dataesr/scientific-paragraphs-categorization huggingface dataset:
An advantage of our approach is that it can also fit unknown data noise, which reduces the risk of overfitting the data. In addition, uncertainties in the source parameters can be propagated, yielding more accurate model parameter uncertainties. In this study, we first present our technique and discuss the challenges stemming from using a single station to characterize both structure and the source and from the absence of a Mars reference model. We then demonstrate the method feasibility using the Mars Structure Service blind test data and our own synthetic data, which included realistic noise levels based on the noise recorded by InSight.
Plain Language Summary In preparation for the InSight mission that landed on Mars on November 26, 2018, we adapted an algorithm developed for Earth to measure the dependence of the speed of Rayleigh waves with frequency. These waves are useful to constrain the interior structure of planets because of their ability to resolve vertical changes in elastic parameters. This is because data at lower frequencies are sensitive to deeper structure than at higher frequency, a property called dispersion. The original method to measure this dispersion was modified for Mars because of the lack of a good starting interior reference model, and because of the larger uncertainties in estimating the quake parameters with one seismic station only. Here, we explain the method, which involves searching for hundreds of thousands of interior models that can explain the seismic recording and using them to determine the wavespeed as a function of frequency.
Introduction One of the objectives of the InSight mission is to constrain Mars interior structure. Previous missions to Mars have focused on the surface characteristics of the planet by examining features like canyons, volcanoes, and rocks. However, to better understand the early formation and evolution of Mars, we need to obtain constraints on its internal structure. Several Mars seismological models have been proposed over the years based on the estimated bulk density and moment of inertia of Mars, as well as geochemical and equation of state data (e. g. Gudkova & Zharkov, 2004; Nimmo & Faul, 2013; Sohl & Spohn, 1997). There are, unfortunately, large uncertainties associated with these indirect methods and our prior knowledge of the Martian interior is still limited. Seismology remains the most direct and efficient way to probe the interior of a planet.
Mars is not as geologically active as the Earth, so the number of marsquakes with intermediate to large magnitudes is likely limited. This has been verified by the InSight SEIS data collected so far: most of the marsquakes detected during the first year of operation of SEIS have moment magnitude between 3 and 4  (Giardini et al. , 2020). Besides the limitation of the number of large enough events, other complications result from the fact that InSight only has one seismic station. Surface wave inversion techniques and receiver function analyses become thus natural choices to obtain models of Mars interior. While the first constraints on the properties of the shallow upper crust using InSight data and receiver functions were recently published  (Lognonné et al. , 2020) , surface waves have not been detected yet.
However, if they were to be observed in the future, dispersion measurements of fundamental and higher mode surface waves would provide depth constraints on the planet's internal structure, which was expected to be a primary means of constraining velocity structure by the Mars Structure Service (MSS;  Panning et al. , 2017) , and demonstrated by the MSS blind test  (Drilleau et al. , 2020). In addition, the method presented in this study to measure dispersion data, and the modifications we brought to our original code to allow for less well-constrained reference models and source parameters, are extremely important for any planetary seismology application, including the likely future missions on the moon and on Titan (Dragonfly), as well as the ones that have been proposed for other bodies like Europa, Enceladus, from balloons on Venus, and even possibly on the surface of Mercury.
XU ET AL. (right)  at periods between 5 s and 200 s. They were calculated using software package Mineos  (Masters et al. , 2011). The model of  Zheng et al. (2015)  with no low velocity zone was used as reference. In this section, we briefly summarized the framework of our original inversion method, which was described in  Xu and Beghein (2019)  in detail, and emphasized the improvements we made to adapt it to studying Mars.
