dataesr/scientific-paragraphs-categorization huggingface dataset:
An efficient algorithm for solving such problems is provided by the software zsolve available via  [24]. Note that, in order to have a faster program, we first try to minimize the value of d by finding a point v ∈ R n which is near to x, though not necessarily the nearest, by small coordinate changes. Another algorithm for which zsolve is useful is when we want to test that a given polytope P is a V N -space: Input: a polyhedral metric L and a polytope P Output: If P is a V N -space return true and otherwise a certificate that it is not. E ← set of vertices of P. c ← isobarycenter of E. v 0 ← nearest point to c. 0 ← the form ∈ L realizing the maximum of (c -v 0 ). if the inequalities (x -v 0 ) ≤ 0 (x -v 0 ) are not valid on P then return a and x ∈ P satisfying (x The idea of this algorithm is that we take upper bound on possible values of 0 (x) -(x) which gives a potentially larger polytope.
Each of the three satellites of the European Space Agency Swarm mission carries an absolute scalar magnetometer (ASM) that provides the nominal 1-Hz scalar data of the mission for both science and calibration purposes. These ASM instruments, however, also deliver autonomous 1-Hz experimental vector data. Here, we report on how ASM-only scalar and vector data from the Alpha and Bravo satellites between November 29, 2013 (a week after launch) and September 25, 2014 (for on-time delivery of the model on October 1, 2014) could be used to build a very valuable candidate model for the 2015. 0 International Geomagnetic Reference Field (IGRF). A parent model was first computed, describing the geomagnetic field of internal origin up to degree and order 40 in a spherical harmonic representation and including a constant secular variation up to degree and order 8.
This model was next simply forwarded to epoch 2015. 0 and truncated at degree and order 13. The resulting ASM-only 2015. 0 IGRF candidate model is compared to analogous models derived from the mission's nominal data and to the now-published final 2015. 0 IGRF model. Differences among models mainly highlight uncertainties enhanced by the limited geographical distribution of the selected data set (essentially due to a lack of availability of data at high northern latitude satisfying nighttime conditions at the end of the time period considered). These appear to be comparable to differences classically observed among IGRF candidate models. These positive results led the ASM-only 2015. 0 IGRF candidate model to contribute to the construction of the final 2015. 0 IGRF model.
The latest call, issued in May 2014, requested candidate models for the 2015. 0 IGRF model and for the 2015. 0-2020. 0 predictive IGRF secular variation model. As is standard practice, it also called for candidate models for the a posteriori 2010. 0 Definitive Geomagnetic Reference Field (DGRF) model, meant to replace the 2010. 0 IGRF model provided with the previous 2010 IGRF release  (Finlay et al. 2010). All candidate models were requested by the deadline of October 1, 2014. The present paper reports on the production of a 2015. 0 IGRF candidate model in response to this call. This model was built from a unique set of data acquired by absolute scalar magnetometers (ASM) running an experimental vector mode on board the satellites of the European Space Agency (ESA) Swarm mission. For reasons later explained, no candidate predictive IGRF secular variation model for the interval 2015.
For the purpose of the present study, calibration was done on a daily basis, and the CCDB updated accordingly, using a 3-day sliding window scheme (parameters for the CCDB of a given day being optimized by minimizing residuals over the day of interest, the previous day, and the day after; see  Léger et al. (2015) ). This led to residuals with no bias (since the same sensor is used to infer both the scalar and vector components) and a noise level of σ = 2. 3, 2. 4, and 4. 5 nT for, respectively, Alpha, Bravo, and Charlie (one standard deviation, before any outlier rejection or data selection; see  Léger et al. (2015) ). Using these pseudo-daily CCDBs, calibrated vector data could thus be produced every second at t ASM instrument's time stamps. However, these data (and the ASM scalar data) still had to be corrected for satellite stray fields.
They also had to be synchronized to the t UTC times used in all nominal L1b files (using a cubic B-spline interpolation method). The information needed for this was taken from nominal L1b files provided by ESA. Applying these corrections and synchronization then led to 1-Hz calibrated and corrected ASM vector data in the ASM frame, together with ASM scalar data, fully synchronized with all official L1b data. These ASM-V vector data, provided in the ASM instrument frame, are formally analogous to the official L1b calibrated VFM data provided in the VFM instrument frame. They can be used in exactly the same formal way for modeling purposes. The ASM scalar data produced in the process can also be compared to the ASM scalar data stored in official L1b data files. Differences are very small (root mean square (rms) of 30 pT) and simply testify for the slightly different way strayfield corrections and synchronizations are implemented in the two (ours and ESA's) processing chains.
