dataesr/scientific-paragraphs-categorization huggingface dataset:
ECMWF The European Centre for Medium-Range Weather Forecasts (ECMWF) provides global analyses of atmospheric ozone from the ground to the lower mesosphere. Ozone profiles can be obtained from 2 different products from ECMWF: operational analysis or reanalysis. ERA-Interim  (Dee et al. , 2011)  is their latest global atmospheric reanalysis dataset and provides profiles every 6 hours, for 60 pressure levels between the surface and 0. 1 hPa. The altitude range of the operational analysis reaches pressure levels up to 0. 01 hPa with a vertical resolution of 137 levels. For the validation of GROMOS-C we compare with the operational data, because our retrieval altitude range goes up to 0. 03 hPa.
Reduction of the noise level of the spectra is performed by integration in time, being the minimum time resolution used 1 hour. Ozone profiles are retrieved from the resulting averaged spectra, yielding a reliable profile from roughly 23 to 70 km, with a vertical resolution of 10-15 km depending on the pressure level. The inversion process requires an a priori knowledge of the vertical ozone profile. This a priori information is taken from a zonal mean climatology using monthly mean Aura MLS data from 2004-2013. The a priori covariance matrix used has a fixed maximum value for the diagonal elements of 0. 4 ppm and a Gaussian correlation decay at neighbouring levels. The covariance matrix of the measurement is built as a diagonal matrix, where the elements in the diagonal are the square of the noise level of the spectra, which are assumed constant for all channels.
Forward model calculations require a temperature-pressure-altitude profile. This data is taken from the ECMWF model for the corresponding time and location, interpolated to the pressure grid. Spectroscopic parameters for the ozone line are taken from the JPL and HITRAN catalogs. We used the oxygen and water continuum and peaks from the Rosenkranz model  (Rosenkranz, 1998). We used water vapour profiles from the ECMWF model and oxygen and nitrogen mixing ratios are assumed to be constant. An example of a daily mean retrieved ozone profile is shown in Fig. 3a , the a priori profile used for the retrieval is plotted with a dashed line. The corresponding averaging kernels and measurement response are shown in response, is used as an indicator of the sensitive altitude range of the retrieval. We assume this altitude range delimited by the levels where the measurement response is larger than 0.
We observed a decreased altitude range for the last month of data, which corresponds to the very humid period and with high opacity. A comparable plot of Aura MLS ozone profiles is shown in Fig. 6  (below). The similarity is striking. The annual cycle increase in ozone in the middle stratosphere from winter to summer due to increased solar radiation is obvious. The diurnal cycle is also perceptible. Special features of this dataset are discussed below, in section 6. For a more quantitative comparison we have divided the profiles into 3 pressure levels, and plotted the mean volume mixing ratio for each layer (Fig. 7 ). Each point corresponds to a daily mean value; this time we have opted for the tipping curve calibration for the comparison. We chose altitude levels at 25-10 hPa, 10-2 hPa and 2-0.
Result and validation of wind measurements Although GROMOS-C was primarily designed for observations of ozone and carbon monoxide profiles its setup includes the capacity for middle-atmospheric wind measurements. Thanks to its relatively low noise temperature, to its sufficient spectral resolution, and to its ability to observe the atmosphere in the four cardinal directions zonal and meridional wind profiles can be assessed. The wind profiles are determined from the Doppler shifts of different parts of the Doppler shifted spectrum through an optimal estimation inversion, which was first described by  Rüfenacht et al. (2014). The retrieval is performed with the ARTS/QPACK software package  (Eriksson et al. , 2011 (Eriksson et al. , , 2005)). The atmospheric parameters of the forward and inverse model for the GROMOS-C wind retrieval were set analogously to the ones of the retrieval for the wind radiometer WIRA (see Sect.
measurements by the co-located wind radiometer (WIRA) and ECMWF model data are also shown. The grey lines in the upper panels represent the upper and lower limit of the trustworthy altitude range. This range is defined to be the region where the measurement response is higher than 0. 8, the altitude resolution higher than 20 km, and the altitude accuracy (defined as the offset between the peak altitude and the nominal altitude of the averaging kernel) is better than 4 km. In the vast majority of cases, the trustable altitude range for GROMOS-C is delimited by the averaging kernel criterion. Data outside the trustable altitude range should not be considered. The overall agreement between GROMOS-C, WIRA and ECMWF is clearly visible. The principal features such as the stratospheric wind reversal in July 2014 are captured by GROMOS-C.
