dataesr/scientific-paragraphs-categorization huggingface dataset:
esrl. noaa. gov). 3) Vegetation: Potential vegetation map was acquired from the Synergetic Land Cover Product (SYNMAP, ftp://ftp. bgc-jena. mpg. de/pub/outgoing/mjung/SYNMAP/), which merged multiple satellite-global land cover maps into a desired classification approach  (Jung et al. , 2006). Each 0. 5° grid cell includes the area fractions for a maximum of 47 land cover types. Vegetation in SYNMAP is classified according to its life form, leaf type, and leaf longevity. Barren ground, permanent snow and ice are also included in this dataset. Based on this SYNMAP dataset, participating model groups could create vegetated land fraction and reorganize the vegetation types to generate the corresponding plant functional type and fractions for their models. Annual cropland area from 1860 to 2015 was acquired from the HYDE 3.
2 datasets (ftp://ftp. pbl. nl/hyde/), which reconstructed time-dependent land use by historical population and allocation algorithms with weighting maps  (Klein Goldewijk et al. , 2016). This dataset shows that global cropland area increased from 5. 9 million km 2 in the year of 1850 to 15. 2 million km 2 in the year of 2015. 4) Atmospheric N deposition onto land surface: The monthly atmospheric N deposition (NHx-N and NOy-N) during 1860 -2014 were from the IGAC/SPARC Chemistry-Climate Model Initiative (CCMI) N deposition fields. CCMI models explicitly considered N emissions from natural biogenic sources, lightning, anthropogenic and biofuel sources, and biomass burning  (Eyring et al. , 2013). The transport of N gases has also been simulated by the chemical transport module in CCMI models.
This data was recommended by the Coupled Model Intercomparison Project (CMIP) and used as the official products for CMIP6 models that lack interactive chemistry components (https://blogs. reading. ac. uk/ccmi/forcing-databases-in-supportof-cmip6/). 5) N fertilizer application: Spatially-explicit synthetic N fertilizer use data was specifically developed in this project. We reconstructed the annual synthetic/mineral N fertilizer dataset from 1960 to 2014 for the global cropland, matched with HYDE 3. 2 cropland distribution  (Lu and Tian, 2017 ; https://doi. pangaea. de/10. 1594/PANGAEA. 863323). Data on national-level crop-specific fertilizer use amount was collected from the International Fertilizer Industry Association (IFA) and FAO. This N fertilizer dataset shows that the global total N fertilizer amount increased from 11 Tg N yr -1 in 1960 to 112 Tg N yr -1 in 2013, and N fertilizer use rate per unit cropland area increased by about 8 times in this period.
N fertilizer application rate before 1960 was linearly reduced to the zero in the 1900s. 6) Manure N production and application: Gridded annual manure N production in the period of 1860-2014 was developed by integrating the Global Livestock Impact Mapping System (GLIMS), the country-level livestock population from FAO, and N excretion rates of different livestock categories according to IPCC 2006-Tier I and  (Zhang et al. , 2017,  https://doi. org/10. 1594/PANGAEA. 871980). This annual dataset shows that manure N production increased by more than 6 times from 21 Tg N yr -1 in 1860 to 131 Tg N yr -1 in 2014, and application rate of manure N to cropland is less than 20% of the total production. In this project, we only consider the manure N application in cropland area. Manure N production and application rates in 2015 was assumed to be same as that in 2014.
All the input datasets were delivered to the modeling groups in netCDF format. To fit with individual modeling requirements for input datasets, the modeling groups could either use a subset of these data sets or add some additional data sets. For example, the participating model DLEM used all these environmental factors as inputs, while the model O-CN did not use manure N as an input (See Table  3  for model input requirements in each model). Figure  2  illustrates the inter-annual variations of the major input datasets at the global level during different available time periods. Figure  3  shows the spatial patterns of atmospheric N deposition, N fertilizer use, and manure N production in  1860, 1900, 1950, and 2014.