Data selection Only data from the two Alpha and Bravo satellites were considered, the ASM-V data produced on Charlie being significantly noisier (recall above). This did not turn out to be a major limitation for the purpose of the present study, since Charlie is anyway orbiting very close to Alpha compared to the length scales of the main field to be modeled (note that having Charlie close to Alpha can nevertheless be taken advantage of for modeling the high-degree field, using the scalar ASM and nominal L1b vector data on Charlie, as illustrated in, e. g. ,  Olsen et al. (2015) ). The data selected extended from November 29, 2013 (1 week after launch) to September 25, 2014 (1 week before the deadline for the delivery of the IGRF candidate models). Some data were manually removed in an ad hoc manner, based on early inspection of the ASM-V data (early in the mission, to avoid using data at times of known or suspected issues either obviously or potentially affecting these data, such as satellite manoeuvres): between 27/01/2014 and 06/02/2014 (inclusive) for Alpha, and on 05/12/13 between 09:36 and 12:00, plus between 08/12/2013 and 17/12/2013 (inclusive) for Bravo.
e. , not obviously inconsistent) official L1b datum was also available at exactly the same time on the same satellite, with vector field components within 500 nT (and scalar field within 100 nT) of predictions from the CHAOS-4 model of  Olsen et al. (2014) , up to degree and order 20, linearly forwarded to the appropriate epoch (starting from epoch 2013. 5 and using the model's SV computed at epoch 2013. 5). Since this was implemented, two additional data sets could also be assembled. One, which simply used nominal L1b VFM vector data in the VFM instrument frame (release 0302 when available, otherwise release 0301) in place of the ASM-V data; the other, built in the same way, except that each L1b VFM vector datum (still in the VFM instrument frame) was systematically normalized by the synchronous ASM scalar datum to ensure an exact match of its norm with this scalar datum.
Both these data sets, to which we will refer as the VFM and normalized-VFM data sets, respectively, were thus constructed to include the same amount of data and be completely synchronous with our experimental ASM-V data set. Figure  2  provides the altitude of the Alpha and Bravo satellites during the period modeled and the coverage of the selected data as a function of latitude and time. Note how critical the latest data were to avoid a dangerously short temporal coverage of the Northern latitudes (an issue entirely related to the dark region selection criteria and not to the ASM instrument itself ). In all, we used 3 × 65,169 vector and 66,454 scalar data from the Alpha satellite and 3 × 74,123 vector and 67,095 scalar data from the Bravo satellite. This made a total of 551,425 data. This, we note, is about a factor 2 less than the nearly 10 6 data used in the more advanced model recently produced by  Hulot et al.
Note, in particular, that no regularization was applied. A sin(θ) weight was introduced (where θ is the geographic co-latitude), to balance the geographical sampling of data. In all computations, a priori data error variances were otherwise set to 2. 5 nT for both scalar and vector data, also assuming an isotropic attitude error of 10 arcsecs (using the formalism of  Holme and Bloxham (1996) ). Due to both scalar data and Huber weights being involved, the cost function depends nonlinearly on the model parameters, and the solution was obtained iteratively, using a Newton-type Applying the methodology described above to the selected ASM-V data led to the production of an ASM-V parent model. This parent model was next forwarded to epoch 2015. 0, using its own secular variation (up to degree and order 8) and truncated at degree and order 13.
This led to the ASM-V 2015. 0 IGRF candidate model, which we delivered on October 1, 2014, as requested by the call. Useful auxiliary information was also provided to the task force in the form of additional files. This extra information included the Gauss coefficients of the SV model associated with the ASM-V parent model, which could have been proposed as a candidate IGRF SV model for the interval 2015. 0-2020. 0 but was eventually provided as an ASM-V 2015-2020 SV test model. Indeed, rules of the call for candidate models requested leading institutions to propose no more than one candidate for the same IGRF type of model, and it turned out that IPGP was also leader in the production of another predictive IGRF secular variation candidate model (using a data assimilation type of strategy,  Fournier et al.
