dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation metrics We have evaluated the method's performance using ROUGE  (Lin, 2004)  quality of an automatically-generated summary by comparing it to a "gold-standard", typically a human generated summary. ROUGE-n measures ngram precision and recall of a candidate summary with respect to a set of reference summaries. We compare the system-generated summary with two reference summaries for each article in the dataset, and show the results for ROUGE-1, ROUGE-2 and ROUGE-SU4 that allows skips within n-grams. These metrics were shown to perform well for single document text summarization, especially for short summaries. Specifically,  Lin and Hovy (2003)  showed that ROUGE-1 achieves high correlation with human judgments. 4.
At wavelengths shorter than 300 nm isomerization forming peroxynitrite is added as an additional pathway to the overall reaction scheme at longer wavelengths. Strong cage effects and a product pool that might recombine to nitrate are also present at higher excitation energies, cf. Figure  1  and the Appendix. The goal of this work is to study nitrate photolysis in natural snow under controlled conditions including light spectrum and temperature while flushing the snow to test for desorption and recombination. This paper describes the setup, sample handling, data analysis, and interpretation in terms of photochemistry, while the companion paper discusses the results for the stable isotopic composition of nitrate for the same samples. In the present study, the quantum yield is determined by measuring the photon flux inside the snow and the nitrate concentrations before and after illumination with a Xenon lamp.
F. Optical characterization An optical detection system consisting of a Maya2000PRO photo-spectrometer (wavelength region 220-400 nm), an optical fibre (solarized, 600 μm diameter), and a cosine corrector (CC-3-UV-T) was calibrated using a calibrated light source (DH-2000-CAL, all components Ocean Optics, Inc. ) in order to measure absolute irradiances. Measurements were taken inside the snow column, comparable to Phillips and Simpson. 44 All spectra were corrected for dark noise, the integration times were varied depending on signal strength (up to the instrument maximum of 10 s) and spectra were averaged at least 10 times. A calibration using the DH-2000-CAL lamp was performed at room temperature. All measurements were performed in a walk-in cold room at -15 • C (the Xe lamp, snow cell, and photo-spectrometer were inside the cold room while the PC for data acquisition was kept outside).
All measured irradiances were corrected accordingly. Irradiances measured at positions 1 and 3 were added, and the data from position 2 doubled, in order to account for the full field of view at both the edge and the center of the snow column. This approximation is valid because snow is highly scattering, i. e. , major parts of the radiation along the optical axis are well represented and not excluded, as was tested by longitudinal measurements. As a second approximation, a circular, Gaussian distribution of the photon flux in the base plane of the disk was assumed. Note that the photon flux obtained using this method is non-zero at the edge, equivalent to a disc-shaped offset of the Gaussian beam profile ((b) in Figure  4 ). Finally, the average of the derived profile is taken as the photon flux at that specific depth, I(λ, z).
For further data processing, an exponential fit at each wavelength was used to construct a synthetic profile of the photon flux based on the measured value at z = 1 cm. Similarly, the filter irradiances measured at z = 1 cm are used to reconstruct the filtered photon flux at all depths inside the snow cell. In other words, Eq. (  8 ) is also used to derive I(λ, z) for the filtered cases, using the same η(λ) and I 0 (λ) for the filtered cases. This parametrization allows accurate modeling of the photon flux for each UV filter. Using the synthetic instead of the measured photon flux and assuming a Gaussian beam profile reduces uncertainties in the measurements. Besides, the uncertainty in the calibration spectrum uncertainties include: (i) The measurement method is intrusive which might allow some photons in the column to be absorbed by the fibre and cosine corrector without being detected, (ii) variations in the placement of the filled snow cell with the probe installed relative to the lamp (angle and side ways) changed the signal within a range of 20 %, (iii) placement of the probes inside the snow was accurate to within ±1 mm, both radially and laterally, (iv) the difference in temperatures between the photolysis experiments (-30 • C) and the photon flux measurements (-15 • C), (v) the distance between the lamp and the snow cell was accurate to within ±1 cm; given the low atmospheric absorption in the wavelength region of interest, this plays a minor role.