Special case study Fig 6 shows an increase of ozone in early July between 4 and 1 hPa. This increase is evident in data from both GROMOS-C and MLS. This feature is presented in more detail in Fig. 10. The timeseries of ozone profiles measured by GROMOS-C have been plotted together with the isentropes (lines of equal potential temperature) taken from the ECMWF model. As air parcels travel on surfaces of equal potential temperature, the isentropes can be used to study the airflow and to identify vertical displacements. In Fig. 10 , an updraft is identified by dashed lines and by deviations of the isentropes at 1200, 1400, and 1600 K. The updraft has higher ozone mixing ratios and results in higher ozone at the correspondent pressure levels. The timeseries of temperature profiles from the ECMWF model are shown in the middle plot.
Conclusions The first validation campaign of GROMOS-C took place at the Maïdo observatory in La Réunion island, from June 2014 till January 2015. We validated its capability to measure continuous ozone profiles with high temporal resolution for a broad altitude range. We have confirmed different calibration methods lead to equivalent retrieved profiles as long as the spectra is baseline free. We demonstrated the advantages of using multiple calibration methods. When the Peltier load method failed due to high humidity, we had the option to use a different approach. The tipping curve method works well at very low brightness temperatures. It 10 Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2016 -117, 2016  Manuscript under review for journal Atmos. Chem. Phys. Published: 11 March 2016 c Author(s) 2016.
Although GROMOS-C was mainly designed for ozone observations its setup allows for middle-atmospheric wind profile measurements. The 4 directions of view allow retrieval of zonal and meridional winds from the Doppler shift. Winds measured by GROMOS-C have been validated using data from another co-located wind radiometer (WIRA) and using output from the ECMWF model. The overall agreement between GROMOS-C, WIRA and ECMWF is clearly visible. Important meteorological features such as the stratospheric wind reversal in July are clearly evident in GROMOS-C data. Thus GROMOS-C measurements can also be used to measure middle atmospheric wind profiles. We also identified an updraft event from an increase of ozone measured by GROMOS-C. The high temporal resolution ozone dataset presented here is unique for this location and will be useful given the dearth of such data from tropical locations.
This work has been funded by the Swiss National Science Foundation under grant number 200020-160048. The authors want to thank the electronics workshop of the IAP for their support during the campaign. The authors acknowledge the European Communities, the Région Réunion, CNRS, and Université de la Réunion for their support and contribution in the construction phase of the research infrastructure OPAR (Observatoire de Physique de l'Atmosphère à La Réunion). OPAR is presently funded by CNRS (INSU) and Université de La Réunion, and managed by OSU-R (Observatoire des Sciences de l'Univers à La Réunion, UMS 3365). 11 Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2016-117, 2016 Manuscript under review for journal Atmos. Chem. Phys. Published: 11 March 2016 c Author(s) 2016. CC-BY 3. 0 License.
Acknowledgements. This work has been funded by the Swiss National Science Foundation under grant number 200020-160048. The authors want to thank the electronics workshop of the IAP for their support during the campaign. The authors acknowledge the European Communities, the Région Réunion, CNRS, and Université de la Réunion for their support and contribution in the construction phase of the research infrastructure OPAR (Observatoire de Physique de l'Atmosphère à La Réunion). OPAR is presently funded by CNRS (INSU) and Université de La Réunion, and managed by OSU-R (Observatoire des Sciences de l'Univers à La Réunion, UMS 3365). 11 Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2016-117, 2016 Manuscript under review for journal Atmos. Chem. Phys. Published: 11 March 2016 c Author(s) 2016. CC-BY 3.