Waveform Modeling Synthetic seismograms can be calculated by normal mode summation  (Dahlen, 1968) :                 exp Δ / m m m s A i c ( 1 ) where ω is the angular frequency of a mode m, c m (ω) is its phase velocity, Δ is the epicentral distance, and A m is the amplitude of the mode. Here, we used software package Mineos  (Masters et al. , 2011)  to perform these calculations for a reference 1-D model of Mars interior. Ideally, one would calculate a seismogram for every interior model generated by the rj-MCMC method using the fully nonlinear formulation of Equation 1. However, since this would mean solving the forward problem hundreds of thousands of times during one single inversion, it would be too time-consuming to calculate normal mode eigenfunctions and eigenfrequencies at each iteration of the MCMC scheme.
We found that the fundamental mode is reliably measured at all periods considered, which is identical to what is found for Earth since they have larger SNR and separate well from the higher modes. We also found, similar to Earth, that the higher modes are reliably measured at the shortest periods (<35 s in this case). Considering the sensitivity kernels for these first few higher modes (Figure  1 ), this means that the data employed are sensitive to structure down to 300-400 km depth. In Figure  15 , we plotted the dispersion curves for the reliably measured modes in addition to the dispersion curves predicted by the true model. The measurements are displayed with two standard deviations. We found that in all cases, the dispersion values for the true model are included within the 2σ uncertainties estimated with our method.
Conclusions In this study, we presented a method to invert multimode surface-wave waveforms and measure dispersion curves based on a single station-event pair. While surface waves have not yet been observed with InSight, this method and the modifications we brought to the original technique will be important for all future seismological exploration of other planetary bodies. We showed the feasibility of our method using the MSS blind data set in the first example, and our own synthetic data in a second example. We modified our original algorithm to help account for the large uncertainties in the source parameters as well as the lack of a good Martian reference model. Source parameters were included in the inversion together with the parameters describing the velocity profile in order to reduce the risk of mapping errors in the source into the models.
Institut de Physique du Globe, Paris  (http://seis-insight. eu). AH was funded by the  UK Space Agency. This InSight Contribution Number  118. We acknowledge  NASA ,  CNES , their partner agencies and Institutions (UKSA,  SSO ,  DLR ,  JPL ,  IPGP-CNRS, ETHZ, IC, MPS-MPG ) and the flight operations team at  JPL, SISMOC, MSDS, IRIS-DMC  and  PDS  for providing SEED SEIS data. Acknowledgments  C. Beghein  and  H. Xu  were funded for this research by  NASA   InSight PSP  grant # 80NSSC18K1679. M. P. P. was supported by the  NASA   InSight mission  and funds from the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ).
Participants Inclusion criteria for the Polish sample were as follow: at least typical IQ as indicated by the norms of the Raven's Progressive Matrices ( > 25 centile; Jaworowska and Szustrowa 2011 ) and Wechsler Intelligence Scale for Children ( > 70 IQ scale;  Matczak et al. 1997  ), birth at term ( > 37 weeks), right-handedness, monolingualism, no history of neurological or language impairments and good quality of the fMRI scan ( < 20% of motion-affected volumes identified with ART toolbox, see below for details). All English-speaking children who met the Polish inclusion criteria with IQ assessed with Wechsler Preschool and Primary Scale of Intelligence ( > 70 IQ scale; WPPSI-IV, 2014 ) or Wechsler Abbreviated Scale of Intelligence ( > 70 IQ scale; WASI-II, 2012 ) were included in the analysis (50 out of 82 collected datasets).
Acknowledgments This work was funded by grants from the  Polish Ministry of Science and Higher Education  (  IP2011 020271  ), the  National Science Center  (  2014/N/HS6/03515  ,  2011/03/D/HS6/05584  ),  Eunice Kennedy Shriver National Institute of Child Health and Human Development  (  P01 HD 001994  ,  P01 HD 070837  ) and  National Institutes of Health  (  5R01HD086168-04  ,  5R37HD090153-03  ). The project was realized with the aid of CePT research infrastructure purchased with funds from the  European Regional Development Fund  as part of the  Innovative Economy Operational Programme , 2007-2013. Funding sources were not involved in the experiment realization, data collection, data analysis, or writing of the report. The authors would like to thank all the families which participated in this study.