The large photolysis rates in the 200 nm band also enhance product species concentrations that might promote reactant reformation within the snow matrix. The cutoff wavelength used in this study to distinguish both bands (270 nm, Eq. (  5 )) allows only for a minor contribution of the 200 nm band to the magnitude of reported results of * 305. There is however the theoretical possibility that the 200 nm band causes some of the observed dynamics in the quantum yields. Based on the present dataset this cannot be discussed further, but other studies that reported changes in photolysis rates in snow (without λ < 300 nm present)  27, 29  support the attribution of such dynamics to the 305 nm band.
ACKNOWLEDGMENTS The research has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement No. 237890  (J. S. , T. A. B. , C. M. , and M. S. J. ). C. M. and M. S. J. thank the  University of Copenhagen  for supporting this research. J. S. and T. A. B. gratefully acknowledge The  Agence Nationale de la Recherche (ANR)  for its financial support through the  OPALE  project (Contract No. NT09-451281 ). LEFE- CHAT, a scientific program  of the  Institut National des Sciences de l'Univers (INSU/CNRS ), provided partial funding for this study (J. S. , T. A. B. , and J. E. ). J. S. , T. A. B. , and J. E. acknowledge  IPEV  for providing access to Concordia station through funding the  SUNITEDC program  ( 1011 ). J. S. has been supported by a grant from  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ).
Acknowledgements. We wish to thank the  ISI-MIP coordination team of the Potsdam Institute for Climate Impact Research. We also acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making their model output available. The  ISI-MIP  Fast  Track  project was funded by the  German Federal Ministry of Education and Research (BMBF) , project funding reference no. 01LS1201A. K. Nishina ,  A. Ito ,  E. Kato , and  T. Yokohata  were supported by the  Environment Research and Technology Development Fund  ( S-10 ) of the  Ministry of the Environment, Japan. Edited by:  D. Lapola.
The Global Burden of Disease 2010 Study is the largest study of its kind. It provides a large volume of information about the global burden of disease and associated risk factors. It estimates that lower respiratory infections, diabetes, diarrhea, and tuberculosis cause the greatest burden in the Pacific, and noncommunicable diseases caused a substantially greater burden in 2010 compared with 1990. Although the Pacific is considered to be a region rich in data, very little of these data has been analyzed, synthesized, and made publically available. Consequently, burden estimates for the Pacific are derived from models built with very limited data, and it is difficult to know how accurate they are. Health information in the Pacific needs strengthening, particularly in relation to data collection, analysis, use, and sharing.
Although the Pacific is considered to be a region rich in data  (13) (14) (15) (16) , very little of these data has been analyzed, synthesized, and made publicly available. Oceania was one of the 21 regions assessed in GBD 2010. It comprised the following PICTs (Table  1 ): American Samoa, Cook Islands, Fiji, French Polynesia, Guam, Kiribati, Marshall Islands, Federated States of Micronesia, Nauru, New Caledonia, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn Islands, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, Vanuatu, and Wallis and Futuna. This group of 23 countries and territories includes all 22 of the Secretariat of the Pacific Community member countries and territories. Norfolk Island is not a member of the Secretariat of the Pacific Community; it has a population of 2,302, and therefore has minimal impact on the overall results for Oceania  (18).
