dataesr/scientific-paragraphs-categorization huggingface dataset:
Most of the data for this paper comes from the Cycle 17 Hubble Space Telescope (HST) proposal "The Disks, Accretion, and Outflows (DAO) of T Tau stars" (PI G. Herczeg, Prop. ID HST-  GO-11616). The DAO program is the largest and most sensitive high resolution spectroscopic survey of young stars in the UV ever undertaken and as such it provides a rich source of information for these objects. The program is described in more detail in  Herczeg et al. (2013). We have complemented the DAO data with GTO data from HST programs 11533 and 12036 (PI J. Green -BP Tau, DF Tau, RU Lup, V4046 Sgr) as well as UV spectra from the literature and from the Mikulski Archive at STScI (MAST). As shown below, there is a wide diversity of profiles in all lines for the stars in our sample. The spectra are rich and a single paper cannot do justice to their variety nor to all the physical mechanisms that likely contribute to their formation.
OBSERVATIONS Tables 1, 2, and 3 list the 35 stars we will be analyzing in this paper and the references for all the ancillary data we consider. Table  4  indicates the origin of the data (DAO or some other project), the datasets and slit sizes used for the observations. Details about exposure times will appear in  Herczeg et al. (2013). The data considered here encompass most of the published high resolution HST observations of the C iv doublet lines for CTTSs. Ardila et al. (2002)  provides references to additional high-resolution C iv data for CTTSs obtained with the Goddard High Resolution Spectrograph (GHRS ). We do not re-analyze those spectra here, but they provide additional context to our paper. Non-DAO STIS data were downloaded from the HST STIS Echelle Spectral Catalog of Stars (StarCAT,  Ayres 2010).
Non-DAO COS data were downloaded from the Mikulski Archive for Space Telescopes (MAST) and reduced as described below. The sample of stars considered here includes objects with spectral types ranging from A7 (the Herbig Ae star DX Cha 18  ) to M2, although most objects have mid-K spectral types. We assume stellar ages and distances to be ∼2 Myr and 140 pc, respectively, for Taurus-Aurigae (see  Loinard et al. 2007  and references therein), ∼2 Myr and 150 pc for Lupus I  (Comerón et al. 2009 ), ∼5 Myr and 145 pc for Upper-Scorpius (see  Alencar et al. 2003  and references therein), ∼5 Myr and 114 pc for the ǫ Chamaeleontis cluster (see  Lyo et al. 2008  and references therein), ∼5 Myr and 160 pc for Chamaeleon I (see  Hussain et al. 2009  and references therein), ∼8 Myr and 97 pc for the η Chamaeleontis cluster  (Mamajek et al.
2007 ). The accretion rates listed in Table 3 are obtained from optical veiling and NUV excesses and represent total accretion for the overall system. At the end of this paper we will conclude that binarity may be affecting the line centroid determination only in AK Sco. For non-spectroscopic binaries, the effect of the binarity may be relevant only if both companions are within the COS or STIS apertures. This is the case for DF Tau, RW Aur A, and the WTTSs EG Cha, V397 Aur, and V410 Tau. For DF Tau and RW Aur A, the primary component dominates the FUV emission (see  Herczeg et al. 2006; Alencar et al. 2005). Table  3  lists the accretion rate, obtained from literature sources. For some stars, the DAO dataset contains simultaneous NUV and FUV observations, obtained during the same HST visit.
5" diameter circular aperture. The acquisition observations used the ACQ/SEARCH algorithm followed by ACQ/IMAGE  (Dixon 2011). The absolute wavelength scale accuracy is ∼ 15 km s -1 (1σ), where the error is dominated by pointing errors. We obtained one dimensional, co-added spectra using the COS calibration pipeline (CALCOS) with alignment and co-addition obtained using the IDL routines described by  Danforth et al. (2010). • STIS observations: Those targets that are too bright to be observed by COS were observed using the STIS E140M echelle grating, which has a spectral resolution of ∆v ∼ 7 km s -1 (R∼45,000), over the region 1150 -1700 Å. All the observations were obtained using the 0. 2"×0. 2" "photometric" aperture, during two HST orbits. The STIS FUV spectra were calibrated and the echelle orders co-added to provide a single spectrum using the IDL software package developed for the StarCAT project  (Ayres 2010).
