dataesr/scientific-paragraphs-categorization huggingface dataset:
The terms for interaction associated with Comparison B indicate whether there are differences between sites in the IRRs adj among minorities compared to the reference population. The tests for the presence of interaction associated with both comparisons are identical. The IRRs adj were also estimated for minorities broken down by region of origin. That is, the rate in a certain minority was compared to that in the reference population in a separate analysis with similar first-order interaction terms. Data preparation, record linkage and estimation of standardized rates were performed using SPSS version 22. 0. The Poisson regression analysis was conducted using STATA version 13. 1.
Sixth, the denominator data were stratified by age, gender, migrant or ethnic minority group, and recruitment site. Thus, this incidence study was restricted to these variables and important determinants such as socio-economic disadvantage or cannabis use could not be taken into account. Analyses of the EU-GEI case-control data will enable the examination of the impact of these factors on the risk of psychosis among minorities and on regional differences in rates and rate ratios. Seventh, we used two different definitions for the minority: one based on self-assigned ethnicity (for the UK) and another on migration history. Since the large ethnic minority groups in Europe do have a recent migration history, most members are  also firstor second-generation migrants. As the denominator data (from the census) in the UK use a combination of selfassigned ethnic minority and birth country (that is, UK or abroad), we used a definition for the UK that was different from the definition used in the other countries.
Egerton et al. (2017)  supported this hypothesis by reporting an elevated striatal dopamine function in migrants and their children, but this study awaits replication in a larger sample. The British epidemiologist Marmot observed that persons lower in the hierarchy are more likely to be affected by a wide range of diseases, a phenomenon which he coined status syndrome  (Marmot, 2015). He argued that key factors related to a person's position in the hierarchy include a subjective sense of control over one's life (autonomy) and the opportunity for social participation. Our findings are in line with his ideas. Of note, the researchers of the present study also collected information on 1497 population-based controls at the pertinent sites. A recent analysis of this data showed that the adjusted odds ratio (OR) of psychosis for ethnic minorities compared to the white majority (1.
61, 95% CI 1. 31-1. 98) decreased following adjustment for social disadvantage (1. 52, 95% CI 1. 22-1. 98) and self-reported fluency in the dominant language (1. 22, 95% CI 0. 95-1. 57) (Jongsma et al. , 2020). Indeed, an imperfect mastery of the dominant language may contribute to an experience of social disempowerment and social defeat. Future studies of the EU-GEI case-control data will evaluate what factors explain the regional differences reported here. This may ultimately lead to tailored and site-specific interventions and may also give insight into protective factors that help persons from minority populations to cope with exposure to high risk. Future studies could also examine trends in incidence rates across calendar time to find out whether the high risks among ethnic minorities persist or decline following specific interventions.
Acknowledgements. The authors thank the following persons for their contribution to the design, organization and/ or data collection of the  European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI)  Project:  Bibiana Cabrera ,  Nathalie Franke ,  Daniella van Dam ,  Elles Messchaart ,  Floor van der Meer ,  Marion Leboyer ,  Franck Schürhoff ,  Stéphane Jamain ,  Grégoire Baudin ,  Aziz Ferchiou ,  Baptiste Pignon ,  Silvia Amoretti ,  Gisela Mezquida. The  EU-GEI  Project is funded by grant agreement  HEALTH-F2-2010-241909  (Project  EU-GEI ) from the  European Community's Seventh Framework Programme. The Brazilian study was funded by grant  2012/0417-0  from the  São Paulo Research Foundation. Dr Kirkbride  is funded by the  Wellcome Trust  and grant  101272/Z/13/Z  from the  Royal Society.
Species distribution model The area used for the SDM spanned from 58° to 61°N and from 12 to 19°E and embraced parts of the cold range margin of L. vernus. For the model, we considered only forested area (52 409 km 2 , Fig. 1 ). We modelled the distribution of L. vernus by relating presence data to six environmental variables at the highest possible resolution: climate, light, proportion of conifers, forest age, bedrock pH class and soil moisture. Presence data were obtained from a citizen science database (<https://artportalen. se/>, accessed 16 January 2016). We used only observations from the period 2000 to 2016 with a minimum spatial accuracy of 50 m. We filtered out observations outside forests and lumped the remaining points to a maximum of one observation per 50 m grid cell (obtaining 556 presences).
