dataesr/scientific-paragraphs-categorization huggingface dataset:
The most common pattern at the 41% of sites where the trends diverge is a significant decrease in 4MDA8 and non-significant trend for the other metrics, followed by a significant decrease in 4MDA8 and NDGT70 and a nonsignificant trend for the other metrics. In addition, the 4MDA8 and NDGT70 metrics had a common trend at ~80% of all sites. A key issue for further research is to understand the sensitivity of the ozone health metric trends to the site type classifications, particularly across a range of urbanization, for example, low density suburban areas in North America and Australia compared to high density urban centers in Asia such as Hong Kong, Tokyo, Beijing or Delhi. A comparison of the urban classification developed and used in this study to the urban indicators associated with the GPWv3 dataset Global Rural-Urban Mapping Project (GRUMP), v1 urban (SEDAC/CIESIN 2015) would be highly beneficial, as well as establishing the sensitivity of our urban classification to the underlying population data by using the latest GPWv4 global population dataset.
In addition, these station classifications are based on the year 2010; changes in land use driven by population growth or development may change the extent of urbanization around a station and further work is needed to assess how this urban classification may vary over the specified trend time periods. Sensitivity analyses of the trend metric results to the averaging period (i. e. seasonal or annual) and to the trend data period has been performed. Using an annual rather than a seasonal averaging period for the AVGMDA8 metric results in different trends at 27% of all sites. The sensitivity of trend estimate to different time period lengths is also investigated for 4MDA8 and SOMO35. With a shorter 2005-2014 trend period, at some stations in Europe the magnitude of decreasing trends became larger.
g. a late spring/early summer peak in Japan but a November peak in Hong Kong, metrics that employ warm season peak values or averages may need to consider this variation. Finally, there are many uncertainties associated with assumptions for trend calculations in this study which are discussed in TOAR-Surface Ozone database and TOAR-Metrics. However, the general convergence of results across the five metrics suggests the main features of change in ozone for health-relevant and policy-related metrics over the 2000-2014 period are well captured. Therefore, the TOAR database of surface ozone health metrics provides an exciting opportunity for further research, and shows that beyond the dense ozone monitoring networks in North America, Europe, Japan and South Korea ozone monitoring in most countries is sparse and would require significant expansion to characterize the ozone air quality impacting their citizens, especially across large regions of Asia and Africa.
Ozone monitoring in some countries, such as China and Thailand, is more extensive than is indicated by the TOAR database, which is limited to validated data sets that were contributed by air quality agencies and research groups. It is hoped that future TOAR assessments will be able to close some of the biggest gaps and provide a more complete global description of ozone and its relevance for human health. In support of current research needs, TOAR has assembled the world's largest collection of ozone metrics, calculated consistently for all available monitoring sites. These metrics are publicly available to the research community and TOAR encourages their use for future analyses that quantify the impact of ozone on human health.
a) Population density >15,000 people/km 2 , and b) Nighttime lights (at 1 km resolution) ≥60 (dimensionless light intensity). Nighttime lights within a 25 km radius of the monitoring site were also examined to rule out spurious assignments in rural areas (Note the nighttime light data becomes saturated at 63, same scale as above, see TOAR-Surface Ozone database, for details of the classification procedure). The use of two types of metadata ensures consistency in site classification globally. The subset of urban sites included 1,453 of 4,801 stations based on the period 2010-2014, and is depicted in Figure 2.
Acknowledgements We acknowledge the many authors and contributors to the  TOAR-Surface Ozone database  (see Schultz et al. , 2017). We thank  Audrey Gaudel  and  Sara Fenech  for aiding the production of Figure 3, and  Kristina Steinmar  for aiding in the production of Figure 1. Human population data are from the Gridded Population of the World:  Future Estimates GPWv3 , for the year 2010, at a resolution of 2. 5 arc minutes, or 4. 6 km ( ~5 km),  Center for International Earth Science Information Network (CIESIN) ,  Columbia University ;  United Nations Food and Agriculture Programme (FAO) ; and  Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World: Future Estimates (GPWFE). Palisades, NY: Socioeconomic.