(2015) ; see also  Fournier et al. (2010) ). For both the ASM-V 2015. 0 IGRF candidate and ASM-V 2015-2020 SV test models, simple uncertainty estimates were also provided. These were computed by just splitting the original data set into two sub-data sets. Numbering days from first to last day of available data, all odd number days were put in one sub-data set, all even days in the second one. We checked that this indeed leads to very similar data distributions in both sub-data sets. These sub-data sets were then used to compute two sub-models using exactly the same procedure and parameters as when using the full data set. These two sub-models (indexes 1 and 2, using index 0 to refer to the original model) were then used to infer a rough σ estimate for each Gauss coefficient in the following way: σ g m n À Á ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1=2 g m n;1 -g m n;0 2 þ g m n;2 -g m n;0 2 s ð1Þ and likewise for the h m n coefficients and SV Gauss coefficients.
A number of useful auxiliary models were also built and provided. For this, we used the L1b VFM and normalized-VFM data sets in place of the ASM-V data set but otherwise relied on the same modeling procedure (using exactly the same choice of parameters). Models built in this way from the VFM data set are later referred to as the VFM parent, VFM 2015. 0, and VFM 2015-2020 SV auxiliary models (analogous to the ASM-V parent, ASM-V 2015. 0 IGRF candidate, and ASM-V 2015-2020 SV test models, respectively), while those built from the normalized-VFM data set are later referred to as the N-VFM parent, N-VFM 2015. 0, and N-VFM 2015-2020 SV auxiliary models.
At lower latitudes, however, this result is less obvious, since the models are also constrained by their respective ASM-V or VFM vector data. The impact of using ASM-V vector data (rather than VFM vector data) at low latitudes is more clearly found in the geocentric components B r , B θ , and B ϕ. Indeed, the residuals of the ASM-V parent model with respect to these components are systematically larger than those of the VFM and N-VFM parent model. The increase, however, is quite small (by 12 to 34 % depending on the misfit, the largest increase being observed on B r ), considering that the noise of the 1-Hz ASM-V vector components is less than 2 nT rms (see  Léger et al. (2015) ) and that the ASM is sitting on a boom much further away from the STR than the VFM, in less stable mechanical conditions (recall Fig.
Comparing our candidate 2015. 0 and 2015-2020 SV test models with analogous models based on the mission's nominal L1b VFM data showed that the greatest discrepancies were to be found at high northern latitudes. Not surprisingly, these are also the latitudes were our models displayed the greatest disagreement with the final official 2015. 0 and 2015-2020 SV IGRF models. At other latitudes, however, the agreement was much better, in fact very comparable to the type of disagreements found in most of the IGRF candidate models (see  Thébault et al. (2015b) ). This is quite remarkable, given that all other IGRF candidates heavily relied on the nominal L1b data of the Swarm mission or on ground data and that our model was the only one entirely and only relying on ASM-V data. These very nice performances led our ASM-V 2015.
Acknowledgements The authors thank  Chris Finlay  for kindly providing the RC index needed for this study and two anonymous reviewers for constructive comments. They also thank the  ESA Swarm  project team for their collaboration in making ASM-V experimental data acquisition possible. They finally gratefully acknowledge support from the  Centre National d'Etudes Spatiales (CNES)  within the context of the " Travaux préparatoires et exploitation de la mission SWARM " project and from the  European Space Agency (ESA)  through  ESTEC  contract number  4000109587/13/I-NB "SWARM ESL". All Swarm L1b  data are freely available from ESA at http://earth. esa. int/swarm. Experimental ASM-V data used for the present study are available from the corresponding author, subject to approval by  CNES  and  CEA-Léti.
Patient Characteristics and Measurements Human lung tissue was obtained from 6 patients with iPAH who underwent lung transplantation at the Universitaire Ziekenhuizen Leuven, Leuven, Belgium. Table  I  in the online-only Data Supplement reports their hemodynamic and clinical characteristics. The collection protocol was approved by the Institutional Ethics Committee of the University Hospital of Leuven, and all participants gave written informed consent. Control lung tissue was obtained from 6 patients undergoing lung resection surgery for localized lung tumors at the Institut Mutualiste Montsouris (Paris, France). The control subjects had forced expiratory volume in the first second of expiration/forced vital capacity values >70%; none of the patients with iPAH or control subjects had chronic cardiovascular, hepatic, or renal disease or a history of cancer chemotherapy.
