dataesr/scientific-paragraphs-categorization huggingface dataset:
Color is coded in [0, 255], q = 0. 5, and the experimental protocol is developed in section 5. 2. Despite the diversity of the images used, 10 to 30% of the components of vector h fall in the unsaturated range [h min , h max ]. The proportion in this range is heterogeneous in some dataset (COIL or Flowers) but the differences are not really more important between datasets. VIDEO has a high and more homogeneous rate that is close to a few CBS views. MNIST is very particular with its gray-level and almost binary views, resulting in a very high near-null activation rate.
Network properties and applications This section presents and illustrates some IRF-NN properties. A few of these properties and perspectives of application have been presented in recent conference papers  [5, 6, 7]. We also carried out an in-depth study to determine the parameters of the network, its encoding performances on large image datasets, and its generalization capabilities. This study, that is the subject of a companion article  [35] , has been conducted on public image databases COIL and ALOI  [36]  for 3-D objects in rotation and compared to other classification techniques. This  section summarizes the main results and develops new experiments.
Image datasets The datasets used for the experiments, all freely available, are presented briefly. The diversity of the tested images is illustrated in figure  5  with a couple of views extracted from each set. The widely used COIL 1  [37]  and the more recent ALOI 2  [36]  datasets consist of color views of small 3-D objects in rotation on a turntable, taken at a five degree interval. COIL and ALOI consist respectively of 100 and 1,000 objects, amounting to 7,200 and 72,000 image views. The Change Blindness Scenes (CBS) 3 dataset has been devised to conduct psychological experiments to compare how color, object position, and object presence are encoded in visual memory  [38]. CBS has been used to measure the time it takes for observers to detect small scene changes. This set 1 http://www.
cs. columbia. edu/CAVE/software/softlib/coil-100. php 2 http://staff. science. uva. nl/ aloi/ 3 http://wiki. cnbc. cmu. edu/Objects contains 66 color pictures of real scenes, each of which is supplemented with 2-9 variants (300 views in total) where a small element in the scene has been altered in color, position, and presence/absence. The changed versions would look as natural as the original versions (Figure  6 ). MNIST  4  is a well-known database of handwritten digits composed of a training set of 60,000 examples and a test set of 10,000 examples. The digits have been size-normalized and centered in a fixed-size image  [19]. Flowers collection 5  contains more than 8,000 photos of flowers belonging to 102 different categories  [39]. Experiments have also been conducted on video sequences from various sources, including commercials, a movie, and personal videos.
Internal representation of images Since the input layer is constant, without adaptation to image sets, an important verification is the capacity of the network to represent any image. Internal vector h is an image encoding which must be significant for any image, and discriminant and sensitive to small changes. The training and response steps only use this internal data. Numerous experiments have confirmed that the internal representation is rich, sensitive and discriminant. Properties of this image mapping are easier to point out with supervised training tests, but a direct examination gives an interesting insight. This analysis doesn't require labeled datasets. After the initialization of an IRF-NN, any picture I can be presented. Vector h(I) is computed by (  6 ) for gray-level images or (8) for color images.
Lets consider an initialized IRF-NN of 2,000 neurons and input some views. The bar graph in figure  3   To what extent is vector h sensitive to image details? Let ∆ h = |h kh k+1 | be the difference between the representation of two images k and k + 1. Figure  7  presents two histograms of ∆ h for images taken from the CBS dataset. One is for two almost identical views (original and minor color variant of scene m21055 in Figure  6 ), and the other for views of very different scenes (m21055 and h17146). As expected, the differences are small but significant for similar views, and large for different scenes. In similar views, about 90% of the component values of vector h are identical. The amplitudes of the other values are widely distributed and reflect the range of sensitivity of the random IRFs, as shown in Figure  8.
A quick and easy test is to measure the recognition rate after training and verify that τ learning = 100%, repeatable for any network initialization. A first test associates a different class to each view to verify that the network is able to distinguish them. A preliminary trial on the 366 CBS views has shown that 44 images cannot be differentiated. It allowed us to detect duplicates and renamed image files in the dataset. Therefore only 322 views are retained for this test and are all correctly recognized after training. Table  1  shows exhaustive tests on all datasets. In this paper most experiments are realized with 2,000 neuron networks. When the number of views increases, more neurons are necessary to ensure total discrimination. Discussions on the number of neurons will be detailed in next papers.