Data Accessibility Statement The TOAR data portal on PANGAEA (https://doi. pangaea. de/10. 1594/PANGAEA. 876108) contains ozone statistics (including metrics for assessing health, vegetation, and climate impacts), trend estimates, and graphical material. In particular, all maps and box and whisker plots of the ozone metrics used in this paper are archived at: https://doi. pangaea. de/10. 1594/PANGAEA. 876109. The TOAR data portal also provides free and unrestricted access. All use of TOAR surface ozone data should include a reference to the TOAR-Surface Ozone Database  (Schultz et al. , 2017). Data and Applications Center (SEDAC), Columbia University. Available at: http://sedac. ciesin. columbia. edu/ gpw, data downloaded on October 19, 2011.
21  present, the average diameter of NCs in a given batch is mostly determined by means of transmission electron microscopy (TEM). Operated in the most common, bright field mode, contrast in the 2D images results from a difference in mass and thickness between the sample and the carbon-film support. Therefore, the crystal regions of NCs will appear dark and this enables sizes to be determined by automated software packages, such as Digital Micrograph and ImageJ. Herein, particles are localized and measured by a variety of thresholding algorithms. 22  should be noted that the interpretation of such images is often hampered by the simultaneous occurrence of the contrast-forming phenomena,  23  and that the particle-background boundary determination is prone to operator bias. In the particular case of semiconductor NCs or quantum dots, TEM imaging is typically used to construct calibration curves that relate the NC diameter to the energy of the band-edge transition.
Acknowledgement We are grateful to  J. Perez  and  T. Bizien  for assistance during beamtime and to the  SOLEIL  staff for smoothly running the facility. We thank  ESRF  for provision of the beamtime and  S. Prevost  for help during the measurements. We appreciate the scientific discussions concerning imaging of nanocrystals by transmission electron microscopes with  Jordi Arbiol. Z. H. acknowledges  Ghent University  ( GOA 01G01513 ) for funding. Benjamin, do you want to include some fundings?.
In the first part, annual nitrogen depositions to the Baltic Sea basin and its nine sub-basins were calculated for the period 1995-2014. These calculations have considered all EMEP emission sources, including Polish sources. Polish contribution was calculated as absolute (mass of nitrogen deposited) and relative, in percent of total deposition from all EMEP sources. In the second part of the study, we have focused on one year currently available in the EMEP data base-2013. For the same year in addition to standard EMEP MSC-W model with 50-km resolution, we have also used the air pollution model developed at the Institute of Meteorology and Water Management (IMWM) in Poland  (Mazur 2008; Mazur et al. 2014 ) with better spatial resolution-14 km. The year 2013 was chosen because this was the year for which the results of IMWM model were available.
Comparison of the EMEP and IMWM Model Results When comparing the results of EMEP and IMWM model some important differences between them, in addition to spatial resolution, should be taken into account. These two models use different sets of chemical reactions, different parameterizations of wet and dry deposition, different initial and boundary conditions, and different meteorological data as input. The emission of nitrogen data is the same in terms of country annual total values, but the spatial distribution of emissions is slightly different because of different numerical grids and spatial resolutions in both models. In addition, the way of computing Polish contributions to nitrogen deposition was different for two models. Having all these differences in mind, we do not expect a perfect agreement between EMEP and IMWM model results.
We assume that the EMEP MSC-W model is more correct in this case for two reasons. First, comparison of two versions of the EMEP MSC-W model, standard with 50-km resolution and experimental (0. 1°× 0. 1°in geographical coordinates) with approximately 11-km resolution at 60°N, performed for the year 2013  (Simpson et al. 2015)  showed good (within 10%) agreement between calculated nitrogen depositions. The second reason is a continuous evaluation of the EMEP model by comparing its results with measurements in over 100 European stations including so-called HELCOM station in the Baltic Sea region  (Gauss et al. 2015). The IMWM model has also been verified against measurements  (Mazur 2008 (Mazur , 2016)) , but not so much in the Baltic Sea region. Both EMEP and IMWM models use the same nitrogen emission data for the year 2013, however in different spatial resolution.