Following the principles of Linked Data (LD), data providers are producing thousands of interlinked datasets in multiple domains including life science, government, social networking, media and publications. Federated query engines allow data consumers to query several datasets through a federation of SPARQL endpoints. However, data providers just receive subqueries resulting from the decomposition of the original federated query. Consequently, they do not know how their data are crossed with other datasets of the federation. In this paper, we propose FETA, a Federated quEry TrAcking system for LD. We consider that data providers collaborate by sharing their query logs. Then, from a federated log, FETA infers Basic Graph Patterns (BGPs) containing joined triple patterns, executed among endpoints.
Introduction Linked Data (LD) interlinks massive amounts of data across the Web in multiple domains like life science, government, social networking, media and publications. Federated query engines  [1-3, 5, 9, 11]  allow data consumers to execute SPARQL queries over a decentralized federation of SPARQL endpoints maintained by LD providers. But, data providers are not aware of users' federated queries; they just observe subqueries they receive. Thus, they do not know when and which datasets are joined together in a single query. Consequently, the federation does not hold enough meta-information to ensure services, such as, efficient materialization to improve joins, activation of query optimization techniques, discovering data providers partnership, etc. Knowing how provided datasets are queried together is essential for tuning endpoints, justify return of investment or better organize collaboration among providers.
, SQ n ]. Subqueries are processed by endpoints of the federation at given times represented by timestamps. We suppose that endpoints' clocks are synchronized, i. e. , timestamps of logs can be compared safely. qe i , is represented by E qei (F Q i ). In addition, we represent a concurrent execution of n federated queries by E(F Q 1. F Q n )) = [SQ 1 ,. , SQ n ]. This work addresses the following research question: if data providers share their logs, can they rebuild the BGPs annotated with the sources that evaluated each triple pattern? From the previous example, we aim to extract two BGPs: one corresponding to CD3 {tp @IT 1. (tp 2. tp 3 ) @IB. (tp 4. tp 5 ) @N Y T } and another to CD4 {(tp 1. tp 2. tp 3 ) @LM DB. (tp 4. tp 5 ) @N Y T }. Next definitions formalize this problem.
To simplify, all annotations to sq i are omitted. g1 ∈ G sq 1 ={?x p1 ?y} @ep1 sq 3 ={s1 p2 ?y} @ep2 sq 4 ={s2 p2 ?y} @ep2 g2 ∈ G sq 2 ={?z p1 o1} @ep1 Graph reduction. The graph of queries is transformed into a graph of patterns. This heuristic aggregates triple patterns, produced from mappings of the outer dataset towards the inner dataset, into one big aggregated pattern (that we call inner pattern). This pattern, for instance, has the form of injected_values,predicate, object , if mappings of the outer dataset are injected into the subject. Graph reduction significantly reduces the size of each g ∈ G, because nestedloops can be executed with hundreds of subqueries. Figure  4 , illustrates G after the graph reduction phase, for our motivating example. g1 ∈ G ?x p1 ?y @ep1 ?x p2 ?y @ep2 ?z p1 ?y @ep1 ?z p2 ?y @ep2 Fig.
5:  Deduced graphs in G after N estedLoopDetection, for an infinite gap. Nested-loop detection. This heuristic analyzes existing graphs in G to identify nested-loops. From n subqueries, it obtains two joined triple patterns by nested-loop. To do this, Algorithm 1, Lines 3-6, associates the pattern that pushes the outer dataset (that we call outer pattern) towards the inner pattern. This association is made by searching for a matching, between the injected values of the inner pattern and the variable mappings of the outer, with the function of inverse mapping that we propose below.
Definition 3 (Inverse mapping). We define the inverse mapping as a partial, non surjective and non injective, function µ -1 : BLI → S where µ -1 = {(val, s) | val ∈ BLI, s ∈ S} such that (s, val) ∈ µ. B is considered for generalization reasons even if blank nodes cannot be used for joins between datasets. N estedLoopDetection is the most challenging heuristic of FETA because µ -1 may return more than one variable, when the same value was returned for more than one variable. This depends on the similarity of concurrent federated queries and the considered gap. Thus, some times, identifying the variable that appears in the original query is uncertain. Figure  5 , illustrates G after N estedLoopDetection for our motivating example with an infinite gap. We observe that graphs g 1 and g 2 are merged.