Before discussing exhaustive result, consider again the previous CBS example and observe now response vector ŝ. Each component is interpreted as the probability that the tested image belongs to the corresponding class. Figure  9  plots ŝ values for 5 variants of scene m21055. The first network (left) has been trained with one view per scene. Its response for every variant gives a very high probability (near 1) for label m21055 and near 0 for any other class. As expected, the figure confirms a suitable generalization. This example is confirmed for the complete dataset: every view is correctly associated with the original scene, and the classification margins are near 0. 9. When the network is trained with 2 independently labeled views of the scene (C2 and P1), score ŝ shows a perfect recognition of the learned images, and can approach 0.
5 for intermediate views (figure  9  right). This result is an additional confirmation of the IRF-NN sensitivity and generalization properties. Numerous experiments have been systematically realized on the datasets and give excellent results for recognition of objects using different views. In the reported tests, the COIL and ALOI datasets are split in two sets, a learning set composed of 18 photos per object with a 20 degree rotation (25% views of the dataset), and a testing set composed by all remaining views, and a testing set composed by all remaining views. Table  2  summarizes the statistical results with recognition rate on the testing set. It shows very good performance for these tasks. The network can work with a very large number of classes (or objects), and a large learning set, as will be illustrated in more detail with ALOI in a forthcoming paper  [35].
The results are near the state of the art, and largely better for ALOI, which is remarkable for such a simple architecture. The computational cost is very competitive. The total duration of the COIL test  7  is about 1 mn and can be detailed: 12s for  the loading of the images, 1. 8 s for the initialization of the 1,800 neurons; 15 s for the training of the 1,800 images; 34 s for the test of 5,400 images. With 9,000 neurons for the ALOI dataset, the total duration is 25mn. As a comparison, the same test with SalBayes take several hours and a SIFT approach hundreds of hours  [40].
is an indicator of the neurons' activation ranges for samples of 15 images selected in various datasets (CBS, COIL, FLOWERS, VIDEO, MNIST). For this activation analysis, absolute values are arbitrarily classified in 3 ranges: near-null [0, h min ], unsaturated [h min ,h max [, and quasi-saturated [h max , 1], where thresholds used are h min =. 1, h max =. 98. The graph verifies that for various kinds of pictures, the representation consists of a sufficient number of neurons in the unsaturated range. Best application results are generally obtained for a proportion from 5 to 20%. The exhaustive test verifies that each image has several components in the unsaturated range (see section 4. 2).
At the same time, the biospheric fluxes are strongly influenced by climate variability  (Keeling et al. , 1995). Therefore, the timely availability of accurate meteorological datasets is also crucial. The recent development of the CTESSEL Carbon module within the IFS takes advantage of accurate real-time climate forcing in order to provide online terrestrial biogenic fluxes also in real time. The online computation of terrestrial biogenic fluxes and transport -both forced and initialized by NWP analyses -is key to ensure consistency in the coupling between fluxes and transport. An example of the importance of this consistency is the passage of mid-latitude frontal cyclones. The change in radiation associated with the frontal cloud reduces the photosynthetic CO 2 uptake which results in a substantial increase in atmospheric CO 2 (∼ 10 ppm) near the surface, as respiration continues to emit CO 2  (Chan et al.
CO 2 seasonal cycle The phase and amplitude of the seasonal cycle of CO 2 are very dependent on latitude. Thus, the model is first evaluated using the NOAA GLOBALVIEW-CO2 (2011) dataset which displays the integrated effects of surface CO 2 fluxes over large regions at different latitudinal bands (Fig. 3 ). At first glance, the annual cycle phase and amplitude and latitude dependency appears to be reasonably represented in the hindcast. However, there are clear discrepancies between the hindcast and GLOBALVIEW-CO2 (2011) in the NH. First of all, the hindcast does not release enough CO 2 before and after the growing season (i. e. March to May and October to December). Secondly, the onset of the CO 2 sink associated with the growing season starts too early in the hindcast. The sharp CO 2 decrease in mid-latitudes depicted by GLOBALVIEW-CO2 (  2011 ) in June starts in May in the hindcast.
