dataesr/scientific-paragraphs-categorization huggingface dataset:
Other conclusions for the AGU coder practically coincide with the conclusions 3 to 5 for the AGU coder given above. The scatterplot in Fig. 5 (b) is very similar to the one in Fig. 4 (b). The conclusions 7 and 8 given above for AGU are, in general, valid for the ADCT coder as well. The difference is that the fitted curve crosses zero level at a smaller P 2σ (about 0. 87). This means that the ADCT coder provides a slightly better visual quality of the compressed noisy images (under the condition of the same QS). The results for the AGU-M coder have been obtained for SF ¼ 2. 0σ 0. Some quantitative data are given in Table  3  in the paper. 29 Here, we prefer to present the obtained results as scatter-plots (see Fig. 6 ). The best fitting has been provided by the fourth (for ΔPSNR) and fifth (for ΔPHVS) order polynomials.
Analyzing the metric ΔPSNR, we can conclude that OOP exists if P 2σ exceeds 0. 80 [see the fitted curve in Fig. 7(a) ] or if P 2. 7σ is lower than 0. 10. If the metric ΔPHVS is considered, compression in an OOP neighborhood is quite probable if P 2σ exceeds 0. 88 [analyze the fitted curve in Fig. 7(b) ] or if P 2. 7σ is less than 0. 06. The results are, in general, very close to the corresponding data for the AGU-M coder (see Fig. 6 ). Compared to the AGU and ADCT coders, the ADCT-M coder does not have obvious advantages. Completing our analysis for the test images and simulated AWGN, we can state the following. The proposed prediction methodology is fast and accurate enough. The AGU coder and, especially, the ADCT coder provide, on average, better results for the considered cases of noisy image lossy compression than the AGU-M and ADCT-M coders, especially when oriented on visual quality.
Second, one might doubt if OOP exists in the directly transformed compressed image and inverse transformed image. The simulation results presented in Refs. 20 and 28 show that this is true. Third, for real-life RS data, we do not have a true image, so no metric Metr tc can be calculated. Thus, we can only calculate the predicted values of ΔPSNR and/or ΔPHVS based on estimated P 2σ or P 2. 7σ and look at how these predicted values behave, depending upon the sub-band index. Fourth, there are sub-bands in Hyperion RS data, which are usually not exploited (with indices 1 to 12, 58 to 76, 224 to 242). We also do not present data for these sub-bands. Let us start by analyzing the values P 2σ and P 2. 7σ for real-life data (the set EO1H1800252002116110KZ). The plots are given in Fig. 9. Analysis shows that both probabilities vary practically from zero to unity.
If, according to the prediction, OOP does not exist, then "more careful" compression is necessary. This can lead to less distortion being introduced and a better quality of compressed data. Also, it seems possible to carry out an adaptive setting of QS or SF depending on image local content. This is one possible direction for future research. The proposed approach can be put into the basis of componentwise compression of multichannel RS images, as has been shown for hyperspectral RS data of Hyperion and AVIRIS sensors. We expect that 3-D compression of such data collected into groups might increase CR sufficiently. This is another possible direction for future research.
In the last decade, online PM 1 chemical composition measurements have been performed using aerosol mass spectrometers (AMS) at a number of locations. Measurements of online chemical composition are useful to study hourly variations and daily patterns. Most of these studies, however, correspond to shortterm measurement campaigns (typically a month) (e. g. ,  Crippa et al. , 2014; Jimenez et al. , 2009; Lanz et al. , 2010; Ng et al. , 2010; Zhang et al. , 2007)  given the intensive instrument maintenance required and the need of highly qualified personnel for a good quality data set. In contrast to the use of the AMS in relatively short campaigns, the more recently developed Aerodyne aerosol chemical speciation monitor (ACSM) is becoming a widely used online instrument for long-term measurements of PM 1 chemical composition  (Budisulistiorini et al.