Experiments To the best of our knowledge, a public set of real federated queries executed over the LD does not exists, thus we evaluated FETA using the queries and the setup of FedBench  [10]. We used the collections of Cross Domain (CD) and Life Science (LS), each one has 7 federated queries. We setup 19 SPARQL endpoints using Virtuoso OpenLink 2  6. 1. 7. We executed federated queries with Anapsid 2. 7 and FedX 3. 0. We configured Anapsid to use Star Shape Grouping Multi-Endpoints (SSGM) heuristic and we disabled the cache for FedX. We captured http requests and answers from endpoints with justniffer 0. 5. 12 3. FETA is implemented in Java 1. 7 and is available at https://github. com/coumbaya/feta. The goals of the experiments are : (i) to evaluate the precision and recall of FETA with federated queries executed in isolation and (ii) to evaluate the precision and recall of FETA with federated queries executed concurrently under a worst case scenario, i.
e. , when BGPs of different federated queries cannot be distinguished as they share the same IP address. All results are available at: https://github. com/coumbaya/feta/blob/master/experiments_with_fedbench. md. To analyze traces of federated queries in isolation, we executed CD and LS collections. We captured 28 sequences of subqueries used as input for FETA one by one. In average, we obtained 94,64% of precision and 94,64% of recall of triple patterns deduction. We obtained 79,40% of precision and 87,80% of recall for joins deduction. Deducing sets of joined triple patterns, i. e. , BGPs, is more challenging. From Anapsid traces, BGPs deduced correspond to CD and LS queries, except for Union queries, i. e. , CD1, LS1 and LS2. These queries have two BGPs but a join is possible between them locally at the query engine, and FETA deduces a symmetric hash join.
Limitations of sequential pattern mining algorithms have been pointed out in process mining  [12]. Process mining algorithms recompute workflow models from logs. However, queries are not workflows and federated logs are not process logs. In a process log, events corresponds to identified tasks which is not the case in our context. The number of different subqueries can be proportional to the cardinality of the federated datasets. Moreover, in a federated log, a subquery cannot be the cause of another; in general, join ordering is decided according to the selectivity of subgoals in the original query.
Conclusions and future work Federated query tracking allows data providers to know how their datasets are used. In this paper we proposed FETA, a federated query tracking approach that reverses federated Basic Graph Patterns (BGPs) from a shared log maintained by data providers. FETA links and unlinks variables from subqueries of the federated log by applying a set of heuristics to decrypt behavior of physical join operators. Even in a worst case scenario, FETA extracts BGPs that contain original BGPs of federated queries executed with Anapsid and FedX. Extracted BGPs, annotated with endpoints, give valuable information to data providers about which triples are joined, when and by whom. We think FETA opens several interesting perspectives. First, heuristics can be improved in many ways by better using semantics of predicates and answers.
Patient surveys were administered in English, except to French patient participants who received the survey translated in French. REDCap (V. 6. 13. 3) software  18  was used for survey administration, data collection and management. Participants received an introduction about the project objectives and were asked to rate each domain on an 11-point numerical rating scale from 0, 'not important at all', to 10, 'very important'. Survey results for the patient and physician groups were separately summarised as mean, median and percentage of respondents who rated each domain in one of three categories of importance (≤3, 4-7 and ≥8). Each domain was then assigned to one of three categories reflecting levels of agreement: (A) important for both groups (>50% in each group voted the domain as ≥8); (B) important for one group but not for the other group (>50% voted the domain as ≥8 in one group and >50% voted the domain as <8 in the other); and (C) less important for both groups (>50% in both groups voted the domain as <8).
