRM, mail or web servers, cloud, etc. ) and commercial applications (e-business, finance and telecoms). Virtualization and services consolidation in these data centers become a common trend to reduce energy consumption by focusing workloads on fewer physical servers often save 40 to 80%  [75]. In this matter, we point the lack of a solution which combines advantages of virtualized architectures and hardware accelerators. GPUs benefit from much more computation power at the same order of energy consumption than classical CPUs. Except in some application fields like video games, graphical editing or HPC, GPUs are currently under exploited. There is indeed a considerable amount of scientific publications about exploitation of GPUs, either in computing power and in energy efficiency, for scientific simulations.
The E s even increases on rhodo (Table  4 ) as contamination is introduced due to the extra read pair information. Although average number of misassemblies increases slightly for BESST-v2 with the contamination level in the GAGE runs, the difference is relatively small. In fact, the number of misassemblies at c added = 40 made by BESST-v2 are competitive also at c added = 0 with other scaffolders. The tradeoff between misassemblies and increase in E s gives BESST-v2 favorable results to the other scaffolders. Table  5  for c added = 0 shows that BESST-v2 lowers the misassemblies with almost 40%, with the most significant decrease on ABySS, ABySS2, SGA and Velvet. This suggests that a large part of the edges formed in a fragmented contig graph comes from PE contamination. Notably, BESST-v2 also improves scaffolding compared with integrated scaffolder across almost all data sets, Table  S4 ,S7,S12.
Constraints analysis To provide network access in remote areas, it will be deployed the cognitive radio technology in rural areas but also find a link between this technology and traditional networks (i. e. Backbone). This link is the point of connection or switching between traditional networks and the newly deployed cognitive radio networks. Although the impact in terms of interference on the TV users is minimal in rural areas due to the very low number of TV, signals from cognitive radio transmitters could cause enormous interference around the connecting areas near cities and most populated zones. We therefore performed simulations to assess the importance of these disturbing noise   3. Simulation parameters through the network simulator SEAMCAT. SEAMCAT is a free of charge integrated software tool based on the Monte-Carlo simulation method.
To avoid, for example, interfering with the TV users, we define a set of actions that will be implemented on the cognitive radio base station (or cognitive radio relay antenna). In general, these functions allow antennas to react to various changes that can impact the radio environment such as changes in the air index (medium wave propagation) because of the rain for example, or following the arrival of TV users. Currently, we have defined two functions. The objective of the first function (decreaseTrPower) is to adjust the transmission power of the antenna. As for the second one (moveToBand), it allows antennas to change their transmission channel. Each of the defined two functions was expressed by the rules of logic programming which are then translated to one computer programming language.
This served to justify the development, beginning in 1999, of a more complex model that includes three limiting nutrients (Fe, Si, P), two phytoplankton and two zooplankton size classes. This model was called HAMOCC5  (Aumont et al. , 2003) , as it was based on HAMOCC3. 1  (Six and Maier-Reimer, 1996)  and used in the LSG model  (Maier-Reimer et al. , 1993). When this code was embedded in the ocean model Ocean PArallélisé (OPA)  (Madec et al. , 1998) , it required some major changes and improvements, partly because of the much finer vertical resolution. In addition to the numerical schemes, these changes were mostly an im-proved treatment of the optics and the separation of the particulate organic matter into two different size classes. All these changes and the major recodings it required led us to adopt a new name for the model: PISCES.
Changes from previous release As already mentioned, PISCES as a research tool is in perpetual evolution. Numerous changes have been made relative to the previously documented version, PISCES-v1. A brief list of the main changes is made below, with these changes organized thematically. These changes are detailed in the following sections. -Changes made to the code structure and design: a. Transition to full native Fortran 90 coding. The model has also undergone a reorganization of its architecture and coding conventions following the evolution of NEMO. b. I/O interface should now be set by default to IOM (the new input-output manager of the NEMO modeling system) to benefit from the major improvements this interface offers. c. Memory and performance improvements have been made. This version should run slightly faster and take much less memory than v1.
In order to take sub-model grid scale bathymetric variations into account in the Fe source function, the model grid structure has been compared with the highresolution ETOPO5 data set. An algorithm was developed whereby for each and every horizontal grid cell, the corresponding region in the ETOPO5 data set is considered. For each vertical level in the model corresponding to a particular horizontal grid point, the corresponding ocean-bottom area from ETOPO5 (in fractional units) is saved, with the end result being a three-dimensional array containing an equivalent area for the bottom bathymetry of the ocean for the ETOPO5 data set. The iron flux computed as described above is then multiplied by this fractional area % sed (which varies between 0 and 1): F sed Fe = F sed Fe,max × Fsed × % sed.