Pseudo-absence data were created by sampling 1000 forested background points at a distance of 1-50 km from any presence point. We used growing degree days (GDD) as a (micro)climate variable derived from high-resolution topoclimate grids of Sweden  (Meineri and Hylander 2017). Solar radiation (SR) in spring and summer was used as a proxy for light availability, and was a function of latitude and local topography  (Fu and Rich 2002). We also included the proportion of conifers (SLU Forest Map 2010), assuming that it partly reflected local light availability, light seasonality and soil pH. As another estimate of site acidity, we used the pH of the underlying bedrock, classified as base-rich, neutral or acidic (bedrock map from Geological Survey of Sweden, SGU jordarter vektor 1:25 000-1:10 000).
We used the topographic wetness index (TWI) as an indirect measure for soil moisture  (Sørensen et al. 2006). Lastly, to account for effects of land use we included forest age. Hypotheses for each variable are summarized in Table  1. All predictors were available as 50 m resolution raster files. In the SDMs we used the following algorithms: generalized linear models (GLM), generalized additive models (GAM), boosted regression trees (generalized boosting models, GBM) and random forests (RF). These were combined in an ensemble model using the R package biomod2  (Thuiller et al. 2016). We used the true skill statistics (TSS,  Allouche et al. 2006 ) and mean values of the area under the curve of a receiver operating characteristic plot to evaluate models (AUC,  Fielding and Bell 1997). To assess the role of environmental variables, we computed their relative importance  (Thuiller et al.
Recruitment probability was defined as the proportion of seeds sown in the first year that germinated the second year and were alive at the time of the recording. Growth was defined as the change in log-transformed size between two years, and size was defined as the sum of the products of basal area and length for each shoot ((diameter) 2 × π/4 × length). At each transplant site we took canopy cover photos after canopy closure (June 2016). Light was estimated by extracting the average percentage of white pixels from five binary canopy cover photos using the software ImageJ 1. 50b  (Abràmoff et al. 2004). Soil moisture was measured in volume percent with a soil moisture meter (Delta-T Devices) at three locations per site during six dry days in September 2017. To adjust for differences among measurement days, moisture was also measured at a reference site each day.
Vital rate models We estimated the effects of GDD, FDD, light, soil moisture and soil type on five vital rates (survival, growth, probability of flowering, number of seeds produced and recruitment probability), using generalized linear mixed effect models and site as a random effect in the R package lme4  (Bates et al. 2015). For survival, probability of flowering and recruitment probability, which were binary distributed, we used logit link functions, and for growth and number of seeds (square-root transformed), which were normally distributed, we used identity links. We included size in year 2016 as a predictor for survival, and size in 2017 as a predictor for probability of flowering and number of seeds in 2017. A quadratic term for size was added in the growth function to allow for non-linear relationships between previous and current size.
Acknowledgements -The authors thank  Daniela Guasconi ,  Simone Moras ,  Sonia Merinero ,  Benny Willman  and  Giulia Zacchello  for assisting in field,  Johan Dahlgren  for helping with the IPM,  Hildred Crill  for valuable comments on the manuscript and  Sveaskog  for allowing the transplant experiment on their property. Thanks also to two anonymous reviewers and the subject editor for improving the manuscript. Funding -This research was funded by a  FORMAS  project grant  2014-530  (to KH).
Computational Domain We develop a micro-scale computational mesh that covers low-lying areas of Manhattan and extends to NY/NJ Harbor and the Atlantic Ocean (Figure  1 ). The computational mesh consists of 668,977 nodes and 1,253,688 elements. The mesh resolution is between 3 and 8 m in Manhattan (Figure  2 ), between 100 and 1,000 m in NY/NJ Harbor, and reaches about 25 km in the deep ocean. The mesh is generated using the OceanMesh2D program  (Roberts and Pringle, 2018; Roberts et al. , 2019)  and quality-checked and manually trimmed using the SMS software  (Aquaveo, 2013). The developed mesh is utilized as the geographical mesh in both ADCIRC and SWAN. The spectral mesh in SWAN consists of 36 directional bins, and the lowest and highest wave frequencies are set to 0. 03 and 1 Hz, respectively.