Candidate domains Twenty-four domains were obtained from the SLR of outcomes measured in PsA RCTs, LOS and registries. Measurement of the complete 2006 PsA core domain set increased in RCTs conducted 2010-2015 compared with 2006-2010. Recent RCTs frequently measured domains in addition to the core set; and among RCTs, there was great heterogeneity in outcome measure selection per domain. The focus group study consisted of 16 focus groups with 89 participants with PsA on five continents represented by six countries (number of focus groups in each country): the USA (5), Brazil (3), Australia (2), France (2), the Netherlands (2) and Singapore (2). The UK focus group study consisted of eight focus groups with 41 participants. Thirty-five domains and corresponding definitions were obtained from qualitative data analysis of international PsA focus group transcripts.
International focus group participants were sampled across PsA manifestations, disease activity and duration and socioeconomic status. This ensured that we captured broad content early on in the process of data collection and obtained domains that are generalisable to people with PsA worldwide. This study confirms that patients' and physicians' priorities are different. A core domain set with face validity should capture the diversity of stakeholder perspectives. 6 30 We demonstrated that the inclusion of patient and physician perspectives is possible using a data-driven process and ensuring awareness of both The first panel shows domains selected important* by >70% of either patients or physicians in the first international survey and patient and physician percentages. The second panel shows patient and physician percentages rating each domain as important* in the second international survey (*important was designated as receiving a rating of ≥8 on a numerical rating scale from 0, not important at all, to 10, very important).
Acknowledgements The authors kindly acknowledge  Heidi Bertheussen ,  Jeffrey Chau ,  Rodrigo Firmino ,  Jana James  and  Roland MacDonald  for their participation in the face-to-face meeting; all patients and physicians who participated in the surveys and  Yihui Jiang  for programming the international patient and physician surveys. We thank all collaborators who made possible the international focus group study:  Jessica Walsh ,  Suzanne Grieb ,  Katherine Clegg Smith ,  Lilian HD van Tuyl ,  Karen Schipper ,  Penelope Palominos ,  Ricardo Xavier ,  Tania Gudu ,  Xiao Hui Xin ,  SoumyaReddy ,  Rani Sinnathurai ,  Lyn March , and  Clifton Bingham.
The model systematizes variation in location as well as shape. Expert knowledge can be incorporated through informative priors and the results can be supervised through compact and comprehensive visualizations. BayesFlow is applied to two synthetic and two real flow cytometry data sets. For the first real data set, taken from the FlowCAP I challenge, BayesFlow does not only give a gating which would place it among the top performers in FlowCAP I for this dataset, it also gives a more consistent treatment of different samples than either manual gating or other automated gating methods. The second real data set contains replicated flow cytometry measurements of samples from healthy individuals. BayesFlow gives here cell populations with clear expression patterns and small technical intra-donor variation as compared to biological inter-donor variation.
To handle this we use multiple components to model such populations, an approach that have often been employed for flow cytometry data  [9, 12, 24, 25]  and has the further advantage that the number of cell populations can be automatically detected. We merge Gaussian components into super components with a procedure based on a systematic study of methods for merging mixture components  [26]. Results from the MCMC sampling and subsequent merging are evaluated in a number of quality tests. This is a crucial step since what is deemed as a good clustering is application dependent. In some settings a given amount of variation in location or shape is expected from biological or technical reasons, whereas in others the same variation would indicate a different population. This also means that it is necessary for the user to choose prior parameters for their application.
To simplify this process we have derived parametrizations so that the same value of the parameters gives a similar effect of the prior on data sets of different sizes. We verified the ability of the sampling scheme to recover model parameters by fitting the model to a small threedimensional synthetic data set with 1. 2 million cells in total and a large synthetic data set with in total 28 million cells in 8 dimensions. Then we applied BayesFlow to one of the datasets in the FlowCAP I challenge, the GvHD dataset, which contains samples from patients who have had organ transplants and might have early signs of graft-versus-host disease. We show that BayesFlow does not only give a result which has the same degree of accordance with manual gating as the best performing methods in FlowCAP I-which is much higher than what is obtained for other methods based on joint gating with Bayesian hierarchical models-it does also give a more similar treatment of different samples than manual gating and the best methods from FlowCAP I.