Model result benchmarking and evaluation Except for bottom-up model simulations, the NMIP also plans to synthesize multiple sources of terrestrial soil N2O emission data to provide a benchmark for evaluating model estimates. Four types of data will be collected or developed to serve as a potential benchmark: 1) Site-level N cycling processes and N2O emission measurements through chamber or eddy-flux tower across biomes; 2) N2O flux measurement data from national-based or global-based measurement network (e. g. , Long Term Ecological Research Network, Long Term Agroecosystem Research Network, Greenhouse gas Reduction through Agriculture Carbon Enhancement network, http://www. n2o. net. au, etc. ) ; 3) Other spatialized datasets, including statistical extrapolation (e. g. ,  Xu et al. 2008; Kurokawa et al.
Result analysis and synthesis Based on model results, the NMIP team will provide multi-model ensemble estimates for terrestrial N2O fluxes at various scales from country-, sector-, continental, to global, and also assess differences and uncertainties among participating models. Through the seven simulation experiments, the magnitudes and spatiotemporal variations in terrestrial N2O emissions will be attributed to changes in different environmental factors at both regional and global scales. The global and regional N2O flux data derived from other sources including atmospheric inversion, statistical extrapolation, and inventory approaches (e. g. , the N2O emission data collected in Tian et al. 2016) will be compared and integrated with the NMIP modeled results. Through these syntheses and evaluations of modeled versus field observed N2O dynamics, we will further identify the gaps in our understanding to estimate N2O fluxes and put forward potential strategies to improve the models.
In the following sections, we provide an initial analysis of simulated terrestrial N2O emissions from the three models (DLEM, O-CN, and VISIT), which simulate both natural and agricultural emissions. As indicated by the model ensemble, the global N2O emission has significantly increased, especially since the 1960s with more rapidly rising exogenous N inputs to terrestrial ecosystems (Figure  5 ). Natural soils were the largest source across the entire period. Cropland is the single largest contributor to the increasing trend in N2O emissions during 1860-2015. Despite the same input datasets, the interannual variations among the three models were different due to the differences in model structure and parameters. The estimated N2O emissions from VISIT were consistently higher than those from the other two models during 1860-2015; N2O emissions from DLEM and O-CN were similar in magnitude.
Climate: CRU-NCEP climate version 7 is a fusion of the CRU and NCEP/NCAR reanalysis climate datasets between 1901 and 2015, which was reconstructed by the Laboratoire des Sciences du Climat et l'Environnement, Paris, France (https://vesg. ipsl. upmc. fr). Major climate variables include longwave and shortwave radiation, air pressure, humidity, temperature, precipitation, and wind speed at 6-hourly temporal resolution. Monthly magnitude of climate variables in CRU-NCEP dataset was forced to be consistent with the observational-based CRU datasets.
Acknowledgments H. Tian  and his team acknowledge the support by  National Key Research and Development Program of China  (#  2017YFA0604702 ),  CAS Grants  ( KFJ-STS-ZDTP-0 ;  SKLURE2017-1-6 ), NOAA Grants ( G00010410 ,  G00010318 ),  National Science Foundation  ( 1210360 ,  1243232 ) and  Auburn University IGP Program. A. Ito was supported by  Japan Society for the Promotion of Science  (Grant #  17H01867 ). C. Peng  acknowledges the support by  National Science and Engineering Research Council of Canada (NSERC)  discovery grant and China's  QianRen Program. E. Saikawa  was supported by  NOAA Climate Program Office 's  AC4 program , award #  NA13OAR4310059. S. Zaehle  was supported by the  European Research Council (ERC)  under the European Union's  H2020 Programme  (grant #  647304 ; QUINCY).
1 ) 1 Climate: CRU-NCEP climate version 7 is a fusion of the CRU and NCEP/NCAR reanalysis climate datasets between 1901 and 2015, which was reconstructed by the Laboratoire des Sciences du Climat et l'Environnement, Paris, France (https://vesg. ipsl. upmc. fr). Major climate variables include longwave and shortwave radiation, air pressure, humidity, temperature, precipitation, and wind speed at 6-hourly temporal resolution. Monthly magnitude of climate variables in CRU-NCEP dataset was forced to be consistent with the observational-based CRU datasets.