Vertical mixing is parameterized using the turbulent kinetic energy (TKE) scheme of  Gaspar et al. (1990) , as modified by  Madec (2008). The fields used to drive the ocean are identical to those used by  Aumont and Bopp (2006). However, the resulting physical circulation state simulated by the ocean model is different as several new parameterizations and new algorithms have been included in ORCA2-LIM. Climatological atmospheric forcing fields have been constructed from various data sets consisting of daily NCEP/NCAR 2 m atmospheric temperature averaged over  1948 -2003 (Kalnay et al. , 1996)) , monthly relative humidity  (Trenberth et al. , 1989) , monthly ISCCP total cloudiness averaged over  1983 -2001 (Rossow and Schiffer, 1999) , monthly precipitation averaged over  1979 -2001 (Xin and Arkin, 1997)  and weekly wind stress based on European Remote-Sensing Satellite (ERS) satellite product and TAO observations  (Menkes et al.
Iron Figure  8  shows the distribution of iron at three different depth ranges for the model and for the observations. The observational distributions come from the recently published database of  Tagliabue et al. (2012)  augmented with about 1000 recent observations. The data set can be downloaded from http://pcwww. liv. ac. uk/~atagliab. A complete and exhaustive validation of the model is made difficult by the relative sparsity of the data. As expected, the highest concentrations of iron in the open ocean are found in the subtropical North Atlantic Ocean and in the Arabian Sea. Those high values are produced by the enhanced dust deposition, mainly emanating from the Sahara desert. The model tends to underestimate the maximum values found in both basins. Interestingly, the local minimum, which is observed west off Mauritania just below the maximum Saharan dust plume, is well captured by the model.
Nutrients, oxygen, alkalinity and DIC In this section, the simulated distributions of macronutrients, oxygen, alkalinity and DIC are evaluated against available observations. The observations comprise the World Ocean Atlas 2009 for nutrients and oxygen  (Garcia et al. , 2010) , and the GLobal Ocean Data Analysis Project (GLODAP) database for DIC and alkalinity  (Key et al. , 2004). Figures 9 and 10 show the surface distributions of nitrate and silicate and zonally averaged sections in the Atlantic and Pacific oceans. At the surface, the model compares quite well with the observations, especially for nitrate. Nitrate concentrations seem to be slightly overestimated along the Antarctic   (Garcia et al. , 2010). Panels are the same as on Fig. 9. coast. However, as most of the data have been collected during the productive season in this region, the climatology is likely to be biased toward low values.
Through a reinspection of Fig. 8 , we can see that this weak bias is not surprising. In particular, the gradients from the coastal regions to the open ocean are generally too small. This suggests that the sediment source of iron is too small and should either be increased and/or made more variable. For the carbonate system, the predicted spatial variability is overestimated, in particular in the interior of the ocean. In fact, the data distribution which has been used to produce the observed climatology is rather coarse  (Key et al. , 2004). As a consequence, the interpolation procedure strongly smooths the DIC and alkalinity distribution. Thus, the GLODAP database probably underestimates the real variability of these tracers. To avoid this problem, we should have used a non-interpolated data product as for iron or mesozooplankton.
2b). In this formulation, the light saturation parameter E k directly depends on temperature and nutrient limitation. Thus, since the Q 10 of phytoplankton is close to 2, E k is then predicted to be 6 to 8 times smaller in the very high latitudes than in the tropical Black dot corresponds to NO 3 , brown dot to O 2 , red dot to PO 4 , green dot to SiO 3 , light-blue dot to DIC, purple dot to alkalinity and gray dot to iron. The additional purple dot labeled as Alk-Lee uses the database constructed by  Lee et al. (2006)  to compare with the model. domain. Furthermore, in the very oligotrophic regions, such as the central subtropical gyres, E k is close to 0 as a consequence of a very intense nutrient limitation. In the LIGHT experiment, the initial slopes of P -I curves have been prescribed so that the resulting E k are identical to those of the standard case at 15 • C for no nutrient limitation.
