dataesr/scientific-paragraphs-categorization huggingface dataset:
INTRODUCTION The Air Quality Model Evaluation International Initiative (AQMEII,  Rao et al. , 2010)  has been active since 2008 with the aim of promoting the research on regional air quality model evaluation across the modelling communities of Europe and North America. It is coordinated by the European Joint Research Centre (JRC) and the U. S. Environmental Protection Agency (EPA) and it has now reached its third phase, referred to as AQMEII3 hereafter. The experience gathered in the first two phases consisted of important advancement in the model evaluation research as well as establishing a large community of participating regional modeling groups, and have made AQMEII a natural candidate to collaborate with the Hemispheric Transport of Air Pollution (HTAP) initiative. HTAP, a taskforce of the Long Range Transport of Air Pollution program (LTRAP) acting within the UNECE program, relies on a community of global scale chemical transport models to investigate the fate of air pollutants emitted in the Northern hemisphere and determine the contribution of remote sources as well as their impacts to the background concentration in different parts of the globe.
HTAP is in its second phase and the activities undertaken during this second phase include coordinating simulations by both global and regional scale models. The regions of interest in the Northern hemisphere are North America, Europe and South East Asia. The regional-scale modelling component of this activity for Europe and North America is being coordinated by AQMEII while the Asian component is being coordinated by MICs-ASIA (Model Intercomparison Study-Asia). Global models participating in HTAP are used by the AQMEII regional models as boundary conditions and special attention has been given to the emission inventory to ensure that it is consistent between the global and regional-scale simulations as described in  Janssens-Maenhout et al. (2015). The activity described here relates to the evaluation of the base case scenario set up within the context of HTAP and AQMEII (a description of the HTAP program can be found at www.
MODELS AND OBSERVATIONAL DATA Table  1  summarises the modelling systems participating in AQMEII3. Twelve modelling groups produced outputs over EU and four over NA (although not all fields were made available by all groups). Sensitivity simulations performed by two groups, in which alternate emission inventories were used, raises the number of EU contributions to fourteen. Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2016 -682, 2016  Manuscript under review for journal Atmos. Chem. Phys. Published: 7 September 2016 c Author(s) 2016. CC-BY 3. 0 License. The 'standard' emission inventories are those developed for the second phase of AQMEII for EU and NA and extensively described in  Pouliot et al. (2015). For EU, the 2009 inventory of anthropogenic emissions was used, although biogenic emissions (meteorology-dependent) were specifically calculated for the year of 2010 by several groups.
In regions not covered by the standard inventory, such as North Africa, five modelling systems (Table  1 ) have complemented the standard inventory with the HTAPv2. 2 (Janssens-Maenhout et al. , 2015)  datasets. The two inventories are the same over EU and in the MACC inventory the non-European emissions are not included. Other small differences might exist among the two inventories (like in the shipping emissions), but we consider them to be of small impact for the spatial averaged analysis carried out in this study. Emissions from lightning and volcanic sources are not contained in the EU and NA emissions inventories, since not all participating models include robust methods for estimating these emissions. Two EU modelling systems (CHIMERE, SILAM) made results available with both inventories.
For the NA domain, the 2008 National Emission Inventory was used as the basis for the 2010 emissions, providing the inputs and datasets for processing with the SMOKE emissions processing system  (Mason et al. , 2012). Year specific updates for the year of 2010 were made for several sectors, including mobile sources, power plants, wildfires, and biogenic emissions. Additional details can be found in  Im at al. (2015a,b)  and  Pouliot et al. (2015). Chemical boundary conditions were provided by the Composition -Integrated Forecast System (C-IFS) model  (Flemming et al. , 2015) , including ozone, NO x , CO, CH 4 , SO 2 , NMVOCs, dust, organic matter, black carbon and sulphate. Sea salt at the boundaries, although provided, was not used due to unrealistically high values. [Table  1 here]  2. 3.
The modelling domain covers Europe and a portion of Northern Africa. Simulations of WRF-CAMx over EU have been performed by RSE (Italy) using CAMx version 6. 10 (Environ, 2014) with Carbon Bond 2005 (CB05) gas phase chemistry  (Yarwood et al. , 2005)  and the Coarse-Fine (CF) aerosol module. Input meteorological data were generated by WRF-Chem model version 3. 4. 1  (Skamarock et al. , 2008a,b) , driven by ECMWF analysis fields. Grid nudging of wind speed, temperature and water vapour mixing ratio has been employed within the PBL, with a nudging coefficient of 0. 0003 sec-1. WRF-Chem has been adopted to predict GOCART dust emissions  (Ginoux et al. , 2001)  along with the meteorology. The WRFCAMx pre-processor (version 4. 2; ENVIRON, 2014) was used to create CAMx ready input files collapsing the 33 vertical layers used by WRF to 14 layers in CAMx but keeping identical the layers up to 230 m above ground level.
