dataesr/scientific-paragraphs-categorization huggingface dataset:
1, than for MODIS SST, 3. 2. Finally, we obtain the singularity spectra from the em-650 pirical distributions of singularity exponents shown in Fig. 4c ) and d). One can see in Fig. 4e ) that for the two GLOB-COLOUR products the shape of the spectrum is closer to binomial cascade of multiplicative processes than for MERIS. This is discussed in more depth in the next sections. We now apply the methodology to infer ocean pCO 2 maps at 660 super-resolution from pCO 2 at low resolution derived from For the inference we use the following three combinations of Chl and SST products described in Section 2. 1: MERIS-OSTIA, GLOBCOLOUR-OSTIA, GLOBCOLOUR-MODIS. We do not include the MERIS-MODIS combination in the analysis due to the fact that the use of such satellite data results in a too drastic reduction of the coverage of the resulting pCO infer 2 field, but using merged products offers wider coverage instead.
The inferred pCO 2 obtained from two merged products for Chl-a, GLOBCOLOUR GSM and GLOBCOLOUR AVW is rather the same, with a slight improvement when GSM is used. Thus for the sake of clarity, we only show Figures for GLOBCOLOUR-GSM and some statistical results making comparisons with AVW. Therefore from now on we refer GLOBCOLOUR to the Chl-a obtained by the GSM merged method. Figure  5d  shows one example of pCO infer 2 field corresponding to March 22, 2006 when we use SST data from OSTIA (Fig. 5a ), Ocean Colour from GLOBCOLOUR (Fig. 5b ) at high resolution and pCO 2 at low resolution (Fig. 5c ) derived from CarbonTracker air-sea flux of CO 2 (Fig. 5e ) and using the Eq. 2. The air-sea flux of CO 2 at super-resolution (Fig. 5f ) is obtained from the pCO infer 2 field and a constant value of atmospheric pCO 2 equal to 385.
810 Next we explore the quality of the information contained in the transition fronts, in particular, in the non-merged products such as MERIS OC and MODIS SST as compared to the merged products: GLOBCOLOUR OC and OSTIA SST. The PDFs of pCO 2 values from CarbonTracker and. Indeed the histograms show also a better agreement between merged products and CarbonTracker: the peak of the PDF for pCO infer 2 is closer to CarbonTracker peak in the case of OSTIA and GLOBCOLOUR than when using MERIS and MODIS products. Furthermore, to examine the transition fronts for the different products we compute the singularity spectra for the three product combinations (see Figure  10 ). At low values of h (singularity exponent), related to the most singular manifolds, the shape of singularity spectrum for inferred data from merged products better matches a binomial cascade, with an improved description of the dimension of the sharpest transition fronts.
Validation with in-situ measurements 840 Next, we perform a validation analysis of the results of our algorithm to infer pCO 2 at super-resolution with field observations of oceanic pCO 2. We perform the validation using pCO 2 ocean data from in-situ measurements (pCO insitu 2 ) taken in the Benguela region (see Section 2. 3). We decided 845 to carry out directly the validation on pCO 2 rather than on the air-sea CO 2 flux since the field measurements do provide oceanic pCO 2 data. The number of valid intersections is the largest with the OSTIA-GLOBCOLOUR combination (Table  4 ). To quantify the loss of valid intersections between the in-situ measurements and points in the pCO inf er 2 field, likely due to cloudiness, we compute the relative difference between the number of measurements into the CarbonTracker domain and the valid points in the inferred pCO 2 field with respect to the number of intersections measurements of each cruise and the pCO Ctrack (i) -pCO insitu 2 (i)) (5) M D infer = 1 N N i=1 (pCO inf er 2 (i) -pCO insitu 2 (i)) , ( 6 ) where N is the number of intersections.
Introduction to the electronic age, New York: E-Publishing Inc; 2009, p. 281-304. Reference to a website: [5] Cancer Research UK. Cancer statistics reports for the UK, http://www. cancerresearchuk. org/ aboutcancer/statistics/cancerstatsreport/; 2003 [accessed 13 March 2003]. Reference to a dataset: [dataset] [6] Oguro M, Imahiro S, Saito S, Nakashizuka T. Mortality data for Japanese oak wilt disease and surrounding forest compositions, Mendeley Data, v1; 2015. https://doi. org/10. 17632/ xwj98nb39r. 1.