Recent publications have investigated most of the existing worldwide AMS databases (e. g. ,  Crippa et al. , 2014; Jimenez et al. , 2009; Lanz et al. , 2010; Ng et al. , 2010; Zhang et al. , 2007)  and reflected a prevalence of organic aerosols (20-90 %) in the submicron fraction, largely independent of the region and type of environment. However, our knowledge on organic aerosol (OA) formation, sources, and atmospheric processing is still very incomplete, especially for secondary organic aerosol (SOA) formed from chemical reactions of gas-phase species (e. g. ,  Donahue et al. , 2014; Hallquist et al. , 2009; Kroll and Seinfeld, 2008; Robinson et al. , 2007; Volkamer et al. , 2006). Recent progress has been made in identifying primary organic aerosol (POA) sources (e. g. ,  Elbert et al. , 2007; Zhang et al.
, 2014 (Ripoll et al. , , 2015)). Co-located online and offline PM 1 measurements were also carried out to complement the ACSM data set. Hence, the work presented here interprets the real-time variation of inorganic and organic submicron components during 10 months (July 2011-April 2012), and the types of OA are also studied. Special emphasis is placed on the analysis of the diurnal pattern and seasonal variations of chemical components and the main factors influencing these variations.
Air masses reaching MSC are mainly from the Atlantic (62 % of the days) throughout the year. From March to October, North African (NAF) episodes are more frequent (17 % of the days) and are very often alternated with the summer regional (SREG) scenarios (12 % of the days). The winter regional (WREG) scenarios are detected from October to March (11 % of the days), as well as the European (EU) episodes (11 % of the days). Conversely, the Mediterranean (MED) episodes are detected sporadically (4 % of the days). The boundary layer height was calculated using the Global Data Assimilation System (GDAS1) model from the NOAA Air Resources Laboratory (http://www. ready. noaa. gov/READYamet. php) (Fig. 1 ).
5 were in good agreement with concentrations from other co-located instruments, a CE of 0. 5 was used. The organic components were further investigated by applying the Multilinear Engine (ME-2)  (Paatero, 1999)  to the organic mass spectra. With the ME-2, the user can introduce a priori information about sources with the so-called a value approach. Hence, the user inputs one or more factor profiles and a constraint defined by the a value, which determines the extent to which the output profile can differ from the profile fed to the model. The source apportionment of OA was performed applying ME-2 using the custom software tool of Source Finder (SoFi) version 4. 8 developed by  Canonaco et al. (2013). The ME-2 analysis was carried out separately for the summer period (14 July 2011-24 September 2011) and the winter period (10 January 2012-7 March 2012).
Real-time PM 1 mass concentrations were continuously measured by an optical particle counter (OPC, model GRIMM 1. 107 calibrated with a different latex PSL refraction index 1. 59). PM 1 30 min data were averaged daily and subsequently corrected by comparison with 24 h standard gravimetric mass measurements. The absorption coefficient was measured continuously at 637 nm using a multi-angle absorption photometer (MAAP, model 5012, Thermo). Equivalent black carbon (BC) mass concentrations  (Petzold et al. , 2013)  were calculated by the MAAP instrument software by dividing the measured absorption coefficient σ ap (λ) by 6. 6 m 2 g -1 , which is the instrument default mass absorption cross section (MAC) at 637 nm  (Müller et al. , 2011; Petzold and Schönlinner, 2004). Particle scattering (σ sp ; 0-360 • ) and hemispheric backscattering (σ bsp ; 90-270 • ) coefficients at three wavelengths  (450, 525, 635 nm)  were measured with a LED-based integrating nephelometer (model Aurora 3000, ECOTECH Pty, Ltd, Knoxfield, Australia).
Finally, all meteorological data were measured by the Catalonian Meteorological Service from the Montsec d'Ares station. Gaseous pollutants (O 3 , NO, NO 2 , CO and SO 2 ) were measured using real-time monitors belonging to the Network of Control and Surveillance of Air Quality of the Autonomous Government of Catalonia. NO and NO 2 concentrations were measured using a Thermo Scientific instrument model 42i-TL, CO using a Teledyne 300 EU Gas filter correlation analyzer, O 3 using a MCV 48AV UV photometry analyzer, and SO 2 using a Teledyne 100 EU UV fluorescence analyzer. In addition to these routine measurements, two intensive campaigns were performed in July-August 2011 and January-February 2012. During these intensive campaigns PM 1 filters were collected daily and a scanning mobility particle sizer (SMPS) was installed to measure particle number size distribution of mobility diameters of 11-350 nm in the summer campaign and of 8-450 nm in the winter campaign.