Anthropogenic emissions were derived by the TNO-MACC data applying a NO 2 /NO X ratio of 5% for each emission category. Biogenic VOC emissions were computed by applying the MEGAN emission model v2. 04. Sea salt emissions were computed using published algorithms  (de Leeuw et al. , 2000; Gong, 2003). Aarhus University (Denmark) applied the WRF-DEHM modelling system over EU and NA. The DEHM model used anthropogenic emissions from the EDGAR-HTAP database and biogenic emissions are calculated using the MEGAN model. The gas-phase chemistry module includes 58 chemical species, 9 primary particles and 122 chemical reactions  (Brandt et al. , 2012). Secondary organic aerosols (SOA) are calculated following the twoproduct approach assuming that hydrocarbons undergo oxidation through O 3 , OH and NO 3 and for only two semi-volatile gas products  (Zare et al.
Meteorological initial and lateral boundary conditions were derived from the ECMWF analysis. In order to constrain the meteorological model towards the analyses a grid nudging technique was employed every 6 hours of WRF simulation. The results from WRF simulations were pre-processed for CMAQ using Meteorology-Chemistry Interface Process (MCIP) version 3. 6  (Otte et al. , 2005). In CMAQ model, the gas phase chemical mechanism was based on carbon bond chemical mechanism version 5  (Foley et al. , 2010)  with updated toluene and chlorine chemistry (CB05-TUCL) and the aerosol chemical reaction were treated with AERO6 module. The CMAQ model consisted of 35 vertical layers and extending up to ~16 km height with the thickness of lowest layer is approximately 20 m. The EDGAR HTAP V2 emissions (0.
, 2004) , and ACM2 PBL scheme. The anthropogenic emissions for most part of the model domain are from MACC and the missing information have been filled with the emissions provided by EDGAR/HTAP. The biogenic emissions were estimated using the Biogenic Emission Inventory System version 3 (BEIS3) model in SMOKE v2. 6 (https://www. cmascenter. org/smoke). The dust  (Tong, et al, 2011)  and sea-salt  (Gantt et al. , 2015)  emissions are generated using CMAQ inline modules. The ratio for NO 2 /NO x emissions is 10%  (Bieser et al. , 2011a). The INERIS and CIEMAT institutes jointly applied the ECMWF-Chimere system. CHIMERE (version CHIMERE 2013) has been run for a 0. 25x0. 25 horizontal resolution and 9 vertical levels, extending up to 500 hPa with a first (lower)-layer depth of 20 m, using the meteorology provided by ECMWF IFS (Integrated Forecast System).
Biogenic VOC emissions from vegetation and soil NO emissions have been calculated with the MEGAN model (version 2. 04;  Guenther et al. , 2006 Guenther et al. , , 2012)). Sea salt emissions inside the domain have been calculated according to  Monahan (1986). No sea salt condition was considered at the boundaries. The wind-blown dust is only included from the lateral boundary conditions. CHIMERE uses the MELCHIOR2 chemical mechanism  (Lattuati, 1997)  and ammonium nitrate equilibrium was calculated with ISORROPIA  (Nenes et al. , 1999). Dry  Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2016 -682, 2016  Manuscript under review for journal Atmos. Chem. Phys. Published: 7 September 2016 c Author(s) 2016. CC-BY 3. 0 License. deposition is based on the resistance approach  (Emberson 2000a,b)  and both in-cloud and sub-cloud scavenging have been considered for wet deposition.
HZG has used the COSMO-CLM meteorological model to drive the CMAQ model. For AQMEII3 the CMAQ version 5. 0. 1 was used, with the CB05-TUCL scheme and the multi-pollutant aerosol module AERO6. CMAQ is run on a 24x24km² horizontal grid, using 30 vertical layers up to 50hPa (lowest layer of approximately 40m). CMAQ was run using the optional in-line calculation of dry deposition velocities. Wet deposition processes include in-cloud and sub-cloud scavenging processes. All atmospheric parameters were taken from regional atmospheric simulations with the COSMO-CLM (CCLM) mesoscale meteorological model (version 4. 8) for the year 2010  (Geyer, 2014)  using NCEP forcing data employing a spectral nudging method for large-scale effects  (Kalnay et al. , 1996). CCLM is the climate version of the regional scale meteorological community model COSMO  (Rockel et al.