Coupled Model Approaches Coupled general circulation models (GCMs) of increasing complexity, which often include not only atmosphere, ocean, and land but also biological and chemical processes, provide the most comprehensive simulations of the climate system. Data from model experiments with different forcing combinations are readily available from the archive of the World Climate Research Programme's Coupled Model Intercomparison Project phase 5 (CMIP5)  24  and can be utilized in analyses of extreme events. This typically involves pooling data from multimodel ensembles of simulations with and without anthropogenic influences and so generating large samples of the relevant climatic variable (e. g. temperature, if investigating a heat-wave event). The distribution of the variable in the 'actual' world and the counterfactual 'natural' world without human influence on climate can thus be constructed, from which estimates of the FAR for the event under investigation are obtained.
Sea Surface Temperature Forced Atmosphere Only Model Approaches Another way of conditioning results on aspects of the climatic conditions present at the time of the event is to prescribe observed sea surface temperature (SST) anomalies into an atmosphere only climate model. Thus, many event attribution studies contrast atmosphere-only general circulation model (AGCM) simulations representing the 'actual' world including the observed evolution of SSTs with simulations of the counterfactual 'natural' world, a 'world that might have been,' had there been no human influence on climate. 16, 34 s with coupled model approaches, this methodology also requires the availability of sufficiently large model datasets to simulate the statistics of the events in question, and relies on the model's ability to reliably simulate the climate conditions generating the extreme event.
9, 46 mpirical approaches can thus serve as a check on model results and are often appreciated by the users who are skeptical of the veracity of climate models, even though it necessarily also makes assumptions. They can also be employed for events that cannot yet be represented well by climate models used for attribution, such as extreme summer convective events. An empirical approach to event attribution by fitting observed data to statistical distributions has been used to show that climate change did not play a major role in the 2011 floods in Thailand. 47 A nonsignificant downward trend in rainfall in the upper Chao Praya basin in Thailand found in this analysis, agreed with a similar lack of increasing trends in climate models. Other such analyses show positive results. For example, the cold waves in the United States observed in early 2014 are found to be significantly less likely  48  although they are still not uncommon, with a return time in 2014 of about 12 years compared to a return time in 1950 of about every 4 years (Figure  5 ).
Model evaluation is subject to the availability and quality of observations. Identification of a clear signal of change in the observations often requires long time series of homogenous data but an evaluation of a model's representation of the most relevant processes for a particular event may be achieved with high spatial resolution data that are not available over long time frames. Thus, homogenized multidecadal datasets may be needed for some applications such as signal detection whereas reanalyses or satellite data may be appropriate for others such as model verification. Clear evidence for human influence on extreme temperature events seen in many studies  20  benefits from robust observational support, whereas mixed evidence for human influence on extreme precipitation events 20 can be affected by inadequacies in observations in many parts of the world,  56  as well as limitations in models' representation of cloud processes.
ACKNOWLEDGMENTS The research leading to these results has received funding under the  EUCLEIA (EUropean Climate and weather Events: Interpretation and Attribution ) project under the European Union's  Seventh Framework Programme  [FP7/2007-2013] under grant agreement no  607085  ( PAS, NC, J-V, HvS, GvO, RV, PW, PY ) PAS was partially supported by the  UK-China Research & Innovation Partnership Fund  through the  Met Office Climate Science for Service Partnership (CCSP) China  as part of the Newton fund. PAS and NC were partially supported by the  Joint UK DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). PY acknowledges support from  ERC  Grant No. 338965-A2C2. Y. Sun  is supported by  Chinese  programs  2012CB417205  and  GYHY201406020.
2010) on the Herschel Space Observatory (Pilc 2015 RAS  bratt et al. 2010). They used data from the Lockman Hole and Extragalactic First Look Survey (XFLS) fields of the Herschel Multi-tiered Extragalactic Survey program (Her-MES, http://hermes. sussex. ac. uk, Oliver et al. 2012 ) over about 15 deg 2 observed during the Herschel Science Demonstration Phase (SDP), and including a few hundred sources to a flux limit of about 40 mJy in all three SPIRE bands  (250, 350, 500 µm). Their published functions were integrated over a wide redshift interval at 0 < z < 0. 2. Because of the limited source statistics,  Vaccari et al. (2010)  could not take into account any evolutionary corrections, while significant evolution is expected to be present over this large redshift bin. Still based on the HerMES database, but using a much larger total area, many more independent sky fields, and deeper fluxes, the present paper reports on a systematic effort to characterise the local and low-redshift luminosity functions of galaxies in the sub-millimetre bins.