3 m) Digital Elevation Model, which is a bare-earth digitalelevation surface model derived from 2010 Light Detection and Ranging data (Department of Information Technology and Telecommunications, 2013). The bathymetry data in NY/NJ Harbor are from the Continuously Updated Digital Elevation Model (CUDEM) with a spatial resolution of 1/9 Arc-Second (about 3. 4 m) (Cooperative Institute for Research in Environmental Sciences, 2014). Outside NY/NJ Harbor, the bathymetry is from Shuttle Radar Topography Mission (SRTM), version SRTM15 + V2 (Tozer et al. , 2019), which is a global dataset with a resolution of 15 Arc Second (about 500 m). The bottom friction coefficient is specified based on the land cover type from the 3 ft (about 9. 1 m) landcover dataset of NYC. We set the Manning's roughness coefficient to 0.
DATA AVAILABILITY STATEMENT The meteorological data for Hurricane Sandy were obtained from Oceanweather Inc. Tide data are available on https://www. tpxo. net/home. Measured high-water marks are available on the USGS Flood Event Viewer website (https://stn. wim. usgs. gov/ FEV/). Storm tide observations are from the NOAA Tides and Currents (https://tidesandcurrents. noaa. gov/). The outputs of the numerical simulations are available upon request to the authors.
The second objective is to discuss existing methodologies, including mathematical formulations and solution algorithms, and propose a new taxonomy of exact methods for the DNDP (Section 3). The third objective is to conduct a computational benchmark of selected methodologies to highlight existing computational bottlenecks and outline future research directions. To promote knowledge exchange and ensure results reproducibility, all codes of implemented methodologies, as well as new benchmark instances for the DNDP, are provided in a public repository available at https://github. com/davidrey123/DNDP. In achieving these objectives, this paper aims to provide a new foundations to engage the scientific community and to foster research in developing novel exact and scalable methodologies for the DNDP.
The authors used the VI formulation of  Luathep et al. (2011)  but combine it with an outer approximation of link travel time functions. Numerical results are only reported for Gao's instance. Bagloee et al. (2017)  proposed a B&B algorithm which uses a generalized Benders' decomposition approach to solve the SO-relaxation of the DNDP at each node of the tree. Results on Sioux Falls and Winninpeg's network are reported with up to 20 candidate projects. Although several efforts have been proposed to solve the DNDP or a linear approximation of this problem, the literature on exact or near-exact approaches remains scarce. Further, there is no reference datasets for benchmarking solution methods which undermines the research on this challenging bilevel optimization problem.
(2013) , wherein outer approximation schemes are used. In addition, the method MKKT is implemented as direct MILP approach unlike the Benders' decomposition scheme proposed in  Fontaine and Minner (2014). To measure the quality of the approximated solutions, the flow pattern corresponding to the best (lowest leader objective value) y solution among all three methods is calculated by solving the TAP as a convex problem. All methods are implemented in Python. All MILPs and LPs are solved using CPLEX 12. 8. Convex TAPs are solved using the Pyomo module and IPOPT. All solution methods were tested and implemented on the same Windows 7 machine with 16Gb of RAM and a CPU of 2. 7Ghz, in a single-thread mode with a time limit of 10 minutes. The upper bound on link flows xij was set to 1e 5 and this value is also used for M.
Budget sensitivity experiment. Methods SOBB, SOIC and MKKT are implemented for instances SF DNDP 10 and SF DNDP 20 for a budget B equal to B % = 25%, 50% and 75% of the total cost (i,j)∈A 2 g ij. The time limit is 10 minutes. AT is the average runtime in seconds, AG is the average relative optimality gap upon termination in % and TO is the proportion of time-outs in %. B % = 25% B % = 50% B % = 75% B % = 25% B % = 50% B % = 75% SF DNDP 10 SF DNDP 20 instances SF DNDP 20 require 20 binary variables. All instance data is provided in the public repository available at https://github. com/davidrey123/DNDP in the TNTP format. An extra column has been appended to provide link cost data (g ij ) and a value of 0 indicates that the link is part of the original network (before optimization). D % = 50% D % = 100% D % = 150% D % = 50% D % = 100% D % = 150%.