These priors were then fed into the de-blending tool XID+ to extract flux densities from the SPIRE maps. In the final step, multi-wavelength data were combined with the extracted SPIRE flux densities to constrain SEDs and provide stellar mass (M ) and SFRs. These M and SFRs were then used to populate the SFR-M plane over 0. 2 ≤ z < 6. 0. Results. No significant evidence of a high-mass turn-over was found; the best fit is thus a simple two-parameter power law of the form log(SFR) = α[log(M ) -10. 5] + β. The normalisation of the power law increases with redshift, rapidly at z 1. 8, from 0. 58 ± 0. 09 at z ≈ 0. 37 to 1. 31 ± 0. 08 at z ≈ 1. 8. The slope is also found to increase with redshift, perhaps with an excess around 1. 8 ≤ z < 2. 9. Conclusions. The increasing slope indicates that galaxies become more self-similar as redshift increases.
In this work, XID+ is used to de-blend the maps in the SPIRE bands, resulting in a catalogue of over 200 000 objects with SPIRE flux density measurements in the COSMOS field, compared to approximately 32 000 sources blindly detected at 250 µm  (Roseboom et al. 2010; Wang et al. 2014; Sparre et al. 2015). These data, along with UV to Herschel Photodetector Array Camera and Spectrometer (PACS;  Poglitsch et al. 2010 ) data from a multi-wavelength catalogue, are then used to examine the MS. The paper is structured as follows. Section 2 describes where the data were collected. Section 3 explains the methodology and tools used to de-blend the SPIRE bands and find the MS. Section 4 explores the results of the analysis. Section 5 provides discussion, while Section 6 provides a summary of the conclusions.
(2016). As we wanted to use the Multi-band Imaging Photometer for Spitzer 24 µm (MIPS24;  Rieke et al. 2004 ) data for the ≈40 000 objects with MIPS24 data to help constrain the FIR SED, we used objects that only fall within the MIPS24 image in  (2) The latest SPIRE images from the Herschel Database in Marseille 3 , the Data Release 4 maps from the Herschel Multi-tiered Extragalactic Survey  (Oliver et al. 2012) , were used in XID+ to extract the SPIRE flux densities. The 250 µm, 350 µm, and 500 µm band images have beam full widths at half maximum of  18. 1 , 25. 2 , and 36. 6 (Griffin et al. 2010 ) and 5σ confusion limits of 24. 0, 27. 5, and 30. 5 mJy, respectively  (Nguyen et al. 2010 ). 3 http://hedam. lam. fr.
CIGALE Code Investigating GALaxy Emission (CIGALE;  Noll et al. 2009 ) is a spectral energy distribution (SED) modelling and fitting tool with an improved fitting procedure by  Serra et al. (2011). Here, the Python version 0. 11. 0 is used  (Burgarella et al. 2005; Noll et al. 2009, Boquien et al. , in prep. )  to generate SEDs and to fit these SEDs to the UV to PACS data from COS-MOS2015 to estimate the SPIRE 250 µm, 350 µm, and 500 µm flux densities for use as a flux density prior in XID+ (see  Sects. 3. 1. 2 and 3. 2). After the SPIRE band flux densities had been extracted, CIGALE was also used to calculate the physical parameters of each object, such as SFR and M as well as rest frame colours. CIGALE uses the energy balance between the attenuated UV emission by dust and the IR emission, allowing the estimation of the FIR flux densities.
Science pipeline The extraction of the flux densities in the SPIRE bands, which follows a similar pipeline to the one used in  Pearson et al. (2017) , is briefly summarised in Fig. 2  and is repeated here for completeness. We begin by using the far-UV (FUV) to PACS 160 µm data from COSMOS2015 to generate estimates for the flux densities in all three SPIRE bands simultaneously using CIGALE's Bayesian analysis. All objects that were not classified as galaxies (TYPE flag in COSMOS2015 not set to 0, approximately 1. 3%) were removed, as were objects without any photometric redshift (ZPDF ≤ 0, approximately 2. 9%). All predicted flux densities were then used in XID+ to extract the flux densities for the objects; we did not remove any objects with poor χ 2 values. Once the SPIRE flux densities were extracted, these SPIRE data were added to the FUV-PACS data and CIGALE was rerun, this time to get estimates for M and SFR.
This leaves 205 958 objects to be run through XID+. To prevent an overly restrictive prior in XID+ and conservatively capture the uncertainty in SED modelling, the errors on the flux density estimates are expanded by a factor of two (see also Appendix B). The estimates of all three SPIRE bands from CIGALE are then used as the priors for XID+. The flux density estimates are used as the means in the XID+ priors while the expanded errors are used as the standard deviations. XID+ was then run on the SPIRE images. The next step is to run the data from COSMOS2015 and XID+ through CIGALE to generate the required parameters for each object: M , SFR, and U-V and V-J colours; the colours are needed for the QG/SFG separation.