Summary of Findings The overall BoD in the PICTs was estimated to increase 20% from 4. 0 million DALYs in 1990 (uncertainty interval [UI], 3. 5 million to 4. 6 million) to 4. 8 million DALYs in 2010 (UI, 3. 9 million to 5. 8 million). However, when population increase was taken into account by calculating the DALY rate per 1,000 population, the BoD actually decreased by 23%, from 621 DALYs per 1,000 population in 1990 (UI, 546 to 714) to 481 DALYs per 1,000 population in 2010 (UI, 393 to 586)  (5). This suggests an improved average health status per capita, although this was not significant at the. 05 level. Table  2  shows the top 10 rankings for DALYs, YLLs, YLDs, and risk factors in PICTs for 1990 and 2010. The GBD 2010 estimates of deaths by age and sex were derived for 187 countries, using several methods and a large database of vital registration, verbal autopsy, surveillance, and other sources  (8).
Improving Reliability and Comparability of Burden Estimates Improving the reliability, analysis, use, and availability of national-level empirical data is needed to better understand the effect of disease, injury, and common risk factors on PICT populations as well as their progress toward key development indicators such as the Millennium Development Goals (25). This includes information collected through censuses, surveys, civil registration, routine surveillance of diseases and risk factors, and research studies. Table  3  highlights some data improvement needs and potential opportunities to address these. In particular, better data are necessary for those conditions that are estimated to cause a substantial burden but for which there are very little data available for PICTs. These include lower respiratory infections and diarrheal diseases for YLLs and depression and low back pain for YLDs.
Several initiatives exist to strengthen health information for PICTs, including the completeness, timeliness, validity, reliability, and comparability of data. There is a critical need for better harmonization to ensure a comprehensive, collective, and integrated approach. Pacific health ministers are well aware of the issue of the existence of data but the lack of use of these data. They have recommended "development of comprehensive training programmes to develop core competencies in 'data techs', 'epi techs' and epidemiologists" (p. 46)  (28). These data techs and epi techs are not full-blown epidemiologists, but rather people who have some training in data management, analysis, and interpretation. In response, the Pacific Public Health Surveillance Network (PPHSN) regional partners, under the leadership of Secretariat of the Pacific Community, have revitalized the existing PPHSN and Fiji National University collaboration for data for decision-making and are exploring ways to broaden this program to strengthen national and regional capacity for essential public health functions and services in the PICTs.
Operational research is the search for knowledge that informs policy and practice in the short and intermediate term, with a special focus on interventions, strategies, or tools that can enhance the performance (quality, effectiveness, or coverage) of the program in which the research is being conducted  (30). Establishing a joint health information and operational research agenda for PICTs, including BoD research in which countries identify common priorities and data and knowledge gaps to guide partners, would facilitate the evidence base for a more informed implementation of policies. Regional and national leadership is needed to enhance the perceived value and use of data. All of the above initiatives have great potential to contribute to rapidly strengthening, rationalizing, and harmonizing existing data collection and analysis efforts, including BoD estimates, to ensure good quality data are available for informing policy and programs in the region.
2000; Leckie et al. 2004 ) measured after fumigation-extraction  (Vance et al. 1987) , although this relation may vary according to the soil type and more specifically to their carbon content  (Leckie et al. 2004 ). The advantage of molecular biomass, compared with the biomass obtained after fumigation-extraction, is that it can be measured on dry soil samples at the same time as physical-chemical analyses and that it allows the development and application of referential systems, using a moderate-throughput system, for interpreting analysis data (cf. Section 2. 4). Functional communities, or those belonging to a particular taxonomic group, can also be quantified from soil DNA extracts by applying quantitative polymerase chain reaction (PCR used to amplify DNA) to determine the number of copies of the functional gene shared by populations in the functional community or of a specific sequence in the targeted taxonomic group.
ACKNOWLEDGMENTS We are grateful to  Nicolas Brunel  for insightful comments on a previous version of the model. We thank the two reviewers for their constructive comments and  Peter Follette  for his proofreading of the article. Frédéric Lavigne and Laurent Dumercy were supported by a grant from the  French Agence Nationale pour la Recherche   ANR-10-FRAL-009-01.