The SMPS system comprises a classifier unit (model TSI 3080) and a differential mobility analyzer (DMA, model TSI 3081) connected to a condensation particle counter (CPC, model TSI 3772). The SMPS data for the winter campaign were also used to estimate the mass concentration to compare with the ACSM data. To this end, the volume size distributions were calculated from the measured particle number distributions assuming sphericity. The total volume concentrations were computed by integrating over the measured particle range and converted to mass concentration using the estimated composition-dependent density, calculated using the chemical composition given by the ACSM and the equation of  Salcedo et al. (2006). Average concentrations shown in the whole paper are arithmetic averages unless otherwise specified.
, 2015). Therefore, the OM-to-OC in the present study was estimated from the slope of OA, measured by ACSM, vs. OC, obtained from offline measurements. This slope was found to be 3. 39; however, values of 2. 2 are more common for aged aerosol (e. g. ,  Aiken et al. , 2008; Minguillón et al. , 2011; Takahama et al. , 2011). This disagreement of a factor of 1. 54 may be attributed to different reasons. A negative volatilization ar- tifact may occur in the filters, hence resulting in an underestimation of OC. Alternatively it is possible that the RIE for OA in the ACSM is larger than the value of 1. 4 determined for the AMS, a topic currently being investigated by the ACSM manufacturer, which would result in an overestimation of OA. Similar series of intercomparisons with a similar discrepancy for OA have been found for a 1-year data set (June 2012-July 2013) with the same instrument at the Montseny site  (Minguillón et al.
Acknowledgements. This study was supported by the  Ministry of Economy and Competitiveness  and  FEDER  funds under the  PRISMA  ( CGL2012-39623-C02-1 ) and  CARIATI  ( CGL2008-06294/CLI ) projects, and by the  Generalitat de Catalunya  ( AGAUR 2009 SGR8  and the DGQA). The research received funding from the  European Union Seventh Framework Programme  ( FP7/ 2007-2013) ACTRIS  under grant agreement no. 262254. We would like to extend their gratitude to the personnel from the  COU  and the OAdM. We would also like to express our gratitude to the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model, and boundary layer height calculation, used in this publication. D. A. Day  and  J. L. Jimenez  thank the  DOE (BER/ASR )  DE-SC0011105  and  NOAA NA13OAR4310063.
, 2011). Exploration for these ecologically important, but numerically less abundant, members of subsurface ecosystems will require the capability of accessing high volume subsurface material (fluids and/or solids) from any proposed ICDP site. Most current microbiology-based research efforts that aim to describe subsurface microbial communities utilize so-called next generation sequencing approaches (e. g. ,  Wrighton et al. , 2012; Baker and Dick, 2013)  that can detect and identify microorganisms present in deep systems even when they comprise < 1 % of the total community. Because this technology is sensitive to trace constituents in DNA extracts, maintaining sample quality and conducting proper controls is essential for reducing the likelihood of sequencing contaminants infesting the community database.
Methodological and reagent blanks should be included to account for DNA contamination that might occur during sampling and in the laboratory or that might be present in reagents, enzymes, or buffers. By consulting databases that catalog classical contaminant sequences, such as those present in DNA extraction kits (cf. Salter et al. , 2014) , indigenous minor or rare biosphere microorganisms can be identified with higher confidence. In some cases, oligotyping may differentiate closely related but distinct taxa  (McLellan et al. , 2013)  as well as the respective origin of these taxa (i. e. , from the subsurface vs. introduced at some point during the analysis) (cf. Magnabosco et al. , 2014).
Acknowledgments Authors thank  Helene Chacun  and  Daniel Foulkes  ( Institut Galien ) for their technical assistance. We are grateful to  Dr. Gerard Fe  ´rey ( Institut Lavoisier ) for helpful discussions. This work has been carried out in the frame of the European ITN networks CYCLON (nu 237962) and  CycloN Hit  (nu 608407). We also acknowledge support from  CNRS ,  Junta de Andalucia  ( FQM-6903 ) and the  ANR Virmil  grant.