This reduction procedure provides a wavelength-scale accuracy of 1σ = 3 km s -1. Non-DAO STIS data were taken directly from StarCAT. For a given pointing, errors in the positioning of the target within the aperture result in an offset in the wavelength scale. As indicated above, these are supposed to be 15 km s -1 for COS and 3 km s -1 for STIS, according to the instrument observing manuals  (Dixon 2011; Ely 2011). In addition, the geometric correction necessary to account for the curvature of the COS FUV detector may make longer wavelength features appear redder than they really are. The offset depends on the exact position of the target on the detector. In most cases this intra-spectrum wavelength uncertainty is <10 km s -1 , but in a few extreme cases it will introduce a ∼15 km s -1 shift from the reddest to the bluest wavelengths in a single COS grating mode (See Figure  4  from  Linsky et al.
Unlike the results reported by  Linsky et al. 2012) , we do not observe a systematic increase in the line center as a function of wavelength, neither star by star nor in the average of all stars for a given line. This is likely the result of the different acquisition procedure followed here. For the STIS data, the average standard deviation in all the H 2 wavelengths is 4. 4 km s -1. This shows that the errors in the COS wavelength scale are smaller than the 15 km s -1 reported in the man-uals and that no systematic shifts with wavelength are present. For the rest of the paper we will assume that the pointing errors in the COS data result in a velocity uncertainty of 7 km s -1 (the average of the first three H 2 lines considered) while the STIS errors are 5 km s -1. We do not correct the spectra for the H 2 velocities, as it is not clear how to implement this correction in the case of stars with asymmetric H 2 lines.
THE RELATIONSHIP BETWEEN ACCRETION RATE AND C IV LUMINOSITY As mentioned in the introduction,  Johns-Krull et al. (2000)  showed that the accretion rate is correlated with excess C iv luminosity. The excess C iv luminosity is obtained by subtracting the stellar atmosphere contribution to the observed line. They estimated the stellar contribution to be 6 × 10 -5 L ⊙ for a 2R ⊙ object. They also showed that the correlation of C iv excess luminosity with accretion rate is very sensitive to extinction estimates. More recently,  Yang et al. (2012)  obtain a linear correlation between the C iv luminosity (from STIS and ACS/SBC low-resolution spectra) and the accretion luminosity (from literature values) for 91 CTTSs. Here we show that our data are consistent with a correlation between accretion rate C iv luminosity and explore the role that the lack of simultaneous observations or different extinction estimates play in this relationship.
In the observations we present here, the C iv doublet lines are blueshifted by ∼-100 km s -1 , as can be seen from the position of the 1550 C iv line. This points to an origin in the approaching part of the outflow. The net result of these two outflows is that the blueshifted 1548 C iv line is buried under the redshifted H 2 emission. RW Aur A is the only unequivocal example of this coincidence in the current dataset. For HN Tau A the H 2 R(3) 1-8 line is also redshifted, although the redshift (+30 km s -1 ) is within the 2σ of the error introduced by the pointing uncertainty (see also  France et al. 2012). Therefore, HN Tau A may be another object for which we are observing two sides of the outflow, although the velocities of the blueshifted and redshifted sides do not match as well as in RW Aur.
6±0. 6 km s -1 ), derived by  Nguyen et al. (2012) , which we use here, deviates significantly from the velocity of the surrounding molecular cloud (21 km s -1 ;  Kenyon & Hartmann 1995). Using the cloud velocity would shift the C iv lines to the blue even more. France et al. 2012  finds that the H 2 lines have maxima at ∼ 20 km s -1 from the rest velocity, although the line asymmetry suggest components at lower velocity. strong blueshifts (DK Tau, HN Tau A, and RW Aur A for which outflowing material is contributing to the profile, nor AK Sco, for which the two C iv lines are different from each other). We have argued that the velocity uncertainties for this COS dataset are ∼7 km s -1. A onesample Kolmogorov -Smirnov (KS) test confirms that the probability of the observed distribution being normal centered at zero, with 1σ = 7 km s -1 is negligibly small.
In our sample, we have 37% of targets with inclinations larger than 45 • , and the standard deviation of this count is 10%. Therefore, the observed difference in the number of stars with high and low inclinations is not significant. Over a hundred CTTSs with known inclinations are needed before we can distinguish 42% from 58% at the 3σ level. The current inclination dataset is not complete enough to reveal geometric information about the C iv lines. On the other hand, one could assume that the C iv UV lines are not emitted from a particular place but covers the whole star. For a given star, the C iv luminosity should then decrease linearly, by a factor of two, as the inclination increases from 0 • to 90 •. We do not observe this effect either, indicating, at least, that the observed scatter is dominated by intrinsic differences in the objects and not by geometry.