However, due to the low photon counts in the narrow energy bin and the inhomogeneous response problem of detector cells, there are severe noise and ring artifacts in reconstructed spectral CT images. In this study, a mouse specimen was scanned by a spectral CT scanner with a photon-counting detector, and the mouse CT images were reconstructed from the projection dataset. The CT images were used to train the network. Experimental results demonstrated that the proposed method could reduce noise and suppress ring artifacts of spectral CT images concurrently in different energy ranges, and the FCPRN outperforms the competing networks for CT image denoising in this study. In the article ''Material decomposition in spectral CT using deep learning: A Sim2Real transfer approach,'' by Abascal et al.
, a deep learning strategy was proposed for solving the material decomposition problem based on a U-Net architecture and a Sim2Real transfer learning approach, where the knowledge learned from synthetic data was transferred to a real-world scenario. In order for this approach to work, synthetic data must be realistic and representative of the experimental data. For this purpose, numerical phantoms were generated from human CT volumes of the KiTS19 Challenge dataset, segmented into specific materials (soft tissue and bone). These volumes were projected into Radon space to simulate photon-counting data, taking into account the energy response of the scanner. The authors compared projection-and image-based decomposition approaches where the network was trained to decompose the materials either in the projection or in the image domain.
The results on both simulation and real data demonstrated the effectiveness of the proposed method, in terms of both image reconstruction quality and material decomposition accuracy. The Guest Editors of this Special Section express their gratitude to the authors for their contributions, the volunteering referees for their dedication, and the whole IEEE ACCESS Editorial Staff for their invaluable support. The Guest Editors believe that this Special Section will inspire new research interest to solve challenging problems in MECT imaging and its applications in the near future, particularly the machine learning techniques.
• Clusters containing more than six He atoms are assumed to be immobile (i. e. D i = 0 for i > 6 ) due to trap mutation events. This assumption is motivated by DFT and MD results suggesting that the self-trapping energy is below the binding energy of one He atom in a pure He cluster for clusters containing more than five He atoms  3. For smaller clusters ( He 1 , He 2 ,. , He 6 ) the diffusion coefficient and the dissociation by He emission energy vary with the number of He atoms in the cluster (see Table  1 ). This He transport model was implemented in Python and solved using the finite element solving platform FEniCS  37. All plots in this work were generated with Matplotlib  38.
Bubbles were observed up to 100 nm with larger bubbles closer to the surface and smaller bubbles deeper in the bulk. Open bubbles and holes at the surface were also observed suggesting bursting events occurred. This is in accordance with what was observed in the simulations (see Fig. 4 ). A procedure was developed to automate the bubble detection on TEM images using the ImageJ software  45. The area of bubbles were computed as well as their diameter assuming a spherical shape for the bubbles. Bubble density and size as a function of depth was therefore computed using 12 pairs of under-and over-focused TEM images. The bubble density was found to range from 7 × 10 19 m -3 to 2 × 10 20 m -3 and the bubble radius ranged between 1 nm and 10 nm (see Fig. 11 ). Although the resolution of the TEM is below 1 nm, the number of bubbles with radius below 2 nm is underestimated due to the limited contrast.
Acknowledgements The project leading to this publication has received funding from  Excellence Initiative of Aix-Marseille University-A*Midex , a  French "Investissements d' Avenir" programme as well  as from the  French National Research Agency  (Grant No. ANR-18-CE05-0012 ). Work performed under EUROfusion WP PFC. This work has been carried out within the framework of the  EUROfusion consortium  and has received funding from the  Euratom.
Consequently, learning from individual sensor data fails to identify the micro-environment. This classification might be improved by combining multiple sensor data. However, this is not straightforward due to the complexity and interdependence of such multi-dimensional time series  [3]. This task is more or less related to human activity recognition, where there exists a long established research, and a wide range of applications. Different types of data are used ranging from GPS only, to one or many accelerometers, sound or combinations. An attempt to work with environmental data was introduced by Asimina et al. [2]  where the authors explore the capability of predicting location from sensors data using an Artificial Neural Network (ANN) model. The authors use low-cost individual sensors and GPS for data collection, and provide a location predictive model based on Artificial Neural Network (ANN) to derive the time-space activity daily profile of the individuals.