In addition to the description of the lower trophic levels of marine ecosystems, and the biogeochemical cycles of carbon and of the main nutrients (N, P, Si, Fe), as described in this manuscript, a few additional modules have been embedded into PISCES. These modules enable the model to compute the cycles of climate-relevant gases emitted by the ocean such as dimethylsulfide (DMS)  (Bopp et al. , 2008) , and nitrous oxide (N 2 O)  (Martinez-Rey et al. , 2015). An explicit representation of paleo-proxies, such as δ 13 C  (Tagliabue et al. , 2009b) , Pa / Th  (Dutay et al. , 2009) , Nd  (Arsouze et al. , 2009) , is also available. PISCES is still in a phase of active development despite the fact that its development started more than 10 years ago. Avenues for future improvements are large and numerous and concern all aspects of the model.
O. Aumont et al. : A description of PISCES-v2 Appendix A: Model structure The model is being coded in FORTRAN 90. To activate PISCES, the cpp key key_pisces should be declared. Only the subroutines that compute the biological or chemical sources and sinks are considered to be part of PISCES. Thus, this excludes the computation of the advection-diffusion equation (the transport of the tracers), as it is not specific to PISCES. There are two types of subroutines: the initialization of the tracers and of the parameters and the computation of the various biogeochemical sources and sinks. The latter PISCES subroutines are called from within the ocean model time loop. The objective here is not to precisely detail the PISCES code but rather to list the different modules and to briefly describe their role.
Acknowledgements. The authors are grateful to the whole community of PISCES users. The model would be useless without them. In particular, we thank  Thomas Gorguès ,  Keith B. Rodgers ,  Coralie Perruche ,  Christophe Menkès ,  Vincent Echevin ,  Gildas Cambon  and the whole NEMO system team for their precious help, expertise and support which made the release of this version possible. O. Aumont,  L. Bopp ,  M. Gehlen  were supported by  ANR-CEP09  MACROES. O. Aumont received additional support from the  Labex Mer  via grant  ANR-10-LABX-19-01. Olivier Aumont will be eternally grateful to  Ernst Maier-Reimer. Even  if he did not directly participate in the design of PISCES, nothing would have been possible without him. PISCES was built upon  HAMOCC3  and  HAMOCC3. 1 and still some parts of the current code of PISCES come from these models.
Subsequently, we quantified the DiD intensity in each cell by TransQuant, a software we programmed for this purpose (https://github. com/dominikfr/Transquant). This software recognises donor cells based on their high DiD intensity and control areas by the lack of a donor cell. The volume of the donor cell, based on the DiD signal and watershed volume from CellSegm, was dilated by one voxel to avoid falsely quantifying parts of the donor cell as transfer. DiD signal located inside acceptor cells was found based on the cell volumes defined by the cell segmentation. A threshold was set to eliminate signal due to autofluorescence, above which signal was considered to be intercellular transfer (Fig. 1F ). Acceptor cells with a very small volume were automatically excluded by the software in order to avoid measuring increased auto-fluorescence of dead or dying cells as transfer.
Automation of image acquisition. In order to facilitate large scale high-throughput screening formats, we automated the image acquisition using Leica MatrixScreener ® and an in-house pattern-recognition program written in MATLAB (DonorFind). First, using a matrix of the part of the 24-well plate, which is within the scan range of the microscope (Supplementary Fig. S4A  online), an autofocus map is acquired. Second, a low-resolution scan of Hoechst and DiD channels covering entire wells in 2D is acquired. Individual images are assembled into super-images spanning the entire well. Then, regions of interest identifying either single donor cells or no donor cells at all are determined with DonorFind. The coordinates of these regions are sent back to the microscope, and higher resolution 3D stacks in Hoechst, WGA, EGFP and DiD channels are acquired at the specified position (Supplementary Fig.
Discussion We present here a novel, microscopy-based method for automated quantification of codeIT. In our low-ratio confluent cell culture approach, codeIT is increased due to the larger contact area between cells, compared to commonly employed low-density approaches. Using a very low donor to acceptor cell ratio, the contribution of transfer through the medium was minimised and codeIT could be assigned to specific donor cells. Cells in image stacks were segmented by CellSegm 34 in 3D. This enabled donor cell recognition and allowed integration of transfer intensity for each individual cell by our newly developed software TransQuant. The presented method is superior to quantification of intercellular transfer by flow cytometry in several aspects. It 1) excludes signal resulting from the uptake of apoptotic cells, 2) differentiates between signals from inside and outside acceptor cells, 3) assigns transfer events to specific donor cells and 4) monitors cell morphology, expression levels, spatial arrangement of cells and parameters of transferred material such as density, distribution and size.