2 that the errors in the COS dataset are ∼7 km s -1. Errors in the wavelength scale cause the data points to move parallel to the dotted line. We focus first on the upper right quadrant of Figure  9 , those objects with both v BC > 0 and v N C > 0. The hatched region of the plot corresponds to v BC ≥ 4 v N C. As we can see, only TW Hya and CY Tau reside fully within this region. In addition, errors in the wavelength calibration may explain why some objects (AA Tau, BP Tau, CS Cha, DE Tau, DN Tau, DS Tau, EP Cha, MP Mus, V1079 Tau, and V4046 Sgr) reside away from the hatched region. Note that while the rotation of the star may contribute to v BC and v N C , typical values of v sin i for CTTSs are 10 to 20 km s -1 , and so the velocity pair would only move to higher or lower velocities by up to this amount, parallel to the dotted line.
This is speculative and requires further study. We note here that  Telleschi et al. (2007)  find a mass-dependent inverse FIP-effect abundance pattern also in CTTSs. These statements are only intended to identify candidates for further study. What we can tell from our observations is that there is considerable scatter of CTTSs to both sides of the linear relation between L SiIV and L CIV. EM analyses for each star are necessary before significant conclusions can be drawn from this sample regarding abundances. These are possible with the DAO dataset, but they are beyond the scope of this paper.
Indeed, we observe C iv and He ii redshifted, with the former redshifted more than the latter. This, however, is not a very constraining prediction. Linsky et al. (2012)  have shown that C iv lines in dwarfs are redshifted by an amount correlated with their rotation period and  Ayres et al. (1983)  have shown that the C iv lines in late-type giants are redshifted with respect to the He ii lines by variable amounts of up to ∼ 20 km s -1. So, for non-CTTSs gas flows in the upper stellar atmosphere may result in line shifts comparable to those we observe in our sample. The predictions regarding the localization of the emitting region in the stellar surface are difficult to test with this dataset, because, as we have argued in section 5. 2. 5, the sample is not large enough for the inclination to serve as a discriminator of the line origin.
On the other hand, there is a large body of evidence (see the references in the introduction) suggesting that the accretion continuum and the (optical) line emission are well localized on the stellar surface. No unequivocal rotational modulation of the lines studied here has been reported in the literature, but this may be because the available dataset does not provide enough rotational coverage. The change in line flux observed among the different RU Lup epochs (Figure  10 ) may result from rotational modulation. If we assume that the NC of C iv is due to post-shock emission, we would naively expect its importance to diminish for high accretion rates as the post-shock is buried in the photosphere  (Drake 2005). However, we observe the opposite, with the NC flux increasing with respect to the BC flux as the accretion rate increases.
C iv and He ii luminosities are also correlated with each other, and the C iv/He ii luminosity ratio is a factor of three to four times larger in CTTSs than in WTTS. This confirms that the ratio depends crucially on accretion phenomena and it is not an intrinsic property of the stellar atmosphere. • We do not find any significant correlation between inclination and C iv V Max , FWHM, or skewness, or between inclination and any of the Gaussian parameters for the C iv decomposition (Figure  6 ). We conclude that the dataset is not large enough for inclination to be a strong descriptor in the sample. The variability observed in the C iv lines of RU Lup and DR Tau (Figure  10 ) may be consistent with the C iv accretion spot coming in and out of view. • We do not find correlations among V Max , FWHM, or skewness, with each other or with accretion rate or line luminosity.
The other is the small range of accretion rates well-covered by objects. Future observational work should focus on resolving these limitations. Even with these limitations, it is clear that with this work we have only scratched the surface of this magnificent dataset. and the WTTS V396 Aur (blue line) for the four emission lines we are analyzing. The vertical scale is arbitrary. The abscissas are velocities (km s -1 ) in the stellar rest frame. Solid lines: Nominal line positions; Dashed lines: Nominal locations of the strongest H 2 lines. N V, dotted lines: N i (1243. 18 Å, +1055 km s -1 ; 1243. 31 Å, +1085 km s -1 ). Si IV , dotted lines: CO A-X (5-0) bandhead (1392. 5 Å, -271 km s -1 ), O iv (1401. 17 Å, +1633 km s -1 ). He II, dotted line: Location of the secondary He ii line.