ACPD  ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD Acknowledgements. This study was funded by the  European Commission  under the  EU Seventh Research Framework Programme  (grant agreement No. 283576 , MACC II). The ICOS data were obtained from the  ICOS Atmospheric Thematic Center (Laboratoire des Sciences du Climat et l'Environnement ) website at https://icos-atc-demo. lsce. ipsl. fr. The authors acknowledge the  European Commission  for the support of the preparatory phase of ICOS (2008-2013), the  Netherlands Ministry of IenM  and  ECN  for the support of the observations at Cabauw. We are grateful to  Jérôme Tarniewicz  for his technical support with the ICOS database and to  Miha Razinger  for his help in the development and maintanence of the ICOS monitoring plots in the MACC website.
Thanks to the  NOAA/ESRL Global Monitoring Division  for providing their data from the baseline observatories at  Barrow (Alaska) ,  American Samoa ,  South Pole (Antarctica) , the tall towers at Argyle (Maine),  Park Falls (Wisconsin) ,  West Branch (Iowa) , and the vertical profiles from the  NOAA GMD Carbon Cycle Vertical Profile Network. TCCON data were obtained from the  TCCON Data Archive , operated by the  California Institute of Technology  from the website at tccon. ipac. caltech. edu. We acknowledge financial support of the Bialystok TC-CON site from the  Senate of Bremen  and  EU projects IMECC, GEOmon and InGOS , as well as maintenance and logistical work provided by the  AeroMeteo Service  and additional operational funding from the  National Institute for Environmental Studies (NIES, Japan ).
POW and DW thank  NASA's Carbon Cycle Science program  ( NNX10AT83G  and  NNX11AG01G ) and the  Orbiting Carbon Observatory Program  for support of TCCON and this research. The HIPPO Merged 10 s dataset was obtained from the website at hippo. ornl. gov/dataaccess. The  HIPPO ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD.
[33, 34] ), where the full top-mass dependence was retained. The results cited above show that the large top-mass limit is a very good approximation as long as the transverse momentum of the jets is not sensibly larger than the top mass and justify the use of EFT approach for the Higgs-gluons interactions. In the mt → ∞ limit, the resulting analytic expressions at NLO for GF Hjj production have been implemented in MCFM  [35] , which has been used by Powheg Box  [36]  and Sherpa  [37]  to obtain NLO results matched with parton shower (NLO+PS). Independent NLO+PS predictions in the Sherpa package using GoSam  [38]  for the one-loop matrix elements and in Mad-Graph5 aMC@NLO  [39] , which embodies MadFKS  [40]  and MadLoop  [41] , are also available. We note that all the above predictions are for the SM Higgs boson, i.
Gluon fusion production with jets In MadGraph5 aMC@NLO the code and the events for X0 plus two jets in the GF channel can be automatically generated by issuing the following commands (note the / t syntax to forbid diagrams containing top loops): > import model HC_NLO_X0-heft > generate p p > x0 j j / t [QCD] > output > launch where the -heft suffix in the model name refers to the corresponding model restriction. As a result, all the amplitudes featuring the Higgs-gluon effective vertices in the heavy-top limit are generated, including corrections up to NLO in QCD. Analogous commands can be issued to generate events for X0 plus zero and one jet at NLO. The NLO computation for Hjjj in GF has been recently achieved interfacing Sherpa with GoSam  [84]. We note that MadGraph5 aMC@NLO provides the FxFx merging  [63]  to combine several NLO+PS samples, which differ by final-state multiplicities, and NLO merged Higgs production in GF was discussed in refs.