Acknowledgements. We would like to thank the anonymous referee whose comments greatly improved this paper. We would like to thank the  Center for Information Technology of the University of Groningen  for their support and for providing access to the Peregrine high performance computing cluster. This research has made use of data from  HerMES  project (http://hermes. sussex. ac. uk/). HerMES is a  Herschel Key Programme utilising Guaranteed Time  from the  SPIRE  instrument team, ESAC scientists, and a mission scientist. The HerMES data were accessed through the Herschel Database in Marseille (HeDaM -http://hedam. lam. fr)operated by CeSAM and hosted by the  Laboratoire d'Astrophysique de Marseille. HerMES DR4 was made possible through support of the  Herschel Extragalactic Legacy Project ,  HELP  (http://herschel.
Other researchers have developed strategies based on fuzzy logic for congestion control,  5, 41, 47  traffic smoothing,  31  scheduling policies,  39, 45  routing protocols,  34, 50  network energy consumption  15  or data distribution in Internet of things  29. Generally, these studies justify the selection of fuzzy logic because it facilitates an overall grasp of the network complexity. These studies are primarily based on the fuzzy controller concept developed by Mamdani  32  and compensate for the imprecision and uncertainties of the communication system. In most studies on this subject, the fuzzy rules are empirically determined. The originality of the research presented in this paper in comparison to these previous works is its determination of the fuzzy rules of the controller based on automatic modeling rather a human expertise and its identification of network behavior using series of numerical simulations generated using the Riverbed Modeler software, which is a network simulation tool.
Methodology Because the overall objective of this paper is to control the network QoS using an online automatic configuration of different network parameters, we propose the methodology shown in Fig. 2. The first step consists of generating sample datasets that will be used to identify the service offered by the network without referring to human expertise. Thankfully, the datasets may be generated by experimentation or simulation. Simulation has been selected here because it enables the coverage of a larger panel of analysis. Section 3. 1 details the generation of switch crossing delay (the QoS indicator of interest here), sample datasets (d 1 , d 2 , d 3 ,. ) in relation to the WRR weights (the controllable parameters), and other network parameters (e. g. , the traffic load or throughput).
The sample datasets are then analyzed to define a model of the QoS offered by the network. The QoS identification is divided into two parts. As shown in section 3. 3, linguistic variables are first defined according to the application requirements in terms of acceptable and non-acceptable QoS. Section 3. 4 highlights how the linguistic rules and the datasets are then considered together to generate the fuzzy rule set. A rule associates a delay characteristic to a configuration level of the weights w 1 and w 2. Generally, the fuzzy identification is then used to build an observer. However, the last step consists of synthesizing a controller based on the fuzzy rule set. It leads to a network fuzzy controller that will select the values to apply locally on a given network component to assess the QoS service level that is expected on the device.
44 = 2. 5 ms). Under this second threshold, real-time traffic is served in the preferred way, but the background traffic (lowest class) may face weak network availability. When it has a value between the two thresholds (i. e. , 2. 5 and 10 ms), the QoS is better for the background applications but remains sufficient for the real-time traffic. This case study will be used to illustrate the methodology proposed in Fig. 2. The next section addresses the first step, i. e. , the generation of delay sample datasets relative to the w 1 and w 2 scheduler weights. The values will be obtained through simulations using the Riverbed Modeler tool. Be-cause control of the WRR weights will be achieved on each switch, the network architecture shown in Fig. 3  is simplified to one single switch and two traffic flows, as shown in Fig.
Due to the small set of data that can be obtained because of the excessive computing time, the fuzzy approach is chosen. The major issue with these methods is that they require a large amount of simulation data. Thus, we choose the Fuzzy Rule Classifier (FRC) method,  42  which is well adapted for modeling a system with a reduced dataset. The performance of this approach in terms of generalization has been compared to other classifiers (K-NN, NN, SVM, GA, and DTM)  4. The Fuzzy Rule Classifier (FRC) is based on a supervised learning mechanism that is composed of two steps: the training step to obtain the system model and the generalization step to classify the unknown data. To identify the rules of the Network relation between the scheduler weights and the delay, we use the training step of the FRC method.