, 2008; Steppeler et al. , 2003; Schaettler et al. 2008). CCLM uses the TERRA-ML land surface model  (Schrodin and Heise, 2001 ), a TKE closure scheme for the PBL  (Doms et al. , 2011) , cloud microphysics after  Seifert and Beheng (2001) , the Tiedtke scheme  (Tiedtke, 1989)  for cumulus clouds and a long wave radiation scheme following  Ritter and Geleyn (1992). The meteorological fields were afterwards processed to match the 24x24km² CMAQ grid using the LM-MCIP pre-processor. The emission input for CCLM-CMAQ is based on the EDGAR HTAPv2 database, interpolated to the CMAQ model grid and aggregated following the SNAP emission sector nomenclature. Sector specific hourly temporal profiles and speciation factors of PM and VOC species were applied by the SMOKE for Europe emissions model  (Bieser et al.
OBSERVATIONAL DATA USED The observational data used in this study is the same as the dataset used in second phase of AQMEII  (Im et al. , 2015a,b)  and was derived from the surface air quality monitoring networks operating in EU and NA. In EU, surface data were provided by the European Monitoring and Evaluation Programme (EMEP; http://www. emep. int/) and the European Air Quality Database (AirBase; http://acm. eionet. europa. eu/ databases/airbase/). In NA observational data were obtained from the NAtChem (Canadian National Atmospheric Chemistry) Database and from the Analysis Facility operated by Environment Canada (http://www. ec. gc. ca/natchem/). For the purposes of comparing the models against observations, only Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2016 -682, 2016  Manuscript under review for journal Atmos.
Chem. Phys. Published: 7 September 2016 c Author(s) 2016. CC-BY 3. 0 License. stations with data completeness greater than 75% for the whole year and elevation above ground below 1000 m have been included in the analysis. Stations with continuous missing records for periods longer than 15 days have been removed from the dataset. In addition, we also make use of vertical profiles of ozone, temperature and wind speed measured by ozonesondes. Ozonesonde data have been extracted from the World Meteorological Organization (WMO) World Ozone, and Ultraviolet Radiation Data Centre (Toronto, Canada) and made available to the AQMEII community. These measurements report vertical profiles of ozone at several vertical levels. Further details on these data are given in  Solazzo et al. (2013). Time-averaged statistics have been calculated after the spatial aggregation of the modelled and observed time series and prior to the spectral decomposition (the original time series have been spatially averaged first and then this spatial average time series has been spectrally decomposed).
The reason is that while the contribution of the cross components to the overall error can be quantified, the associated time series needed to carry out the apportionment analysis cannot. The neglected part of the error is quantified in Table  S1. In some instances, such portion can be as high as 20% of the total error for ozone. Tables summarising the operational statistics (MB: Mean Bias; r: Pearson Correlation coefficient; RMSE: Root Mean Square Error) are reported in the Supplementary material and have been calculated using the 'openair' package  (Carslaw and Ropkins, 2012).
The most significant underestimation (10 ppb) is observed at the two stations closer to the west boundary for EU (stations 318 and 043). The boundary layer deficit of ozone is a long standing issue, as similar conclusions were derived for the first  (Solazzo et al. , 2013)  and second  (Im et al. , 2015; Giordano et al. , 2015)  phase of AQMEII,  Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2016 -682, 2016  Manuscript under review for journal Atmos. Chem. Phys. Published: 7 September 2016 c Author(s) 2016. CC-BY 3. 0 License. as well as in other studies  (Katragkou et al. , 2015) , emphasizing the strong dependence of regional models on the lateral boundary, whose effects propagate far into the interior of the domain. Towards the interior of the EU domain  (stations 134, 157, 242)  the profiles are in closer agreement with the observations, with the WRF-CMAQ1 model performing the best throughout the troposphere, possibly due to the overestimation of the entrainment of upper tropospheric ozone, as revealed by the strong gradient of WRF-CMAQ1 at 6000m (Figure  19 ).