Acknowledgements We are grateful to our colleagues in EMEP MSC-W in Oslo and in IMWM in Warsaw for stimulating discussions and help with meteorological data. Part of this study was funded by HELCOM in the frame of cooperation between HELCOM and EMEP. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
RL-based Dialog Manager Before going to the details of our method, we provide some background on the RL-based dialog manager in this section. Fig. 2  shows an overview of such dialog manager. We describe each of the parts briefly below. Feature Extraction At the t-th turn of a dialog, the user input u t is parsed into domain specific intents and slots to form a semantic frame a u t by a language understanding (LU) module. o u t and o s t-1 are the one-hot representations of such semantic frames for the current user input and the last system output respectively. Alternatively, o u t can be a simple n-grams representation of u t. But the vocabulary size is relatively large in real-world applications. It will yield slow convergence in the absence of a LU module. Based on the slot-value pair output with the highest probability, a query is sent to a database to retrieve user requested information.
o db t is the one-hot representation of the database result. As a result, the observable information x t is the concatenation of o u t , o s t-1 and o db t. State Representation Based on the extracted feature vector x t and previous internal state s t-1 , recurrent neural networks (RNNs) are used to infer the latent representation of dialog state s t at step t. Current state s t can be interpreted as the summary of dialog history h t up to current step. Dialog Policy Next, the dialog state representation s t is fed into a policy network. The output π(a|h t ; θ) of the policy network is a probability distribution over a predefined system action set A s. Lastly, the system samples an action a s t ∈ A s based on π(a|h t ; θ) and receives a new observation x t+1 with an assigned reward r t. The policy parameters θ can be learned by maximizing the expected discounted cumulative rewards: J (θ) = E T -t k=0 γ k r t+k (1) where T is the maximal step, and γ is the discount factor.
Domain The dialog system provides restaurant information in Beijing. The database we use includes 2988 restaurants. This domain consists of 8 slots (name, area, price range, cuisine, rating, number of comments, address and phone number) in which the first four slots (inform slots) can be used for searching the desirable restaurant and all of these slots (request slots) can be asked by users. In each dialog, the user has a goal containing a set of slots, indicating the constraints and requests from users. For example, an inform slot, such as "inform(cuisine=Sichuan cuisine)", indicates the user finding a Sichuan restaurant, and a request slot, such as "request(area)", indicates the user asking for information from the system  (Li et al. , 2016 (Li et al. , , 2017b;; Peng et al. , 2017).
Implementation of the Original System For the original RL-based dialog system, a feature vector x t of size 191 is extracted. This vector is the concatenation of encodings of LU results, the previous system reply, database results and the current turn number. The LU module is implemented with an SVM 5  for intent detection and a CRF 6  for slot filling. The language generation module is implemented by a rule-based method. The hidden dialog state representation is inferred by a GRU  (Chung et al. , 2014). We set the hidden states of the GRU to be 120. The policy network is implemented as a Multilayer Perceptron (MLP) with one hidden layer. The size of the hidden layer is 80. The output dimension of policy network is 15, which corresponds to the number of system actions. To encourage shorter interaction, we set a small per-turn negative reward R turn = -1.
To train the contrast system, about 2600 episodes are sampled by interacting with a new interaction environment. But no additional data is used to train the extended system. In Fig. 4 (b), the "Satis. " of the extended system is slightly higher than the contrast system. This is due to the fact that the extended system learns how to deal with new user actions from logic rules but the contrast system obtains dialog policy by exploring the environment. As a result, the contrast system learns a more flexible dialog policy than the extended system 10. However, the "Satis. " has a bias to the suboptimal rules,    Left column shows the dialog context condition; Right column shows the corresponding system action. We define 14 rules in all to handle newfound intents and slots shown in Table  3. rather than the optimal policy gained from the environment.