In this range of Reynolds numbers, instabilities and transitions are expected, which is very unfamiliar in the world of micro-flows. It is also very difficult to measure large velocities at the sub-millimeter scale. For these reasons, the dynamics and transition of micro-jets is a new and interesting research field which is related to the more general problem of gas flows at the micro-scale and in MEMS (cf. the GasMEMS network supported by the FP7 of the European Commission). At large scales, it is well-known that free-jets can exhibit very complex features, depending on the jet velocity U j. The shear layers growing around the jet play a significant role in the transition process through the creation of strong Kelvin-Helmholtz vortices. The structures created in the shear layers surrounding the jet usually develop as vortex rings.
Finally, the questionnaire collected information on self-described side effects of treatments and symptoms experienced over the last month (e. g. , nausea, fatigue, muscle and bone pain). Each eligible patient receiving a questionnaire was assigned a study number that allowed identifying her. Patients' names were not displayed on the questionnaires, but only blinded numbers. The information from patients' files was used to create a research database for the sole purpose of this study where each patient was identified by its number. Patients' names were not available on returned questionnaires, nor were the information contained in the questionnaire available to the clinician.
Data analysis We considered as CAM users patients who reported using at least one of the popular methods, physical (such as acupuncture), and especially pharmacological or dietary (e. g. , vitamin therapy, trace elements, homeopathy). Concerning side effects, we considered the quotes "not at all" and "some" as nil and the quotes "average intensity", "important" and "very important" as positive. For questions expressed as continuous variables (perception of side effects, effectiveness of CAM and scientific input) we considered 1 and 2 as negative notes, 3 and 4 as positive notes. The study included a descriptive analysis. A Chi2 test was performed to check for correlation of demographics and clinico-pathological factors and reported secondary effects with the intake of CAM.
The paper is organized as follows. A brief review on generative supervised classification is given in Section 2 as well as a presentation of related works in supervised learning with unobserved classes. Section 3 introduces an adaptive discriminant analysis method based on the mixture model which is able to detect unobserved classes and considers parameter estimation as well. Experimental results highlighting the main features of the proposed method on simulated and real datasets are presented in Section 4 as well as comparison with novelty detection methods. Section 5 presents an application of the proposed method to the detection of unobserved communities in social networks. Finally, Section 6 proposes some concluding remarks and discusses further works.
These Gaussian models were then used in  [10]  for high-dimensional data clustering. Mixture with a noise component Banfield and Raftery have introduced in [3] a mixture model with a noise component in order to improve the robustness of the cluster analysis on noisy datasets. The original work proposed to add to the mixture model a uniform distribution over the convex hull of the data as an additional component. Good results of the use of this specific mixture model were observed in different situations. Hennig and Coretto  [22]  proposed recently to use an improper uniform distribution that does not depend on the data for improving the robustness and provide a better approximation of the likelihood than the one proposed in the original work. An application of noise detection is proposed in Section 4.
The mixture model Let us consider a classical parametric mixture model of K components: the observations X = {x 1 ,. , x n } ∈ R p are assumed to be independent realizations of a random vector X ∈ R p with density: f (x; Θ) = K k=1 π k f k (x; θ k ), (1) where π k ≥ 0 for k = 1,. , K are the mixing proportions (with the constraint K k=1 π k = 1), f k (x; θ k ) is the density of the kth component of the mixture parametrized by θ k and finally Θ = {π 1 ,. , π K , θ 1 ,. , θ K }. We refer to the previous section regarding the choice of the mixture densities. For the mixture model (1), the complete log-likelihood has the following form: ℓ(x 1 ,. , x n ; Θ) = n i=1 K k=1 s ik log (π k f k (x i ; θ k )) , where s ik = 1 if x i belongs to the kth class and s ik = 0 otherwise. However, this work considers a specific learning situation in which only C classes are represented in the learning dataset X = {x 1 ,.