The Spitzer Data Fusion As previously mentioned, the HerMES fields were chosen so as to have the best multi-wavelength data for sky areas of a given size. In particular, the fields used in this work are covered by Spitzer 7-band IRAC and MIPS imaging data which enable not only an improved identification process but also the detailed characterisation of the infrared SEDs of Herschel sources. In this work we exploit the Spitzer Data Fusion  (Vaccari et al. 2010 and Vaccari et al. , in prep. , The Spitzer Data Fusion thus represents an ideal starting point to perform statistical studies of infrared galaxy populations, such as detailed SED fitting analyses to estimate photometric redshifts and masses, as well as star formation rates (SFRs); an early version of the database has already been used to that effect by  Rowan-Robinson et al.
(2013). It has been used to validate Herschel SDP observations within the HerMES consortium team and to produce current and future public HerMES catalogues 2. Since this paper only uses the Spitzer Data Fusion to derive SPIRE local luminosity function estimates, we refer the reader to  Vaccari et al. (in prep. )  for a complete description of the database and in the following we only summarise its basic properties as they relate to this work. The Spitzer Data Fusion is constructed by combining Spitzer IRAC and MIPS source lists, as well as ancillary catalogues, following a positional association procedure. Source extraction of IRAC 4-band images and of MIPS 24 µm images is carried out using Sextractor  (Bertin & Arnouts 1996) , whereas MIPS 70 and 160 µm source extraction is carried out using APEX  (Makovoz & Marleau 2005).
Catalogue selection is determined by a reliable IRAC 3. 6 or IRAC 4. 5 µm detection. We then associate MIPS 24 µm detections to IRAC detections using a 3 arcsec search radius, while MIPS 70 and 160 µm catalogues are matched against MIPS 24 µm positions using a search radius of 6 and 12 arcsec, respectively. UV, optical and near-infrared catalogues are then matched against IRAC positions using a 1 arcsec search radius. This multi-step approach increases the completeness and reliability of the longer-wavelength associations, while better pin-pointing MIPS sources using their IRAC positions. The HerMES wide fields used in this work are part of the Spitzer Data Fusion and are all covered both by Spitzer 7-band infrared imaging and by SDSS 5-band optical imaging and optical spectroscopy  (Csabai et al.
SED fitting Thanks to the Spitzer Data Fusion we are able to perform the multi-wavelength SED fitting analysis of our HerMES Wide Fields sample and thus estimate the IR bolometric (8 -1000 µm) and monochromatic rest-frame luminosities and relative k-corrections. We perform the SED fitting analysis using Le Phare  (Arnouts et al. 1999 and Ilbert at al. 2006). To perform the fit we use SDSS ugriz, 2MASS JHKs, IRAC-3. 6, IRAC-4. 5, IRAC-5. 8, IRAC-8. 0, MIPS-24, MIPS-70, MIPS-160, SPIRE-250, SPIRE-350 and SPIRE-500 flux densities, which are available over the whole area covered by our sample. As template SEDs we use two different set of empirical templates according to the range of wavelengths we are fitting: in the optical-MIR range (up to 7 µm rest-frame) we use the same templates and extinction laws exploited by the COSMOS team to estimate the COSMOS photometric redshifts as in  IIlbert et al.
DISCUSSION Using some of the widest-area surveys performed by Spitzer and Herschel, in this paper we have studied in details the local luminosity functions of SPIRE sources. Our LLFs at 250/350/500 µm (SPIRE) strongly constrain the local lumi-nosity density of the Universe throughout the FIR/submm wavelength range. Our estimates mostly confirm and improve upon the HerMES SDP results published in  Vaccari et al. (2010) , thanks to our increased statistics; this is particularly visible in the 500 µm LF solution, which shows strongly reduced uncertainties. Dye et al. (2010)  used Herschel SDP data to compute the H-ATLAS  (Eales et al. 2010 ) SPIRE local luminosity function. This analysis was carried out very early during the Herschel mission and relied on shallower SPIRE observations, much fewer ancillary data and smaller coverage area than our own.