). Ambient North American concentration measurements were extracted from Environment Canada's National Atmospheric Chemistry Database (NAtChem) PM database and provided by several U. S. and Canadian agencies (AQS, CAPMoN, CASTNet, IMPROVE, NAPS, SEARCH and STN networks); North American precipitation-chemistry measurements were extracted from NAtChem's precipitationchemistry data base and were provided by several U. S. and Canadian agencies (CAPMoN, NADP, NBPMN, NSPSN, and REPQ networks); the WMO World Ozone and Ultraviolet Data Centre (WOUDC) and its datacontributing agencies provided North American and European ozonesonde profiles; NASA's AErosol RObotic NETwork (AeroNet) and its data-contributing agencies provided North American and European AOD measurements; the MOZAIC Data Centre and its contributing airlines provided North American and European aircraft takeoff and landing vertical profiles; for European air quality data the folowing data centers were used:.
ACKNOWLEDGEMENTS We gratefully acknowledge the contribution of various groups to the third air  Quality Model Evaluation international Initiative  (AQMEII) activity. The following agencies have prepared the data sets used in this study:  U. S. EPA  (North American emissions processing and gridded meteorology);  U. S. EPA, Environment Canada, Mexican Secretariat of the Environment and Natural Resources  ( Secretaría de Medio Ambiente y Recursos Naturales-SEMARNAT ) and  National Institute of Ecology  ( Instituto Nacional de Ecología-INE ) (North American national emissions inventories); TNO (European emissions processing);  Laboratoire des Sciences du Climat et de l'Environnement, IPSL ,  CEA/CNRS/UVSQ  (gridded meteorology for Europe);  ECMWF/MACC Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2016-682,2016 Manuscript under review for journal Atmos.
). Ambient North American concentration measurements were extracted from Environment Canada's National Atmospheric Chemistry Database (NAtChem) PM database and provided by several U. S. and Canadian agencies (AQS, CAPMoN, CASTNet, IMPROVE, NAPS, SEARCH and STN networks); North American precipitation-chemistry measurements were extracted from NAtChem's precipitationchemistry data base and were provided by several U. S. and Canadian agencies (CAPMoN, NADP, NBPMN, NSPSN, and REPQ networks); the WMO World Ozone and Ultraviolet Data Centre (WOUDC) and its datacontributing agencies provided North American and European ozonesonde profiles; NASA's AErosol RObotic NETwork (AeroNet) and its data-contributing agencies provided North American and European AOD measurements; the MOZAIC Data Centre and its contributing airlines provided North American and European aircraft takeoff and landing vertical profiles; for European air quality data the folowing data centers were used: EMEP European Environment Agency/European Topic Center on Air and Climate Change/AirBase provided European air-and precipitation-chemistry data.
See-through AR To provide AR overlay on the live video: 1) The VSLAM tracking thread provides a position estimate for each frame of the live stream, then a virtual camera with the same intrinsic parameters of the tablet-PC camera is located at the estimated position in the virtual scene (cf. Fig. 4[b] ). 2) The image acquired by the virtual camera, taking into account the tablet-PC camera distortion, is rendered (cf. Fig. 4[c] ). 3) The fusion is performed (cf. Fig. 4[d] ) between the real camera image (cf. Fig. 4[a] ) and the rendered one (cf. Fig. 4[c] ). 5 Results The proposed system has been implemented in C++ with OpenCV and VTK libraries and executed on a Sony VAIO Duo 13 tablet-PC with Intel(R) Core i7 (1. 8 GHz), 8 GB RAM, with a camera of 640x480 at 30fps. Firstly, the system performance was evaluated on in-vivo data in terms of computation time with two volunteers, each of them laying on the table while the practitioner holds the tablet-PC and moved around them.
The difference is that in the first formula, the CSC specifies VM by attribute and in the second formula, VMID is given directly. These two options give the CSC more flexibility to express their security requirements. In addition, the CSC declares the coexistence constraint by Formula 3: v i and v j can not be allocated on the same HOST. Formula 4 is used by CSP to express the deployment prohibition. Similar with Formula 2, HOST with HOSTID h i is not permitted to deploy VM(s) assigned with attribute. prohibition([hi], [Vattr name : V attr value ]) (4) In an example that we will use throughout the paper, we consider an De-vOps [6] use case. DevOps is an emerged software development methodology that enhances collaboration between development, quality assurance (QA) and IT operations. Numerous companies are actively practicing DevOps since it aims to help them to maximize the predictability, efficiency, security, and maintainability of operational processes.