Biological Measurements in Mouse Tissues and Cultured PA-SMCs Western blotting was used to detect and quantify TERT, p53, and p21 in mouse tissues and PA-SMCs. The antibodies used were anti-TERT (Santa-Cruz Biotechnology, Nanterre, France), anti-p53 (Cell Signaling Technology, Boston, MA), and monoclonal anti-p21Waf1/ Cip1 (Cell Signaling Technology) antibodies. Levels of TERT, p21, and Bax mRNAs in lung tissue were determined by use of a real-time quantitative polymerase chain reaction assay (PCR). Total mRNA was extracted from tissues with the RNeasy Mini Kit (Qiagen, ZA Courtaboeuf, France). Real-time quantitative PCR was performed in a 7900HT real-time PCR system (Applied Biosystems, ZA Courtaboeuf, France), using SYBR Green Mix (Invitrogen) as described previously. 20 The amounts of mRNA were normalized for the amount of the control gene, 18s, which was chosen among 4 tested housekeeping genes on the basis of its stable expression in all samples (data not shown).
Experimental Prior to beamline installation, all synchrotron optics are characterized and optimized in the DLS Metrology cleanroom laboratory using a suite of instruments, including the Diamond-NOM: a non-contact autocollimator-based slopemeasuring profiler  (Alcock et al. , 2010). The next-generation bimorph mirror was mounted and aligned in a face-upward geometry on the Diamond-NOM, and automated scans were synchronized with application of piezo voltages using EPICS and Python scripts.
5 Ambrym earthquake which occurred in the southern part of the central Vanuatu back arc thrust and generated   (Pelletier et al. , 2011). Insert shows land in grey and reef types as barrier reef (orange), fringing reef (yellow) and patch reef (pink) from the Reef Base database (http://www. reefbase. org). a large tsunami in Vanuatu  (Ioualalen et al. , 2006; Pelletier et al. , 2000b; Regnier et al. , 2003).
Wallis and Futuna The French territory of W&F, in the SW Pacific, consists of the Wallis and Horn Archipelagos approximately 240 km apart (Fig. 1 ). Despite their relative proximity, the geodynamic settings of these two archipelagos differ significantly. Wallis Archipelago (176 • 10 W, 13 • 18 S) is located approximately 480 km west of Samoa. It consists of the ca. 70 km 2 island of Uvéa which rises up to 115 m above mean sea level (a. s. l. ) and is surrounded by a lagoon delimited by a barrier reef (Fig. 3 ). The lagoon is deepest to the SE, off the town of Mata'Utu, and reaches depths of 35 m with average depth of ca. 20 m. A number of small islands (approx-   IGN (2007a). The fringing reef (dark grey) is from the Reef Base database. The shaded high-resolution seafloor topography of the seaward reef wall is from the WALFUT survey  (Pelletier et al.
The tsunami inundation height (MWE and run-up) and penetration inland (inundation distance) depend on coastal bathymetry, topography above the shoreline and human modification. Hence, physical parameters of the rupturing fault, ocean floor topography (bathymetry) and land topography are critically important for generating meaningful numerical tsunami modelling. Here, we use the Gerris hydrodynamic software (see http: //gfs. sourceforge. net, Popinet, 2003 Popinet, , 2011) )  for modelling tsunami initiation, propagation and inundation. The code was run from a standard single-processor desktop computer. The initial wave elevation is obtained from a source model derived using seismic data. The simulation domain covers a significant part of the NW Pacific. Gerris uses adaptive mesh refinement whereby the spatial resolution is adapted dynamically from a maximum of 250 m in flooded areas and 250 km for areas at rest, and where wave fronts are tracked at a resolution of 1.