Fuzzy rules set generation The second step of the study is to obtain a rule set from the datasets. Several methods can be used to automatically generate the linguistic rules, including Genetic algorithms, 6,1 Wang and Mendel's algorithm,  51  which is used in the network domain  14  and Ishibuchi's algorithm  25. The Fuzzy Rule Classifier (FRC) that we use here is based on Ishibushi's Algorithm. This method uses an inference mechanism based on Larsen, which is better than the Mamdani inference, especially when there are several premises  3. The final output is obtained by privileging the rule giving the highest response. Each rule is activated in parallel, and a disjunction operator is used to aggregate the partial conclusions and determine the final decision. The disjunction operator selected is the maximum.
Fuzzy controller in Riverbed Modeler The fuzzy controller is implemented in the simulation Riverbed Modeler tool for its evaluation. The simulation Riverbed Modeler tool is structured with three representation levels that are used to compute the network device behavior. In simple terms, the first level defines the process, the devices' input/output ports and their relationships. The second level uses the state chart model to describe the process. Finally, the third level presents associating behaviors written in C++ code describing the states and transitions of the state chart model. The fuzzy controller was tested for the Cisco 2924XL switch model contained in the Riverbed Modeler model library (Fig. 12 ). At level 1 of this model, a specific link (statistic wire) has been added, which enables the time required to cross the switch to be obtained.
The subventricular zone (SVZ) is a region of ongoing postnatal germinal activity that shows complex spatial heterogeneity. For instance, different SVZ microdomains contain neural stem cells that express distinct transcription factors and generate different glial and neuronal progenies. These unique characteristics call for the development of new methods to integrate a spatial dimension to histological analyses performed in this germinal region. We developed "FlashMap", a semi-automatic software that allows the segmentation and rapid measurement of optical densities throughout the full SVZ coordinates. "FlashMap" generates easily readable two-dimensional heatmaps that can be superimposed onto three-dimensional reconstructions of the ventricular system for optimal spatial exploration. Accurate heatmaps can be obtained, even following serial section subsampling thereby reducing the amount of tissue and time required for histological analysis.
Similarly, oligodendrocytes are produced postnatally by the most dorsal regions of the SVZ 10 (dSVZ). Similarly to rodents, dorsal restriction of glutamatergic progenitors and lateral enrichment of GABAergic progenitors was found in newborn marmosets  11. This suggests that a certain degree of heterogeneity is evolutionary conserved. Recent research, driven by the development of new technologies, led to an accumulation of a large amount of transcriptional datasets of various SVZ cell types isolated from distinct microdomains. For instance, we recently described an unexpected level of transcriptional heterogeneity between the dSVZ and lSVZ, as well as between NSCs and transient-amplifying progenitors of those two microdomains 12. While the list of regionally expressed.
genes continuously increases, an appropriate tool for rapid analysis of their expression pattern in the SVZ is still missing. We therefore aimed to develop a tool that allows rapid protein expression analysis along the full rostro-caudal and dorso-ventral extend of this germinal region. "FlashMap" is a software for semi-automatic marker expression analysis in the SVZ, based on optical density (OD) measurements. "FlashMap" allows subsampling of serial sections, in order to further reduce the time for analysis. Data are exported as heatmaps that can be superimposed onto volumetric reconstructions of the LV for optimal and intuitive visualization. "FlashMap" has the potential to be applied to a wide range of semi-automated analyses, and is therefore of substantial interest for the field.
64 µm/pixel; 2). Load the image (3). (B) Define the dimension of the probes (here 50 µm), the hemisphere (here "right" hemisphere) and the ROI (here "lateral" SVZ). (C) Define ROI by placing a "start point" (1, 2) and an "end point" (3, 4). (D) Draw the internal face of the ROI for automatic distribution of probes. (E) Define a reference area in the adjacent tissue (here striatum) by selecting two points in that region (1-3). (F) "FlashMap" automatically calculates the height of the probes according to the differential cell densities within the probe (P) and the selected reference region (R; 1, 2). Using JS div (3) the software estimates the optimal basal height of the probe (N opt ; height of the probe in pixels, where the value of the JS div consistently drops) between the apical and the basal borders N 0 and N max , respectively (4, 5).