Adoption of DevOps is being driven by many factors including using public IaaS. Suppose that a software company has to deploy 3 VMs (v1,v2,v3) in cloud for a development project. Each VM contains its metadata such as properties, required volume, QoS specification and security constraint. We suppose that each VM runs a project server and there exist three types of VM: production (prod), development (dev), and test. prod server runs live applications supporting the company's daily business and the data is public for e-business customers; dev server consists of development environment thus developers with private right can access it; test server is used to conduct software test between development and production phase and it is accessible by testers with private login account. At the same time, there exist 2 CSPs (h1,h2) and each has its own metadata such as price, location and state indicating if it is certificated by security audit organizations.
7 GHz with 16 GB of RAM running  Windows 7). Then different VMs (2 cores and 2 GB of RAM) are created on VirtualBox platform with Ubuntu system. We now install DevStack [16] based cloud framework, a quick installation of OpenStack [17] ideal for experimentation. Each VM is regarded as a physical HOST for the purpose of experimentation. At the same time, a JAVA based program runs as cloud broker and it connects VirtualBox platform by SSH protocol. The OrBAC policy is generated and managed by the JAVA-based OrBAC API  [18]. Figure  3  illustrates our experimental architecture. This experiment measures the duration for contract processing which is the runtime required by the broker to process the JSON [19] based WS-Agreement file and generates VM and HOST list. Since there does not exist a great difference between SLA contracts of VM and HOST, here we measure contract processing time for VMs.
Acknowledgments. The work reported in this paper has been supported by  ANRT  ( Association Nationale de la Recherche et de la Technologie ) and Orange as a CIFRE ( Conventions Industrielles de Formation par la REcherche ) thesis and the work of  Nora Cuppens-Boulahia  and  Frédéric Cuppens  has been partially carried out in the  SUPERCLOUD  project, funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  643964.
Fifteen items associated with 'self-hate' were excluded given the stronger theoretical association between self-hate and shame as opposed to guilt  (Gibson, 2013). The ESS is a 25-item questionnaire that measures experiential, cognitive and behavioral components of trait shame, with questions aimed to uncover personal levels of shame experienced over the past year. Each item is rated on a 4-point Likert scale, from 1-not at all to 4-very much (e. g. , "Have you worried about what other people think of you when you said something stupid?"). Total trait guilt and shame scores were obtained by summing scores on all items from the IGQ-67 (excluding self-hate items) and EES, respectively. These total scores were used as continuous interval-type data for all analyses as per prior literature (e. g.
Statistical analysis The unique effects of trait guilt and shame, and their interaction with age, on thickness in each cortical ROI, and amygdala volume, were examined via regression analysis in SPSS (SPSS Inc. , Chicago, IL). For each regression for guilt, ROI structure was entered as the dependent variable. Age, sex, time between MRI scan and questionnaire completion, and trait shame were entered in the first block, two-way interactions were entered in the second block (age × sex, age × guilt and sex × guilt), and the three-way interaction between age, sex and guilt was entered in the third block. Each regression for shame was set-up similarly. As is standard in studies of ROI volumes (due to the association between ROI volumes and overall head size), estimated total intracranial volume was included as a covariate in amygdala analyses.
Acknowledgments This work was supported by a  National Health and Medical Research Council of Australia (NHMRC)  Project Grant to BJH (ID:  1064643 ). BJH was supported by a  NHMRC   Clinical Career Development Fellowship  (ID:  628509 ). SW was supported by a  NHMRC   Biomedical Career Development Fellowship  (ID:  1007716 ). We thank  Dr. Rebecca Kerestes  and  Ms Katerina Stephanou  for their assistance with data collection. We also thank staff from the  Sunshine Hospital Medical Imaging Department  ( Western Health, Melbourne ) for their contribution to this work.
Existing workflows for the analysis of multi-omic microbiome datasets are lab-specific and often result in sub-optimal data usage. Here we present IMP, a reproducible and modular pipeline for the integrated and reference-independent analysis of coupled metagenomic and metatranscriptomic data. IMP incorporates robust read preprocessing, iterative co-assembly, analyses of microbial community structure and function, automated binning, as well as genomic signature-based visualizations. The IMP-based data integration strategy enhances data usage, output volume, and output quality as demonstrated using relevant use-cases. Finally, IMP is encapsulated within a user-friendly implementation using Python and Docker. IMP is available at http://r3lab. uni.