Acknowledgments We would like to thank the  Higgs Cross Section Working Group  for the encouragement in pursuing the Higgs Characterisation project. We are thankful to  Pierre Artoisenet ,  Stefano Carrazza ,  Stefano Forte ,  Rikkert Frederix ,  Valentin Hirschi ,  Olivier Mattelaer  and  Tiziano Peraro  for their support during the preparation of this work and to  Stefano Frixione  for many useful discussions and comments on the manuscript. We thank  Roberto Pittau  for his participation to the initial stages of this project. This work has been performed in the framework of the  ERC  grant  291377  "LHCtheory: Theoretical predictions and analyses of LHC physics: advancing the precision frontier" and of the  FP7 Marie Curie Initial Training Network MCnetITN  ( PITN-GA-2012-315877 ). It is also supported in part by the  Belgian Federal Science Policy Office  through the  Interuniversity Attraction Pole   P7/37.
The work of FD and FM is supported by the IISN "MadGraph" convention 4. 4511. 10and the IISN "Fundamental interactions" convention 4. 4517. 08. KM is supported in part by the  Strategic Research Program "High Energy Physics " and the  Research Council of the Vrije Universiteit Brussel. The work of MZ is partially supported by the  Research Executive Agency (REA)  of the  European Union  under the Grant Agreement Number  PITN-GA-2010-264564  (LHCPhenoNet) and by the  ILP LABEX  ( ANR-10-LABX-63 ), in turn supported by  French  state funds managed by the  ANR  within the  "Investissements d'Avenir" programme  under reference  ANR-11-IDEX-0004-02.
In sensitive areas of the developing world, deposition levels equaled or exceeded those that had caused damage in the industrialized world. Finally, the assessment concluded that the global database was inadequate to evaluate the extent, severity, and impact of acidic deposition in all parts of the world. This second assessment presents a global overview of worldwide deposition by using quality assured measurements to complement and validate the best available global models. The assessment benefits from a more extensive yet still severely incomplete global database. To the extent possible, data quality was assessed and screened against guidelines established by WMO/ GAW (2004), a key contribution toward harmonizing global wet deposition network measurements. This assessment focuses on regionally-representative chemical concentrations and deposition measurements; thus, urban data or data otherwise representative of very limited areas are not included.
Major ions are shown in different colors, making it clear where anthropogenic or sea salt influences are important e as well as making obvious the areas of the world where there are no measurements. It is believed that the uncharacterized "other" anions shown in white are associated with weak organic acids (primarily formic and acetic acid) that are typically not measured and are unstable in unpreserved samples. Only sites in west central Africa provided organic acid and bicarbonate measurement data; these values are shown on this map. This assessment has relied upon national and regional monitoring methods and networks. It has culminated in the development of a dataset of high quality precipitation chemistry measurements as determined by regional experts. The dataset is publicly available at the GAW World Data Centre for Precipitation Chemistry (http://wdcpc.
As a reference for later discussion of precipitation depth and its influence on wet deposition, global precipitation maps of precipitation depth are included as Fig. S2a ,b of the Addendum. It was beyond the scope of this study to attempt to assess the precipitation measurements by precipitation type (e. g. , snow or rain), or to establish whether the precipitation at a location was from convective rainstorms, monsoons, tropical storms, or long periods of low intensity precipitation. All precipitation chemistry and wet deposition annual data in this assessment were carefully screened. Screening was initially carried out by each network/program and was followed by a secondary screening by Environment Canada's National Atmospheric Chemistry Database and Analysis facility in Toronto, Canada. This facility then carried out most of the data analyses, archiving and mapping of the final data.
The outcome is a final database of quality assured, regionally-representative data. Data screening included the assessment of site representativeness, sampling protocols, laboratory methods, data completeness, and the assignment of a quality rating. Details of the screening and quality rating procedures are shown in Table  1  and are described briefly in the text that follows. Measurement sites were screened to ensure that only regionally-representative sites were used; sampling protocols and laboratory methods were screened to ensure that the protocols and methods were consistent with the standard protocols defined by the World Meteorological Organization in WMO/GAW (2004) (including good performance in external laboratory intercomparison studies where possible); data were screened for completeness through a quantitative comparison of the annual data against the data completeness criteria identified in Table  1 ; data quality was assessed and every 3-year average measurement value accepted in the final data set was assigned a Data Quality Rating.