Aims. Using the VIMOS Public Extragalactic Redshift Survey (VIPERS) we aim to jointly estimate the key parameters that describe the galaxy density field and its spatial correlations in redshift space. Methods. We use the Bayesian formalism to jointly reconstruct the redshift-space galaxy density field, power spectrum, galaxy bias and galaxy luminosity function given the observations and survey selection function. The high-dimensional posterior distribution is explored using the Wiener filter within a Gibbs sampler. We validate the analysis using simulated catalogues and apply it to VIPERS data taking into consideration the inhomogeneous selection function. Results. We present joint constraints on the anisotropic power spectrum as well as the bias and number density of red and blue galaxy classes in luminosity and redshift bins as well as the measurement covariances of these quantities.
). These measurements typically call for precise reconstructions of the density field corrected for observational systematics such as survey gaps and inhomogeneous sampling. This work extends previous analyses by considering the joint distribution of galaxy luminosity, colour and clustering bias with the spatial power spectrum and density field. We begin in Sec. 2 with an overview of VIPERS and the parameterisation of the selection function. The data model is described in Sec. 3, and the method is outlined in 4. In Sec 5 we present the constraints from VIPERS data. We assume the following fiducial cosmology Ω m = 0. 27, Ω b = 0. 0469, Ω Λ = 0. 73, n s = 0. 95, H 0 = 70 km/s/Mpc, σ 8 = 0. 80. This coincides with the MultiDark simulation run  (Prada et al. , 2012)  that was used to construct the mock VIPERS catalogues.
Overplotted is the fiducial model adopted in this study (black dashed curve). We find agreement with the best-fit model using VIPERS data by Rota et al (in preparation) (purple dot-dashed curve). The pink dashed curve is the mean of the power spectrum estimates taken from the 27 mock catalogues. We present three error estimates: the internal chain variance determined from VIPERS data (grey steps), the chain variance determined from mock catalogues (blue steps) and the variance of the individual estimates from the 27 mock catalogues (red steps). The error corridors show 70% confidence intervals. The co-moving coordinates are computed using the fiducial cosmological model. In the real catalogue only, galaxies are up-weighted by the inverse spectroscopic success rate (SSR) depending on quadrant and apparent i-band magnitude.
2. We compute the density on the grid using the anti-aliasing filter based on the super-sampling method proposed by  Jasche et al. (2009)  with a soft k-space cutt-off as described in Appendix A. 3. The angular (α, δ) and radial (z) components of the selection function are computed separately on the grid: w(α i , δ i , z i ) = w(α i , δ i )w(z i ). 4. For the angular component, we generate a uniform grid of test points that over-sample the grid by a factor of 8 and reject points outside the survey angular mask. For VIPERS data the remaining are down-sampled by the TSR. The points are then assigned to the grid points using the anti-aliasing massassignment scheme. The selection function w i is then given by the normalised density of test points on the grid. 5. The radial component of the selection function is estimated in bins of redshift, luminosity and colour.
We estimate the median K-correction term for galaxies within each bin and use Eq. 5 to compute the unnormalised N(z). 6. We estimate the generalised shot noise variance ν i ≡ σ 2 i / Ni which depends on the mask through the anti-aliasing filter in Monte Carlo fashion. We generate a set of 1000 shot noise maps by distributing random points over the survey volume. For VIPERS data the points are down-sampled by TSR. We then compute the variance for each cell of the map over the 1000 realisations. To regularise the noise covariance matrix, a threshold is set ν i = max(ν thresh , ν i ) where ν thresh = 0. 3 for mocks and 0. 15 for data to account for TSR. With the galaxy number density map, selection function and noise map, we have all the components of the data model required to estimate the posterior probability distribution in Eq.
1. To sample this probability distribution we run the Gibbs sampler Markov chain for 2000 steps and, allowing for a burn-in period, begin the analysis from step 1000. The convergence properties and justification for the burn-in period are shown in Appendix C. For the VIPERS data we ran 7 independent chains for 2000 step each providing 7000 post-burn-in samples for analysis. Taking the variance of the Markov chain gives us an internal error estimate on the parameters. The runs on mock catalogues show that the chain variance corresponds to the expected sample variance for the power spectrum. However, this is not necessarily true for the other statistics. For instance the luminosity function quantifies the distribution of observed galaxies which remains fixed in the chain. It is only indirectly dependent on the underlying density.
