dataesr/scientific-paragraphs-categorization huggingface dataset:
The Fisher matrix and the nonlinearity scale We consider galaxy number counts as functions of the observational direction and the observed redshift, ∆(n, z). In Ref. [3]  we describe how the code CLASSgal calculates the corresponding power spectrum C (z 1 , z 2 ). We shall consider these spectra as our basic observables and assume that different -values are uncorrelated. The truly observed spectra have finite resolution in redshift, and are of the form C W (z, z ) = W (z 1 , z, ∆z)W (z 2 , z , ∆z)C (z 1 , z 2 )dz 1 dz 2. (3. 1) Here W (z 1 , z, ∆z) is a normalized window function centered around z with half-width ∆z. We shall use Gaussian and top hat windows (with half-width ∆z). Here C W are the Legendre coefficients of the smoothed angular correlation function ξ W (θ, z, z ) = W (z 1 , z, ∆z)W (z 2 , z , ∆z)ξ(θ, z 1 , z 2 )dz 1 dz 2.
The nonlinearity scale Our code CLASSgal uses linear perturbation theory, which is valid only for small density fluctuations, D = δρ m /ρ m 1. However, on scales roughly of the order of λ 30h -1 Mpc, the observed density fluctuations are of order unity and larger at late times. In order to compute their evolution, we have to resort to Newtonian N-body simulations, which is beyond the scope of this work. Also, on non-linear scales, the Gaussian approximation used in our expression for likelihoods and Fisher matrices becomes incorrect. Hence, there exists a maximal wavenumber k max and a minimal comoving wavelength, λ min = 2π k max beyond which we cannot trust our calculations. There are more involved procedures to deal with non-linearities instead of using a simple cutoff, see e. g. [23]. However, in this work we follow the conservative approach of a cutoff.
Methodology We determine the FoM for the joint estimation of the Hubble parameter H 0 and of the CDM density parameter ω CDM , marginalized over the other cosmological parameters (ω b , A s , n s ). We do the same for the joint estimation of the baryon density parameter ω b and ω CDM , marginalized over (H 0 , A s , n s ). We adopt two different non-linearity scales, λ min = 34 Mpc/h and λ min = 68 Mpc/h. We assume an angular resolution of about 3 arc minutes such that in addition to the condition given in Eqs. (3. 6, 3. 7) we impose max ≤ 3000. Note that we do not combine with known datasets like Planck to minimize the uncertainties on parameters. The goal here is not an optimal prediction of the improvement on cosmological parameter estimation by Euclid (for this we would need a detailed treatment of instrument errors).
A. Tools, models, and data processing The simulations have been performed with the tracking code PLACET  [27]  and in parallel with the ATF2 Flight Simulator  [28]. The results have been checked against each other and only negligible differences could be observed. The beam was tracked through the lattice of version 5. 1, which corresponds to the nominal beam optics with the horizontal beta function increased at the interaction point by a factor 10, which was in use at the time the experiment was conducted. To evaluate the effect of ground motion on the beam orbit, a ground motion generator was used to create realistic element misalignments on a pulse-to-pulse basis. The generator originates from the work in  [2]  and has been integrated into PLACET  [26]. The applied ground motion model has been established from measurements along the ATF2 beam line  [29].
A. Measurement setup and data analysis The experimental setup at ATF2 is depicted in Fig. 8. It consists of three parts: (1) BPM system to measure the orbit jitter; (2) ground motion data acquisition system; and (3) synchronization signal. Two types of BPMs are installed: strip line BPMs and cavity BPMs. For the experiment mainly the cavity BPMs  [32]  are used, since the stripline BPMs are not sensitive enough for the ground motion effects. The BPM data are made available via the distributed control system EPICS  [34]. The collected data are then used in the offline data analysis. To measure the ground motion vibrations, 14 seismometers (GM) of the type CMG-6T from Guralp have been installed along the beam line. These seismometers are specified to measure in a frequency range from 0. 03 Hz to 100 Hz  [23].
The number and the locations of the sensors have been chosen with the help of simulations (see Sec. III). The seismometers have been either installed directly on top of certain quadrupole magnets, or close to them depending on the available space and security considerations (see Table  I  for details). The vibration sensors are connected via well shielded cables to a National Instruments controller of the type PXI 8109 RT. This device is equipped with the digitizer card 6289, also from National Instruments. Here the analog vibration signals in the horizontal and vertical direction are recorded. A Labview program was developed that controls the acquisition process. The stored ground motion data are used in the offline analysis together with the BPM data. It should be mentioned that below 0.