Note. -(a) In addition to the DAO data; we have made use of data from the following HST proposals: GO 8157: Molecular Hydrogen in the Circumstellar Environments of T Tauri Stars; PI Walter GO 8206: The Structure of the Accretion Flow on pre-main-sequence stars; PI: Calvet GTO 11533: Accretion Flows and Winds of Pre-Main Sequence Stars; PI: Green GO 11608: How Far Does H2 Go: Constraining FUV Variability in the Gaseous Inner Holes of Protoplanetary Disks; PI: Calvet GTO 12036: Accretion Flows and Winds of Pre-Main Sequence Stars Part 2; PI: Green (b ) The data set columns indicate the suffix or the full name (if only one) of the HST dataset used. (c) PSA: Primary Science Aperture (for COS). 2. 5" diameter. For the STIS data the slit size used is indicated.
Lower boundary conditions As stated above, we placed the lower boundary of the hydrodynamic model at p 0 = 1 µbar and did not include H 2 or other molecules in the model. This decision was motivated by photochemical calculations for HD209458b  (Lavvas et al. , in preparation)  that we used to constrain the lower boundary condition. The photochemical model was originally developed for the atmosphere of Titan  (Lavvas et al. , 2008a,b)  but it was recently expanded to simulate EGP atmospheres. It calculates the chemical composition from the deep troposphere (1000 bar) up to the thermosphere above the 0. 1 nbar level by solving the coupled continuity equations for all species based on a database of ∼1,500 reaction rate coefficients and 103 photolysis processes. Forward and reverse rates are included for each reaction with the ratio of the rate coefficients defined by thermochemical data.
Scene analysis, in urban environments, deals with street modeling and understanding. A street mainly consists of roadways, pavements (i. e. , walking areas), facades, still and moving obstacles. In this paper, we investigate the surface modeling of roadways and pavements using LIDAR data acquired by a mobile laser scanning (MLS) system. First, road border detection is considered. A system recognizing curbs and curb ramps while reconstructing the missing information in case of occlusion is presented. A user interface scheme is also described, providing an effective tool for semi-automatic processing of large amount of data. Then, based upon road edge information, a process that reconstructs surfaces of roads and pavements has been developed, providing a centimetric precision while reconstructing missing information.
Mobile laser scanning LIDAR data were obtained by a multi-sensor mobile laser scanning system called Stereopolis (see  (Paparoditis et al. , 2012) ). The Stereopolis system is composed of a navigation device hybridating measurements from two GPSs, an inertial measurement unit, and a wheel odometer. The navigation device produces georeferencing of the platform that allows to locate all data and imagery in a global reference datum. Data processed in this article have been acquired using a Riegl VQ-250 located at the back of the truck. This LIDAR have an accuracy of about one centimeter in depth and digitize 100 scan lines per second, thus providing a spatial sampling in the along track direction of about 10 cm.
Fig. 17  presents result of street ground modeling in a Parisian street. It was obtained rapidly (thanks to simple interacting scheme) and is in a centimetric precision. Accurate street ground modeling is reachable for areas of any scale. Tests have only been performed on some parts of the Parisian network provided by the Stereopolis. It would be interesting to test the method on other LIDAR data corresponding to scenes in other cities, in order to verify robustness of the process.
CONCLUSION In this paper, a road/walking areas surface modeling for MLS LI-DAR data is presented. It is composed of two parts, a model for road side and curb ramp detection is first described. Results of this first step are further used to model surfaces of both roads and walking areas. The process handle occlusions due to obstacles since both road sides and surfaces are reconstructed when no information is available. The system has been successfully tested on large parts of the Parisian road network. A simple usermachine interaction step is also proposed to provide an efficient tool for processing of large amounts of data.
Assessment of risk factors of FRAXH and calculation of FRAXH tool All risk factors for fracture included in FRAXH were assessed baseline. Among the secondary osteoporosis categories in FRAXH, data on type 1 diabetes, osteogenesis imperfecta and chronic liver disease were not available. Prior non vertebral fractures were those that occurred after the age of 50 years, identified by self-reporting during the baseline questionnaire. Bone mineral density of the lumbar spine and of the proximal femur was measured using dual energy X-ray absorptiometry (DXA) in the postero-anterior projection (Hologic QDR-4500; Hologic, Bedford, MA, USA in Paris, Kiel and Sheffield centers) or in the antero-posterior (Lunar Expert devices; GE Lunar, Madison, USA in the Berlin and Aberdeen centers) using standardised procedures and centralised quality control.