introduced knowledge distillation to transfer knowledge from  ( | ; )  sample s t a Request(price) Policy Net Query: Select * RNN dialog state Inform(area=Beihai) Request(area) encoding encoding 1 s t a  u t o 1 s t o  t x 1 t t a h t s Figure 2: An overview of the RL-based dialog manager 10 Germy Victorian Godear Holiday Do Eat Database where area=Beihai encoding u t a db t o t s used in our work 3. In the last turn, the system inquires "Where do you want to go?". In current turn, the user input is "Find a restaurant in Beihai. ". a large highly regularized model into a smaller model. The knowledge which can be transferred has not been restricted to models. Stewart and Ermon (2017) proposed to distill the physics and s  domain knowledge to train neural networks with- Zhang et al. (2017) et al.
Moreover, the switching threshold and range of domain wall stabilization depend on the bulk spin transfer torque polarization as well as the interfacial spin transfer torque polarization coefficients. The gain in current efficiency in hard/soft structures studied is larger when lateral sizes are smaller and the current-driven reversal is more uniform. This work was supported by the French Agence Nationale de la Recherche, ANR-10-BLANC-1005 "Friends" by the ANR-NSF Project, ANR-13-IS04-0008-01, "COMAG" by the ANR-Labcom Project LSTNM, by the European Project (OP2M FP7-IOF-2011-298060) and by the Université de la Grande, Institut Carnot ICEEL, the Region Grand Est and Grand Nancy. Work at UCSD supported by the NSF award DMR #1312750.
Acknowledgements This work was supported in part by project  SIGMA  (EDF, AREVA, CEA and ENEL) and by the  Slovak Research and Development Agency  under the contracts  APVV-15-0560  (Project  ID-EFFECTS ). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creat iveco mmons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Upper Rhinegraben Previous geophysical investigations have shown that the magnetic anomalies, which strike NNE-WSW to NE-SW in the northern Vosges, continue in the same direction across the Rhinegraben towards the area of geothermal interest. Therefore, interpretations of geophysical data in terms of the petrology and structural pattern of the Rhinegraben basement require knowledge of the geological and geophysical characteristics of the northern Palaeozoic Vosges. et al. 1970; Elsass and von Eller 2008; Skrzypek et al. 2014 ). The volcanic activity was manifested by the Middle Devonian and possibly older basalts and by acidic pyroclastic rocks that were formerly known as the "spilite-keratophyre association" of the Bruche valley. These rocks show bimodal tholeitic and calcalkaline affinities  (Ikenne and Baroz 1985).
Existing geophysical data of northern Vosges and adjacent Rhinegraben The available gravimetric data consist of a dense network of measurements carried out in the western Rhinegraben by CGG on behalf of the Mines de Potasse d' Alsace in the 1950s and plotted on hand-drawn Bouguer maps at a scale of 1/50,000. The maps were digitized and processed by  Rotstein et al. (2006). The databases for the Vosges from the Bureau de Recherches Géologiques et Minières (BRGM) and the eastern Rhinegraben from the Bundesanstallt für Bodenforschung were compiled and added to the previous set. All of the results were presented in a global Bouguer map that included the Vosges, Rhinegraben and Black Forest  (Rotstein et al. 2006). The previous magnetic data concern only parts of the investigated area. They consist of (1) the aeromagnetic survey of NE France by the PREPA company, which was carried out in the early 1950s, digitized and plotted by  Edel and Schulmann (2009) , (2) the aeromagnetic map of Germany published by the BGR (1976)  (Gabriel et al.
Acknowledgements This is a contribution of the  EOST-IPGS (Ecole et Observatoire de Sciences de la Terre-Institut de Physique du Globe-Université de Strasbourg)  and  ESG (Electricité de Strasbourg-Géothermie ) done in the framework of the  EGS Alsace  project and co-funded by  Electricité de Strasbourg , the  University of Strasbourg  and  ADEME (French Energy and Environnemental Agency). We thank  Fonroche Géothermie  for sharing aeromagnetic data and fruitful discussions before publication. The authors are grateful to  GEIE EMC  and  ECOGI  for sharing the Soultz and Rittershoffen well data, respectively. Dr. A. -S. Tabaud  graciously provided the heat-production values of the northern Vosges granitoids. Prof. K. Schulmann  kindly read the manuscript and made constructive comments and corrections.