Parameter estimation: inductive approach The inductive learning context is, conversely to the previous situation, a more classical situation in supervised classification because it is more convenient to keep only model parameters to classify new observations than keeping all learning observations. In particular, the inductive approach is the only tenable approach for large dataset classification and real-time dynamic classification. However, the inductive approach poses a more complex problem since the mixture parameter estimation can not be done using the classical way. We therefore propose hereafter an inductive approach made of a learning phase and a discovery phase. The learning phase In this first phase, only learning observations are considered and, since the data of the learning set are complete, i.
, x * n * }. Since these new observations are unlabeled, we have to fit the mixture model in a partially unsupervised way. In this case, the log-likelihood has the following form: ℓ(X * ; Θ) = n * i=1 C k=1 s * ik log (π k f k (x * i ; θ k )) + K k=C+1 s * ik log (π k f k (x * i ; θ k )) , where the parameters θ k for k = 1,. , C have been estimated in the learning phase and the parameters θ k for k = C + 1,. , K remain to estimate. Due to the constraint K k=1 π k = 1 on the parameters π k , the mixture proportions of the C known classes have to be re-normalized according to the proportions of the K -C new classes which will be estimated on the new sample {x * 1 ,. , x * n * }. However, the test set {x * 1 ,. , x * n * } is an incomplete dataset since the labels z * i are missing and the s * ik are consequently unknown for all observations of this set.
An introductory example: the iris dataset The dataset considered in this first experiment is a classical one: the iris dataset made famous by its use by Fisher in  [17]  as an example for discriminant analysis. This dataset, in fact collected by Edgar Anderson  [2]  in the Gaspé Peninsula (Canada), is made of three classes corresponding to different species of iris (setosa, versicolor and virginica) among which the classes versicolor and virginica are difficult to discriminate (they are at least not linearly separable). The dataset consists of 50 samples from each of three species and four features were measured from each sample. The four measurements are the length and the width of sepal and petal. This dataset is used here as a toy dataset because of its popularity and its biological nature.
Comp. 2 Figure  2 : Detection of 2 unobserved classes with AMDA on the Iris dataset: the class "setosa" (red triangles) has been observed during the learning phase whereas the classes "versicolor" (green plus-es) and "virginica" (blue crosses) have not. figure presents a sample of new observations of iris for which the botanists are asked to classify. However, as the top-right panel indicates, this new sample contains individuals from a class which has not been observed by the botanists in the past and the iris experts will very likely classify all these new observations as belonging to either the class setosa or the class versicolor. The bottom-left panel of Figure  1  shows the result of such a scenario, using Quadratic Discriminant Analysis (QDA) in place of the iris experts, which yields to the classification of all virginica observations in the class versicolor.
Remark that, even though this result is disappointing from our point of view, it is understandable both for an human expert and a classification method since the classes versicolor and virginica are indeed very difficult to discriminate. The strategy proposed in the previous section, hereafter referred to by Adaptive Model-based Discriminant Analysis (AMDA), was applied to this dataset. The bottom-center and right panels of Figure  1  presents the results provided by AMDA (with the inductive approach). On the one hand, it turns out that model selection criteria (AIC here) succeed in identifying a new group of points in the test set. On the other hand, once the number K of mixture components chosen, AMDA classifies almost perfectly (only 2 errors on this example) the observations of the unobserved class virginica.
Detection of an unobserved noise class This second experiment aims to evaluate the ability of AMDA to detect an unobserved non Gaussian class of noise. For this, data were simulated in a 2-dimensional space according     approach) on the test dataset and shows that all noise observations were classified into the classes #2 and #3. The bottom-center and right panels of Figure  3  show respectively the AIC values for AMDA with and without a noise component and the classification provided by AMDA with a detected noise component (as indicated by the largest AIC value). We can observe on this quite complex example that AMDA succeeds in both detecting the unobserved noise class and modeling it through a uniform component. Table  1  confirms that AMDA recognizes all noise observations as belonging to one unobserved class in the past and makes only 2 false noise detections which is very satisfying.