Increasing the demand to 150% of the base demand yields substantially easier problems, notably for SF DNDP 20 instances, and SOIC is found to outperfom other methods in terms of number of time-outs. To assess the quality of the linear approximation of link travel time functions, we compare the network travel time obtained by solving the TAP in its convex NLP form for the best y solution found among all three methods. The deviation observed is on average of 0. 28% with a standard deviation of 0. 25%. Detailed numerical results are provided in the supplementary material available at https://github. com/davidrey123/DNDP.
For this benchmark, a total of 20 new instances for the DNDP have been created. For reproducibility purposes, all implementation formulations, codes and benchmarking instances are available at https://github. com/davidrey123/DNDP. Overall, this study shows that even medium-size instances for the linearized DNDP can present considerable computational challenges. The numerical experiments revealed that some 10-project instances and most 20-project instances could not be solved within the imposed time limit (10 minutes). Although this time limit is relatively small, all methods were implemented using a piecewise linear approximation of link travel time functions which is expected to outperform outer approximations approaches. Further research is critically needed to develop scalable exact methodologies for the DNDP and its variants.
Code availability The source code for PMIFv1. 0 is included in the Supplement. To run PMIF, some input files are needed. The ODIAC inventory is available at http://db. cger. nies. go. jp/dataset/ODIAC/DL_odiac2018. html. The clump dataset is available at https://doi. org/10. 6084/m9. figshare. 7217726. v1. The list of clump information (e. g. index, latitude and longitude of the center), which is also needed as an input, is included in the Supplement. The wind fields from CCMP are available at http://www. remss. com/measurements/ccmp/. EDGAR v4. 3. 2 emission maps are needed to run the SectCS inversion, and are available at https://edgar. jrc. ec. europa. eu/overview. php?v=432_GHG.
Acknowledgement This work received support from the  TRACE Industrial Chair  ( UVSQ / CEA / CNRS / Thales Alenia Space / TOTAL / SUEZ) ANR-17-CHIN-0004  funded by the program « Chaires Industrielles 2017 » of  ANR. We would like to thank  Bernard Pinty  for providing the vision of a CO2 Monitoring and Verification Support (MVS) Capacity within the framework of the  EU's Copernicus Programme.
During the past 25 years, altimetric observations of the ocean surface from space have been mapped to provide two dimensional sea surface height (SSH) fields that are crucial for scientific research and operational applications. The SSH fields can be reconstructed from conventional altimetric data using temporal and spatial interpolation. For instance, the standard Developing Use of Altimetry for Climate Studies (DUACS) products are created with an optimal interpolation method that is effective for both low temporal and low spatial resolution. However, the upcoming nextgeneration SWOT mission will provide very high spatial resolution but with low temporal resolution. The present paper makes the case that this temporal-spatial discrepancy induces the need for new advanced mapping techniques involving information on the ocean dynamics.
An algorithm is introduced, dubbed the BFN-QG, that uses a simple data assimilation method, the back-and-forth nudging (BNF), to interpolate altimetric data while respecting quasigeostrophic (QG) dynamics. The BFN-QG is tested in an observing system simulation experiments and compared to the DUACS products. The experiments consider as reference the high-resolution numerical model simulation NATL60 from which are produced realistic data: four conventional altimetric nadirs and SWOT data. In a combined nadirs and SWOT scenario, the BFN-QG substantially improves the mapping by reducing the root-mean-square errors and increasing the spectral effective resolution by 40 km. Also, the BFN-QG method can be adapted to combine large-scale corrections from nadir data and small-scale corrections from SWOT data so as to reduce the impact of SWOT correlated noises and still provide accurate SSH maps.
g. ,  Mulet et al. 2012; Buongiorno Nardelli 2020). The most commonly used SSH maps, the Developing Use of Altimetry for Climate Studies (DUACS) products, are derived from a statistical space-time interpolation of nadir altimeter observations. This intrinsically limits the effective resolution [as defined in  Skamarock (2004) , i. e. , the fully resolved scales] of DUACS SSH maps to 150-200 km at middle latitudes  (Ballarotta et al. 2019). The SSH mapping algorithm was developed by CNES and CLS in 1997, as part of the DUACS project, and has been continuously improved since then  (Taburet et al. 2019). The DUACS products are now distributed by the Copernicus Marine Environment Monitoring Service (CMEMS). DUACS algorithm implements a statistical interpolation of SSH satellite data in space and time to produce global daily maps  (Le Traon et al.