MULTI-VIEW LEARNING Our multi-view model is based on the idea of multi-view stacking from  [5]  and we adapted it to best fit our needs. As we suggest in  [1] , to learn the micro-environment of participants from multi-variate time series, we propose a two-stage model based on multi-view learning. Figure  2  shows our multi-view learning architecture. It consists of two steps multivariate time series classification while preserving the properties of each source of data. A first-level learner is used for each view independently, then the results of the first-level classification are used to generate a D' dataset having feature vector which contains the prediction and its corresponding probability for each class as shown in Table  1  to train the meta-learner. Assuming that   is a dimension of the n-dimenstional time series   = ( 1 ,  2 ,.
DEMONSTRATION SCENARIO In this section, we illustrates the user interaction with COMIC interface. The experiments are carried out on different environments. The multi-view learning model was implemented in Python 3. 6 using scikit-learn 0. 23. 2 and tslearn  [9]. The deep-learning model MLSTM-FCN  [7]  was trained using Keras 2. 2. 4. Our GUI (graphical user interface) was implemented using python 3. 6, Plotly 2  , and Dash 3  framework. A real-world environmental data collected in the context of Polluscope project is used as a benchmark of environmental crowdsensing data. In this context, participant collect air quality measurements (NO2, PM1. 0, PM2. 5, PM10, BC, Temperature, Humidity) plus GPS locations which are used to derive participants speed. The recruited participants where given a mobile app in order to annotate their micro-environments whenever it changes.
ACKNOWLEDGMENTS This work has supported by the  French National Research Agency (ANR)  project  Polluscope , funded under the grant agreement  ANR-15-CE22-0018 , by the  H2020 EU GO GREEN ROUTES  funded under the research and innovation programme  H2020-EU. 3. 5. 2  grant agreement No  869764 , and by the  DATAIA convergence institute project StreamOps , as part of the  Programme d' Investissement d'Avenir ,  ANR-17-CONV-0003. Part of the equipment was funded by  iDEX Paris-Saclay , in the framework of the  IRS  project  ACE-ICSEN , and by the  Communauté d'agglomération Versailles Grand Parc. We are thankful to all the members of the Polluscope consortia who contributed in one way or another to this work:  Salim Srairi  and  Jean-Marc Naude  ( CEREMA ) who conducted the campaign;  Boris Dessimond  and  Isabella Annesi-Maesano  ( Sorbonne University ) for their contribution to the campaign;  Valerie Gros ,  Baptiste Languille  and  Nicolas Bonnaire (LSCE) , and  Anne Kauffmann  and  Christophe Debert (Airparif)  for their contribution in the periodic qualification of the sensors and their feedback.
When two or more licensed datasets participate in evaluating a federated query, to be reusable, the query result must be protected by a license compliant with each license of the involved datasets. Due to incompatibilities or contradictions among licenses, such a license does not always exist, leading to a query result that cannot be licensed nor reused on a legal basis. We propose to deal with this issue during the federated query processing by dynamically discarding datasets of conflicting licenses. However, this solution may generate an empty query result. To face this problem, we use query relaxation techniques. Our problem statement is, given a SPARQL query and a federation of licensed datasets, how to guarantee a relevant and non-empty query result whose license is compliant with each license of involved datasets? To detect and prevent license conflicts, we propose FLiQue, a license-aware query processing strategy for federated query engines.
Introduction and Motivation The Linked Data is a network of distributed and interlinked data sources. Federated query processing allows to query such a network of live and up-to-date datasets. A federated SPARQL query can retrieve information from several RDF data sources distributed across the Linked Data. Since the beginning of Linked Data, licensing has been an important issue  [1, 36]. To legally facilitate reuse, data owners should systematically associate licenses with resources before sharing or publishing them. There are still several open issues to legally access and reuse linked data, as shown in recent Dagstuhl seminars  [2, 12]  and surveys  [14]. When two or more licensed datasets participate in evaluating of a federated query, the query result must be protected by a license that is compliant with each license of involved datasets.