On the other hand, deep data become more and more important with increasing redshift where, our sample soon starts being limited to the higher luminosity bins. Our luminosity function estimates show significant and rapid luminosity evolution already at low redshifts. In Fig. 19  we report our results about the redshift evolution of the parameters expressing the spatial density dependence of the LFs (Φ * ) and the luminosity dependence (L * ) estimated for the IR bolometric at the 250 µm luminosity functions. We found positive evolution in luminosity and negative evolution in density with L * IR ∝ (1 + z) 6. 0±0. 4 , Φ * IR ∝ (1 + z) -2. 1±0. 4 for the IR bolometric LF and L * 250 ∝ (1 + z) 5. 3±0. 2 , Φ * 250 ∝ (1 + z) -0. 6±0. 4 for the 250 µm LF. The high values of the evolution rates that we find (both positive and negative) for the luminosity and density parameters are however consistent with previous results based on previous and more limited datasets from Spitzer  (Patel et al.
2. From these preliminary comparisons we notice that the  Fontanot, Cristiani, & Vanzella (2012)  predictions (using the MORGANA code by  Monaco et al. 2007 ) seem to broadly reproduce the shape of the LF within the uncertainties, but they underestimate the LF at lower luminosities when compared to our IR bolometric LF estimates. Other predictions done by e. g. ,  Negrello et al. (2007) ,  Serjeant & Harrison (2005)  and  Valiante et al. (2009)  at different wavelengths also show good agreement with our results at higher luminosities, but most of them seem to underestimate the LF when compared to what we obtain at lower luminosities. A more careful and systematic analysis of existing and improved models is required to properly address this issue (e. g. ,  Gruppioni et al. 2015, Franceschini et al.
CONCLUSIONS The determination of the galaxy luminosity function is often hampered by the difficulties of covering a wide area down to faint fluxes on the one hand, and determining counterparts and redshifts for detected sources in a complete and reliable manner on the other hand. In this work we have thus assembled and exploited the widest area Spitzer and Herschel extragalactic surveys to select IR galaxy samples in a complete and reliable manner, and the best UV/Optical/NIR ancillary data to identify them. Thanks to Spitzer and Herschel observations we are now able to reliably sample the IR bolometric luminosity of local sources and thus provide important insights into dust obscured star formation activity across cosmic time. Even with the best data sets, however, accurately constructing the luminosity function remains a tricky pursuit, since the presence of observational selection effects due to e.
g. detection thresholds in apparent magnitude, colour, surface brightness or some combination thereof can make any given galaxy survey incomplete and thus introduce biases in the luminosity function estimates. Only a comparison of results coming from different luminosity function estimators applied to the same samples can ensure we can assess the impact of these biases in a robust manner. Armed with the Spitzer Data Fusion, we were able to describe the 0. 02 < z < 0. 5 local luminosity function of sources selected in wide fields by Herschel SPIRE imaging. We fully exploited the multi-wavelength information collected within the Spitzer Data Fusion to perform a SED fitting analysis of SPIRE sources and thus estimate the monochromatic rest-frame luminosities at 250, 350 and 500 µm as well as the IR luminosity between 8 and 1000 µm.
ACKNOWLEDGEMENTS LM acknowledges support from the  Science and Technology Facilities Council  under grant  ST/J001597/1. LM, MV and AF acknowledges support from  ASI "Herschel Science"  Contracts  I/005/07/1  and  I/005/11/0. Mattia Negrello  produced additional predictions based on his models. JW acknowledges the  Dark Cosmology Centre  funded by the  Danish National Research Foundation. MV acknowledges support from the  Square Kilometre Array South Africa  project, the  South African National Research Foundation and Department of Science and Technology  ( DST/-CON 0134/2014 ), the  European Commission Research Executive Agency  ( FP7-SPACE-2013-1 GA 607254 ) and the  Italian Ministry for Foreign Affairs and International Cooperation  ( PGR GA ZA14GR02 ). NS is the recipient of an  ARC Future Fellowship.