1998). The data are collected by a constellation of 2 to 4 nadir-looking altimeters (sometimes referred to as conventional altimeters), and characterized by large data gaps reaching 200 km in the zonal direction at the equator. The Surface Water and Ocean Topography (SWOT;  Fu et al. 2012; Morrow et al. 2019 ) altimetry mission, to be launched in early 2022, will open the way to SSH maps with resolution significantly higher than 150 km at midlatitudes, but this perspective entails a thorough revisit of the mapping algorithm. SWOT will considerably increase the measurement density at the surface of the oceans thanks to SSH measurements at a kilometric pixel resolution over a swath 120 km wide. On the swath, SWOT is expected to resolve scales down to 15 km at low latitude and 30-45 km at mid-and high Denotes content that is immediately available upon publication as open access.
The relevance of using a simpler dynamical framework has been shown in  Ubelmann et al. (2015). Although the model considered is representing a very reduced dynamic (based on the conservation of potential vorticity), the quasigeostrophic (QG) dynamical framework is able to capture a large part of the mesoscale dynamics for small Rossby numbers  (Vallis 2006). As a proof of concept,  Ubelmann et al. (2015)  reconstructed the trajectory between two SSH fields 10 days apart from each other by averaging a model forward run (initialized by the first snapshot) and a backward run (initialized by the second snapshot). They show a significant improvement of the two dimensional reconstruction of SSH fields compared with a linear interpolation (such as the one implemented in the DUACS system). The method was then successfully applied with synthetic nadir altimeter data  (Ubelmann et al.
The backward nudging applies the same procedure on the backward-in-time model equations and the nudging term with opposite sign. The BFN iteratively combines the standard and the backward nudging to provide a smoothing over a time period of the best fit to the observations under the dynamical constraint  (Auroux and Blum 2008). In the present application, the QG model can be seen as a weak constraint to the altimetric data interpolation. The goal of this paper is to evaluate the SSH mapping abilities of the BFN-QG in comparison with the interpolation method, presently used in DUACS, with conventional altimetry and SWOT data. The methods are tested with simulated altimetric observations realistically distributed in space and time using an observing system simulation experiments (OSSE) approach.
The reference simulation (Nature Run) is a very high-resolution ocean simulation of the North Atlantic (NATL60; Molines 2018) from which simulated SSH observations are built considering four nadir and one SWOTlike virtual altimeter sampling along realistic orbits. The focus is made on one region of the Gulf Stream characterized by strong mesoscale dynamics. Three observational scenarios are investigated: the four nadirs only (Nadirs), the SWOT data only (SWOT) and the combination of the four nadirs and the SWOT data (Nadirs 1 SWOT). For the SWOT data, in the latter two scenarios, an alternative implementation of the BFN-QG where the nudging is applied to the relative vorticity only-named SWOT(j) in opposition to the standard nudging on the potential vorticity SWOT(q) that includes both relative vorticity and vortex stretching-is also investigated in anticipation of possibly strong correlated SWOT errors.
This alternative implementation is expected to reduce the impact of correlated SWOT errors on the SSH reconstruction. In each case, a systematic comparison with the DUACS products (made from the same synthetic dataset) is carried out to evaluate the relative performance of the new mapping technique in terms of accuracy and spectral resolution. The paper is structured as follows. Section 2 details the data assimilation method, from the theoretical aspects to the strategical implementation. Section 3 presents the experimental design, based on OSSE, to assess the ability of the BFN-QG to derive SSH maps from SWOT and nadir altimetry. In section 4, we evaluate the performances of the BFN-QG technique in comparison with DUACS products. Conclusions and perspectives are drawn in section 5.