Land topography and lagoon bathymetry While regional seafloor topography is acceptable in deep oceanic environments, the best possible accuracy is required in the coastal zone for numerical modelling of tsunami propagation. For this study, particular attention was devoted to generating integrated bathymetry -topography Digital Terrain Models (DTM) for Wallis and for Futuna-Alofi. The onshore DTMs for Wallis combines a variety of existing topographic  (IGN, 2007a, b)  and satellite derived data, with data collected using real time kinematic GPS and processed for this project (see Supplement). In Futuna and Alofi, we digitised the 0, 5 and 10 m contour lines from the 1 : 25 000 topographic maps (IGN, 2007a) to generate the onshore DTM. Seaward of the coast of Wallis and Futuna, we used the bathymetry of the Pacific Basin from the GEBCO data set.
Trans-pacific sources All tele-earthquake sources are located along the Pacific Rim (Fig. 5 ) in the circum-Pacific deep subduction trenches of Japan, the Kurils, Aleutian, Cascadia, Peru and Chile, where earthquake magnitudes greater than M W = 8. 5 have been recorded (Table  1 ). All earthquake fault parameters are overestimates of data from historical earthquakes retrieved from  the USGS (http://earthquake. usgs. gov) and NOAA databases (http://www. ngdc. noaa. gov). This is required to model the maximum credible magnitude for that fault. Six scenarios (Sc. 1 to Sc. 6) were generated for trans-Pacific tsunamis, with sources located in Japan, the Kurils, the Aleutian-Alaska, Cascadia, Peru and Chile (Table  4 ). For earthquake sources at offshore Japan (Sc. 1), fault parameters are based on the 11 March 2011 Japan event.
The issue is of dramatic importance for low lying islands such as a number of Pacific atolls  (Orpin et al. , 2015)  where the highest elevation is often more than 5 m above mean sea level. The elevation data we acquired for the coastal fringe in Wallis enabled us to calibrate the DTM and strengthen our models. This was not done in Futuna, but there the topography with a narrow coastal fringe abuts steep cliffs makes it easier to devise escape routes. Ideally the very shallow (intertidal to 10 m depth) bathymetry should be acquired using a specific technique such as LIDAR. Once acquired the coastal topography and lagoon bathymetry should be merged into a single data set. Assessing the uncertainty associated with the likelihood of occurrence of the tsunamis would require to develop probability of occurrence of the generating earthquakes, which is beyond the scope of this paper.
Conclusions This paper presents 15 numerical models of tsunamis based on scenarios developed using the Gerris hydrodynamic software  (Popinet, 2011)  for modelling tsunami initiation, propagation and inundation. We used the best bathymetric and topographic information available for Futuna, whereas for Wallis we were able to acquire land elevation data using GPS RTK technology and derived the lagoon depth from satellite imagery. The tsunami sources were circum-Pacific fault sources which parameters were drawn from the published literature and are considered to be worst-case scenarios. The results are the first numerical models of tsunami generation and inundation run for W&F and provide an overall assessment of the potential impact of tsunami for these isolated islands. The work demonstrates that the hazard associated with tsunami is significant for W&F.
Acknowledgements. This work has benefited from financial support from the  Pacific Fund of the French Ministry of Foreign Affairs , and the European Development funds channelled through  SPC/SOPAC. Mr Enelio Liaufau  from the  Direction de l'Environnement in Wallis and Futuna was extremely supportive of our projects and provided excellent assistance during field investigations. L'Administration Supérieure des îles Wallis et Futuna  supported our project and facilitated the funding from  SPC-SOPAC. Land owners in both Wallis and Futuna were understanding of the needs of our research and enabled us to access their land for trenching. The bathymetric data around Wallis and Futuna acquired during the 1999 and 2000 cruises and WALFUT survey in 2011 were provided by  IRD. Two anonymous reviewers and Guest Editor Hélène Hébert are thanked for their very constructive comments and suggestions for improvements on the manuscript.
At the end, the list of medications administered for the stroke patient was requested to further confirm the case. Interviews were done in the presence of two interviewers (a pharmacist and a nurse) to better ensure the quality of the interview. Data entry was performed by two persons unaware of the study objectives. A total of 50 questionnaires were checked for errors by a third person and the error rate was <1%; thus, the entry was considered adequate. Statistical analysis was performed using SPSS software, V. 18. 0. A weighting was performed according to the number of inhabitants by age group and sex in each governorate, as described by the official 2005 estimates (local agestandardization), to obtain representative results of the Lebanese population. We also performed an age standardization to the WHO standard world population estimates for 2000-2025  [25]  to allow comparisons with other countries.