AF acknowledges support from the  ERC  via an  Advanced Grant  under grant agreement no. 321323-NEOGAL. This work makes use of STILTS http://www. starlink. ac. uk/stilts/ and TOPCAT (Taylor  2005 ). SPIRE has been developed by a consortium of institutes led by  Cardiff University (UK)  and including  Univ. Lethbridge (Canada) ;  NAOC (China) ;  CEA ,  LAM (France) ;  IFSI ,  Univ. Padua (Italy) ;  IAC (Spain) ;  Stockholm Observatory (Sweden) ;  Imperial College London ,  RAL ,  UCL-MSSL ,  UKATC ,  Univ. Sussex (UK) ; and  Caltech ,  JPL ,  NHSC ,  Univ. Colorado (USA). This development has been supported by national funding agencies:  CSA (Canada) ;  NAOC (China) ;  CEA ,  CNES ,  CNRS (France) ;  ASI (Italy) ;  MCINN (Spain) ;  Stockholm Observatory (Sweden) ;  STFC (UK) ; and  NASA (USA).
Facebook, Twitter, Instagram, and other players in the social media world have been on the rise during the last couple of years. In contrast to their popularity, their underlying business models are vague and often only linked to advertising. In this explorative study we identify new revenue sources for social media service providers besides advertising. Based on three use cases with Facebook, Tencent, and LinkedIn, we identify three possibly fruitful ways to extend existing social media business models. Subsequently, a survey with 301 respondents changes perspectives on the user's willingness to pay in order to identify usage-related differences evoked by cultural and external circumstances. Four derived hypotheses lead the way to avenues of further research especially in terms of Big Data analytics with new e-commerce trends like Facebook's Buy Button.
g. ,  [8] ). Until now, information systems (IS) or marketing research has not yet focused on operationalization of alternative revenue sources that trigger users' perceptions appropriately. In this empirical study we analyze different existing revenues, seeking to answer our main research question: Which revenue sources besides advertising should social media service providers utilize? Directly linked to this question are the underlying techniques in terms of suitable Big Data analytics routines  [9]. The structure of this study is as follows. We first briefly discuss the term social media and its cognates as well as advertising in the context of this study (Section 2). Then we explain our research method, which contains three case studies of Facebook, Tencent, and LinkedIn and a survey with 301 participants (Section 3).
A more common variation nowadays is user-related advertising. It is a kind of targeted advertising that exploits user data to personalize the ads shown with the goal of increasing the click through rate and the conversion rate. It entails elements of users' data (e. g. , language and location)  [15] , browser history (by means of cookies saved in browsers), and social media activities. All of these social media related activities are tracked, recorded, and registered into a social graph, which provides advertisers with relevant data. However, a problematic "industry-driven obsession with the 'social graph'"  [16]  can occur if the underlying Big Data analysis procedures are too technical and lead to invalid interpretations of activities. Such activities may comprise the action of adding contacts, Facebook Likes on a page or post, as well as comments on other users' timelines and actions that involve online applications such as games, music, or news.
We additionally created user accounts on all three services to gain an overview of all offered functions and services that could potentially serve as alternative revenue sources for the service providers. After the selection of Facebook, Tencent, and LinkedIn (Step 1), this identification of revenue streams ties in afterwards as a second step within a logical chain of evidence. We then distributed a questionnaire to a nonspecific target group of social media users in a second part of the data collection phase (B) to identify their willingness to pay for additional services and functions (Step 3). The questionnaire itself is separated into three main sections and follows a funnel approach design  [27] , starting with questions on the demographic and socioeconomic situation of the participants.
Limitations and Further Research In general, we could prove that users are willing to pay for additional features within social media services. However, this environment is absolutely fast moving with new ideas, functions, and implementations aspiring daily, like Facebook being on the brink of introducing its Buy Button after trying out several precursors. We had to set limits on this initial explorative approach, and we think that in addition to leading players like Facebook, Tencent, and LinkedIn, more and new emerging social media services like Instagram, which struggles to make money at the moment, should be examined as well. From the empirical data perspective, we gained a suitable sample size of 301 respondents for this explorative setting. The hypotheses deduced are based on data from individuals from all around the leading social media markets in North America, Asia, and Europe.