Automated image analysis. Cell segmentation and quantification of transfer: All algorithms for segmentation and automated analysis were written in MATLAB. The cell segmentation was done with CellSegm which was published previously  72  and included the pre-processing step of anisotropic filtering, marker generation, watershed segmentation and classification of the watershed regions into background or cells. After watershed segmentation, the transfer of signal was analysed with TransQuant, which can be downloaded (https://github. com/dominikfr/Transquant). The algorithm automatically detects a DiD donor cell by high signal intensity. In absence of any donor cell, the stack is marked as a control stack. Small objects with a volume of less than 1,000 μ m 3 are discarded during donor cell definition.
Statistical analysis. For each experiment we normalized the integrated fluorescent intensity of codeIT to the respective control condition (EGFP expressing or control siRNA transfected cells), which was set to 100%. Since the amount of codeIT originating from the different donor cells was not normal distributed (Fig. 2B ), the data was transformed logarithmically (to the base e; i. e. the natural logarithm, i. e. using Euler's number) for significance testing. This transformation yielded a normal distribution (Test for normality: Chi-square test, Fig. 2C ). All statistical tests used to detect differences between experimental conditions were performed on this transformed data. For multiple comparisons, ANOVA was used to test if significant differences exist between the datasets, followed by post-hoc Dunnett's test to identify the different dataset.
Automated image acquisition. We automatized the image acquisition on a Leica SP5 confocal laser scanning microscope with the help of Leica MatrixScreener software (version 3) in combination with our own DonorFind program (https://github. com/dominikfr/Transquant). MatrixScreener and DonorFind communicate through a TCP/IP port and the CAM module from Leica. We produced a scanning template for a low resolution screen with a 20x objective (HCX PL APO CS 20. 0 × 0. 70NA DRY UV), speed 400 Hz, 1x zoom, covering the entire reachable well area for a 24 well plate. Format: 87*142 scan fields of 775 μ m width each (Fig  S5A ). Into each field we placed autofocus points as regular grid with 3 scan fields distance, at least 2 scan fields distance from the well rim. Setup within the Matrix Screener: Matrix Screener; Single rectangular Matrix; Setup Template: start coordinates: X: 9,700, Y: 7,500, field distance: 775 μ m, dz offset: 20, AF (autofocus) job: Z-Galvo; first channel only; analyse type: Intensity based method; excitation 405 nm, pinhole airy 2, capture range 60 μ m, steps 30.
Format 64 × 64 pixels. Low resolution job: Link low resolution scanning job to autofocus job; xyt; zoom 1; excitation: 405 nm, 561 nm, 633 nm. Format 512 × 512 pixels. High resolution job: Link to autofocus job. Zoom 3. 4; pinhole airy 1, xyz; sequential scan; Excitation: a) 405 nm, 561nm, 633 nm. Stack, sequential scan: from 15-65 μ m, 1 μ m z-distance, format 512 × 512 pixels, line average 16. Waiting job: is an empty job without defined parameters that has to be set after each well. First, all wells are marked as low resolution job and one scan field after each horizontal line of wells is marked as a Waiting job. The autofocus map is started manually. After completion of the autofocus map, the low resolution job is started from MATLAB via DonorFind. The waiting job after each horizontal line of wells has the function as a trigger to start pattern analysis by DonorFind.
Acknowledgements We are very grateful for help with establishing the automatic imaging procedure from  Volker Hilsenstein ,  Advanced Light Microscopy Facility, EMBL Heidelberg. We thank  Marianne Enger  for cell sorting and  Eva Britt Erlandsson ,  Benedikte Rosenlund  and  Khanh Dao  for technical assistance. Cell sorting and imaging were performed at the  Molecular Imaging Center, Dept. of Biomedicine, University of Bergen. We thank  James Lorens  for critical comments on the language of the manuscript. This work was supported by  Helse Bergen HF  project  303385 , the  Norwegian Research Council , Grant number  204921/F20  and project  102244 ,  Norwegian Cancer Society  Project no. 642251 , and A. B. was supported by a MarieCurie FP7 IntraEuropean Fellowship n. 255515.