Licenses specify precisely the conditions of reuse of data, i. e. , what actions are permitted, obliged, and prohibited. Machine-readable licenses are necessary to ensure automatic license compliance. The W3C Open Digital Rights Language (ODRL)  [17]  allows defining machine-readable licenses. The Data Licenses Clearance Center (DALICC)  [25] , proposes a library of wellknown standard machine-readable licenses. We consider that a license l j is compliant with a license l i if a resource licensed under l i can be licensed under l j without violating l i. If l j is compliant with l i , then l i is compatible with l j. Unfortunately, it is not always possible to find a license compliant with each license of datasets involved in a federated query  [22]. If such a license does not exist, the query result cannot be licensed and, thus, should not be reused nor published.
We consider that a query whose result set cannot be licensed should not be executed. Notice that having the rights to query several datasets individually does not mean having the rights to execute a federated query involving these datasets. Consider datasets of LargeRDFBench  [32] , a benchmark for federated query processing. Figure  1  shows the compatibility graph of licenses 3 that protect Larg-eRDFBench datasets. By transitivity, license CC BY is compatible with itself, with CC BY-SA, CC BY-NC, and CC BY-NC-SA. Thus, datasets protected by CC BY can be queried along with other datasets protected by these licenses. However, the whole set of datasets of Figure  1  cannot be queried together because there is no license compliant with the fourth licenses. For instance, there is no license with which CC BY-SA and CC BY-NC-SA are both compatible.
CC BY KEGG One solution to the incompatibility of licenses is negotiating with data providers to change a conflicting license, e. g. , to ask DBpedia to change its license to CC BY or CC BY-NC. Nevertheless, negotiation takes time and is not always possible. A second solution is to discard datasets that are protected by conflicting licenses. However, this solution can lead to a query with an empty result set. To face this problem, we use query relaxation techniques. That is, we use relaxation rules to relax the query constraints to match triples of other datasets. Fig. 2 : Compatibility graph of licenses for datasets D1, D2, and D3. As there is no license compatible with licenses of these datasets, sub-federations F1 and F2 should be created. Queries (or relaxed queries) can be evaluated over these sub-federations to produce licensable results.
SELECT ? s t u d e n t WHERE { ? s t u d e n t r d f : t y p e ex : S t u d e n t. #tp1@{D3} CC BY-NC ? s t u d e n t ex : e n r o l l e d I n ? c o u r s e. #tp2@{D3} CC BY-NC ? c o u r s e ex : h e l d A t ex : U n i v e r s i t y O f N a n t e s. #tp3@{D1} CC BY ex : Ben ex : t e a c h e s ? c o u r s e. #tp4@{D2} CC BY-SA } Listing 1. 1: A SPARQL query Q. Consider the compatibility graph of licenses for datasets D1, D2, and D3 of Figure  2  4 and the federated query Q of Listing 1. 1, which asks for students enrolled in a course held at the University of Nantes taught by ex:Ben. The query is annotated with the datasets over which each triple pattern can be evaluated. D2 and D3 should not be queried together because their licenses are respectively CC BY-SA and CC BY-NC, and there is no license compliant with both.
Thus, the result set of the query cannot be licensed. Creating a sub-federation of sources with compatible licenses without D2 makes the result set licensable with CC BY-NC or with another license compliant with CC BY-NC (e. g. , CC BY-NC-SA). The problem is that the query gives no result because there is no more dataset to evaluate tp4. The case is similar with a sub-federation with compatible licenses discarding D3 because there is no more dataset to evaluate tp1 and tp2. As none subset of licence compatible sources can produce a non-empty result set for Q, we propose using query relaxation techniques. For instance, in such relaxation, instead of asking for students in tp1, the federated query could ask for persons, or instead of asking for courses taught by ex:Ben in tp4, the federated query could ask for courses taught by anybody.