Values were calculated as the sum of the ensemble-mean grid square emissions and total deposition estimates within individual continents, oceans and coastal zones as defined by Fig. 2. 2. Note that total Coastal emissions (and deposition) are equivalent for the Continents and Oceans. Thus, Global Total emissions and deposition equal the sum of the Non-coastal Continents, Non-coastal Oceans and one of the Coastal Continents or Oceans. Dentener et al. (2006)  in which the regional anthropogenic emissions were generated by IIASA and spatially distributed using the EDGAR 3. 2 database. It must be acknowledged, however, that the ensemble variability of global emissions in our case was relatively high, with a standard deviation of AE7. 3 Tg S. that wet deposition changes reflect both concentration and depth changes.
Since there are no direct dry deposition measurement data against which to compare the inferential dry deposition estimates, it is not possible to say which of the two inferential models is more accurate. Also unknown is whether the model-measurement differences are due to differences in ambient concentration measurements or estimated dry deposition velocities, and whether the HTAP model results are more accurate than the inferential results. In spite of the foregoing uncertainties, the HTAP and inferential results have been merged in Fig. 3. 12a to give the best-available estimates. The poor model-measurement comparability in Fig. 3. 12b (i. e. , modeled values > measurement-based inferential estimates) is due to a number of factors including uncertainties in the measurement-based inferential estimates and the dry deposition parameterizations of the HTAP and inferential models, plus the fact that modeled values are derived from grid square average emissions and concentration values while the measurement data apply to a single point typically located > 50 km from large emission sources.
3 mg S L À1 in 1997e1998. These values are higher than the remote background concentrations of 0. 04  (1985)  to 0. 08 (1990e92) mg S L À1 reported in east Venezuela by  Morales et al. (2001a)  and of <0. 1 mg S L À1 mg S L À1 in Amazonia, Brazil reported by  Forti et al. (2000) ,  Lesack and Melack (1991)  and  Pauliquevis et al. (2012). For this assessment, data were included from the one regionally-representative monitoring site in South America (Brazil) that met our time period restrictions and quality acceptance criteria. The precipitation-weighted mean concentration and wet deposition values, as provided by C. Forti (personal communication, April 29, 2010) , appear in Fig. 3. 6a and b and were measured at a forest site in eastern Brazil (Cunha) for the period 1999 to 2001. The precipitation-weighted mean concentrations of S and nssS at Cunha were 0.
162 mg S L À1 and 0. 133 mg nssS L À1 , respectively, where the concentration is very close to the modeled grid cell value in that area (see Fig. 3. 6b) and consistent with the modeled deposition hot spot in southeast Brazil. The sparse amount of data on the continent makes it necessary to use the HTAP model results to understand the concentration pattern across South America. To that end, the pattern (see Fig. 3. 6a) closely resembles the pattern of S emissions (Fig. 3. 1), with low concentrations in the range of <0. 04 mg S L À1 in the remote areas of Amazonia and southern Argentina and high concentrations in the range of 0. 1e1. 0 mg S L À1 in Colombia, northern Chile, southeastern Brazil and northern Argentina (with an extension into the eastern Pacific Ocean). The lowest concentrations on the continent are in the same range as concentrations in other remote areas of the world.
The ICP Forest monitoring network is very dense, especially in Central Europe, and some outlying sites show relatively large differences, suggesting that not all sites are regionally representative. Nevertheless, the spatial pattern of S total deposition is similar in shape to the HTAP model results. The highest total deposition levels (>20. 0 kg S ha À1 a À1 ) appear in East Central Europe, decreasing toward Southeastern Europe and toward the northwest, around the English Channel (Fig. 3. 18a). Throughout much of Central Europe, total deposition ranges between 10. 0 and 20. 0 kg S ha À1 a À1 and in northern and Southwestern Europe, S deposition is < 10. 0 kg S ha À1 a À1. The lowest deposition levels are seen in Scandinavia at about 1 kg S ha À1 a À1. Relatively high deposition levels are seen in Bulgaria and Romania in 2000e2002, as shown by the HTAP model results, and although there were no measurements in this region for that time period, the 2005e2007 data confirm these enhanced levels.