, 2012). Previous studies with VIPERS data estimated the galaxy bias of the full galaxy sample as a function of luminosity and redshift. Marulli et al. (2013)  measured the projected galaxy correlation function in luminosity and redshift bins to constrain the mean bias averaged over scales 5 -20h -1 Mpc. Di Porto et al. (  2014 ) modeled the counts-in-cells probability distribution function to estimate the linear bias. To compare our estimate of the galaxy bias with these previous results we construct luminosity threshold samples counting both red and blue galaxies. We cross-correlate these number density maps with the Wiener density field from the core analysis and estimate the measurement uncertainty from the chain variance. The resulting bias values are shown by the markers with error bars in Fig.
Despite this freedom, we find that the resulting error in the key quantities such as the distortion parameter are only marginally larger than that given by traditional methods which can be strongly dependent on parameterisation. Our methodology may be extended to jointly analyse multiple datasets in a self-consistent manner. A particular challenge when considering multiple surveys is dealing with the differ-ences in angular coverage, sampling rates and galaxy types. The Bayesian approach provides a means to homogenise datasets allowing for consistent measurements. For future studies with VIPERS we can consider the joint analysis with the VVDS-Wide spectroscopic survey  (Garilli et al. , 2008). Although the two surveys partially overlap, the selection function and sampling rates differ prohibiting their direct combination.
Acknowledgements. This work is based on observations collected at the  European Southern Observatory, Cerro Paranal, Chile , using the  Very Large Telescope  under programs  182. A-0886  and partly  070. A-9007. Also based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA/DAPNIA, at the  Canada-France-Hawaii Telescope (CFHT) , which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii. This work is based in part on data products produced at TERAPIX and the  Canadian Astronomy Data Centre  as part of the  Canada-France-Hawaii Telescope Legacy Survey , a collaborative project of  NRC  and  CNRS. The VIPERS web site is http://www.
vipers. inaf. it/. We acknowledge the crucial contribution of the  ESO  staff for the management of service observations. In particular, we are deeply grateful to  M. Hilker  for his constant help and support of this program. Italian participation to VIPERS has been funded by  INAF  through  PRIN 2008  and 2010 programs. LG, and BRG acknowledge support of the  European Research Council  through the  Darklight ERC Advanced Research Grant  (#  291521 ). AP, KM, and JK have been supported by the  National Science Centre  (grants  UMO-2012/07/B/ST9/04425  and  UMO-2013/09/D/ST9/04030 ), the  Polish-Swiss Astro  Project (co-financed by a grant from  Switzerland , through the  Swiss Contribution  to the enlarged European Union), and the  European Associated Laboratory Astrophysics Poland-France HECOLS.
KM was supported by the  Strategic Young Researcher Overseas Visits Program for Accelerating Brain Circulation  (#  R2405 ). OLF acknowledges support of the  European Research Council  through the  EARLY ERC Advanced Research Grant  (#  268107 ). GDL acknowledges financial support from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement #  202781. WJP and RT acknowledge financial support from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement #  202686. WJP is also grateful for support from the  UK Science and Technology Facilities Council  through the grant  ST/I001204/1. EB, FM and LM acknowledge the support from grants  ASI-INAF I/023/12/0  and  PRIN MIUR 2010-2011.
III. EVENT SELECTION The data are recorded using a trigger that requires two muons. One muon is required to have p T > 13 GeV, and the other, p T > 8 GeV. This trigger has no requirement on the isolation of the muons. Events with a pair of oppositely charged, wellreconstructed, and isolated muons and an isolated photon are selected. The kinematic and fiducial requirements for selecting events are based wholly on the muon and photon kinematic quantities, and are summarized in Table  I. As explained below, we use the dimuon mass M μμ to define a "signal region" that is rich in FSR photons, and a "control region" that is dominated by background sources of photons. Muons are selected in the manner developed for the measurements of the DY cross section  [17]. They must be reconstructed using an algorithm that finds a track segment in the muon detectors and links it with a track in the silicon tracker, and also through an algorithm that extrapolates a track in the silicon tracker outward and matches it with hits registered in the muon detectors.