The primary advantage of MCMC is the ability to produce a full approximation of the posterior distribution. As a result, MCMC is often treated as the benchmark to evaluate the performance of other parameter inference methods  [41, 24, 16]. In order to obtain good resolution of the posterior distribution, a large number of samples are required  [12, 13]. This makes MCMC very computationally demanding, as each MCMC iteration requires a model evaluation in order to compute the likelihood. As a result, using MCMC for parameter estimation is often too costly for a realistic large scale inference problem. Even with model reduction techniques, e. g. , Polynomial chaos, KL expansions, etc. , parameter estimation in MCMC may still be quite computationally prohibitive. Bayesian inference can also be cast as a filtering problem in which the posterior distribution is updated sequentially as data becomes available  [42] , an approach known as data assimilation.
63 ± 0. 10 R A. The reproducibility of standard measurements was on the order of 1. 8% for argon, 2. 6% for neon, and 9% for helium abundances, whereas the reproducibility of the isotopic ratio of the helium standard is 3. 5%. Blank contributions to sample analyses were ≤ 15% for 20 Ne (1 × 10 -16 mol) and ≤ 10% for 36 Ar (5 × 10 -16 mol; with the exception of gas-poor sample 14TG21, for which the Ar blank represented 24%); helium blanks were below the detection limit (≤10 -16 mol). Analyses with blank contributions ≥ 20% are not considered for further discussion. The uncertainty attributed to the data (1 sigma) includes both internal (i. e. , counting statistics) and external (i. e. , standard reproducibility) analytical uncertainties. The helium isotopic composition and the 4 He/ 20 Ne ratio of fumarole gases were analyzed with the Helix SFT multicollector noble gas mass spectrometer, following separation of the He-Ne fraction from the heavy noble gases using liquid nitrogen cooled activated charcoal.
Acknowledgments This contribution is dedicated to the memory of  Pete Burnard  with whom we loved sharing time, his knowledge and happiness during East African field trips. Clémentine Le Gentil  is thanked for her help with the analytical treatment of the fumarole data. Constructive reviews by  Masahiko Honda ,  Samuel Niedermann  and an anonymous reviewer are gratefully acknowledged. We thank the  Tanzania commission for science and technology (COSTECH)  for the field permits. We thank all the guides and porters for their help during field work. The authors acknowledge the help of  Robert Dennen  for English editing. This work has been supported by the  French National Research Agency  through the  national program  " Investissements d'avenir " with the reference  ANR-10-LABX-21-01/LABEX RESSOURCES21 , and through the project  GECO-REE  ( ANR-16-CE01-0003-01 ; P.
Methods The PanCareSurFup (PCSF) consortium pooled data from 13 cancer registries and hospitals in 12 European countries to evaluate subsequent primary malignancies, cardiac disease and late mortality in survivors diagnosed between ages 0 and 20. Additionally, PCSF integrated radiation dosimetry to sites of second malignancies and to the heart, developed evidence-based guidelines for long-term care and for transition services, and disseminated results to survivors and the public.
PanCareSurFup disseminated information (WP7) about the study to the general public, health professionals and survivor/parent groups. Management expertise to run the consortium resided within WP8. The tasks and objectives of PCSF were as follows: To meet the needs for sufficient sample size and detailed information on cardiac disease, second malignancies, and late mortality, a dataset that was supra-national in scope, yet extensive in its information on individual patients, was necessary. Collecting and harmonizing these data was the first task of PanCareSurFup; these methods are described by Grabow et al  [8]. A second task ran in parallel, namely a survey of European cancer registries to determine the availability and quantity of data on long-term survivors. The goal of radiation dosimetry was to establish the dose and location of radiotherapy for cases and controls enrolled in studies of cardiac disease and second malignancies  [9].