Using data of the SOF (Study of Osteoporotic Study) study, a cohort of older communitydwelling women (n = 6252, mean age of 71. 3 years), Ensrud et al  showed that a simple model based on age and BMD predicted 10year risk of hip, major osteoporotic, and any clinical fracture as well as FRAXH models with BMD  [30]. In the MENOS study (mean age 5464 years) included 2,651 women (exclusion of women with past/current osteoporosis treatment. 3 months at baseline (n = 455) with a mean follow-up period of 13. 4 years (61. 4 years), only a limited number of clinical risk factors were found to be associated with the risk of major OP fracture. In this population as in our study, the FRAXH did not significantly improve the discriminatory value of femur BMD alone  [26]. The FRAXH tool is not designed to examine the risk of radiographic vertebral fractures; however we performed this exploratory analysis because of the importance of vertebral fractures in the outcome of osteoporosis, and because OPUS design gives the opportunity to assess that, as vertebral fractures were assessed on radiographs performed with standardized Table  6.
Biochemical markers of bone turnover were not in FRAXH because of the biological variability, the lack of reference analytes and analytical standards  [44]. An attempt to adjust the result of FRAXH based on dose of corticosteroid has been recently published  [45]. Our study has several limitations. The first limitation concerns the characteristics of the cohort: low rate of incident fracture, quite short duration (6 years) of the follow-up and the significant proportion of women previously or currently receiving bone protective therapy which limit a lot the interpretation of the results. Our data has been obtained in a population including patients previously treated with anti-osteoporotic treatments. We showed that FRAXH is a predictor of major osteoporotic fracture in patients ever treated and not in patients never treated.
Acknowledgments We would like to acknowledge the contributions of the other members of the OPUS teams at the five participating centres:  Rosie Reid  (deceased),  Lana Gibson  and  Alison Stewart in Aberdeen ;  Antonia Gerwinn ,  Dr. Maren Glu ¨er ,  Roswitha John ,  Roswitha Marunde-Ott ,  Monika Mohr ,  Regina Schlenger ,  Pia Zschoche ,  Dr Reinhard Barkmann ,  Dr. Carsten Liess ,  Carsten Rose  and  Wolfram Timm in Kiel ;  Therese Kolta ,  Nathalie Delfau  in Paris;  Margaret Paggiosi ,  Nicky Peel ,  Diane Shutt ,  Anne Stapleton ,  Debbie Swindell  in Sheffield. We also would like to thank for the support of the equipment manufacturers: DMS, IGEA,  OSI/Osteometer Meditech , Quidel/metra.
Edges link gene functions that share genes. Edge thickness is proportional to number of shared genes and edge color is blue for shared worm genes and green for shared yeast genes. Clusters of functionally related nodes were manually circled and labeled. the BioGRID database  (Breitkreutz et al, 2008) , 31 of which are conserved (Supplementary Figure  3 ). This overlap is statistically significant (P ¼ 1. 5 Â10 À 7 , Fisher's exact test), and likely underestimates the true number of conserved interactions, as no comprehensive human SH3-mediated PPI network is available. Moreover, at least 14 worm SH3 domains have human orthologs with highly conserved SH3 domain sequences (455% sequence identity), suggesting that their specificity is also conserved (see Supplementary Figure  1 ). Thus, our results suggest that extensive SH3-mediated PPI rewiring has occurred between multicellular worm and unicellular yeast.
Rewiring scenarios The rewiring observed between worm and yeast may be due to a lack of conservation in: (i) the SH3 domain specificity (or the SH3 domain presence); (ii) the ligand; or (iii) both. For SH3mediated interactions whose ligand binding motif can be predicted using the phage-derived binding specificity, our data enable us to estimate the frequency of these different rewiring scenarios. We analyzed all SH3-mediated interactions present in either our worm SH3 interactome or the yeast SH3 interactome of  Tonikian et al (2007) , but not both (also excluding interactions found to be conserved by considering published data in the BioGRID database), and for which the binding motifs can be predicted based on our SH3 binding profiles (see Materials and methods and Supplementary Table  9 ).