2 Hz, the sensor noise contributions are high  [25]. Therefore, lower frequencies have been suppressed by applying a second-order Butterworth high-pass filter in the offline analysis. The BPM measurements are triggered by the beam arrival that occurs at a repetition rate f R of 3. 12 Hz. The ground motion digitization, on the other hand, is triggered by the internal clock of the National Instruments at a repetition rate of 1024 Hz. It is necessary to select the recorded ground motion data that are closest in time to the beam arrival times. Therefore, a synchronization signal was set up that is also recorded by the National Instruments digitizer. The synchronization signal indicates the beam arrival time, and it is used for the ground motion data selection. To achieve a secure synchronization the experiment is conducted in the following way.
The beam is only turned on after the data acquisitions of the BPMs and ground motion sensors with synchronization signal have already been started. This switching on of the beam is clearly visible in the data of both measurement systems, due to the following reasons. In the BPM system, the missing beam will result in very large measurement noise that can be easily identified in the offline analysis. With the National Instruments equipment, the ground motion measurements as well as the synchronization signal are recorded. As is shown in Fig. 8 , the synchronization signal has pulses at the kicker trigger times, only if the beam charge is above a certain threshold value. In the offline analysis, these recorded pulses can be detected and the corresponding ground motion data can be extracted (downsampling from 1024 Hz to 3.
Global and regional totals for the year 2005 from the most recent emission inventories GFEDv2 (van der  Werf et al. , 2006 ), GFEDv3 (van der Werf et al. , 2010) , and GFASv1  (Kaiser et al. , 2012)  are displayed in Table  2. Note the large differences in continental-scale emissions between the inventories and the large interannual variability in the datasets. Bio-Figures.
For a long time, global models have underestimated carbon monoxide concentrations in the NH as is comprehensively shown in the model intercomparison by  Shindell et al. (2006). Their multimodel mean from 26 global models exhibited large underestimates of NH extratropical CO, particularly during winter and spring when compared to NOAA GMD station measurements. Such a bias is still existent in more recent model studies like  Elguindi et al. (2010) ,  Huijnen et al. (2010 ), Fisher et al. (2010)  and  Williams et al. (2013). With the growing availability of atmospheric CO observation data from surface and in-situ observations as well as from satellites, also a number of global model inversions have been performed to derive optimized CO sources from these observations  (Bergamaschi et al. , 2000; Pétron et al.
MOZART MOZART as used in the MACC project is based on the MOZART-3 model code  (Kinnison et al. , 2007)  which itself is an extension of the tropospheric chemical transport model MOZART2  (Horowitz et al. , 2003)  to the stratosphere and mesosphere. Some features have been added from MOZART4  (Emmons et al. , 2010) : most notably a detailed isoprene degradation scheme as described in  Pfister et al. (2008)  and a description of SO x and the nitrogen cycle. Other improvements have been introduced with the model version presented in this paper including an update of chemical rate constants as well as improvements in the photolysis lookup-table approach and the parameterization of polar stratospheric clouds. In the following we will present the model set-up used in this study, focussed on the model's lower boundary conditions.
This simulation utilized the same dry deposition parameterization as in MI-DEP. All model simulations were analyzed for the year 2008 but started at 1 July 2007 to allow for a spinup period. The model was run at T63LR resolution (1. 875 • × 1. 895 • ) and with 60 vertical levels from the surface up to 0. 1 hPa. Tracer initial conditions were taken from the MACC reanalysis which included assimilation of CO, O 3 , and NO 2 satellite information. It should be noted that 2008 was within the three years with lowest atmospheric CO concentrations out of the 13 yr 2000-2012 where satellite observations were available  (Worden et al. , 2013). CGG) operated by the WMO GAW programme (http://ds. data. jma. go. jp/gmd/wdcgg/). In total we used monthly means of CO concentrations measured from 67 surface stations which provided data for all months in 2008, their positions on the globe are shown in Fig.
MOZAIC profiles The MOZAIC (Measurements of Ozone and water vapor by Airbus In-service airCraft) program collects ozone, CO, odd nitrogen (NO y ) and water vapor data, using automatic equipment installed on-board several European long-range passenger airliners flying regularly all over the world  (Marenco et al. , 1998). Here we use CO data from vertical tropospheric transects taken in the vicinity of airports during ascent and descent. For more details of the data processing see  Elguindi et al. (2010). In 2008, only three aircraft were operating with the MOZAIC instrumentation and therefore only a limited number of airports were visited regularly (13 airports with a total of 935 profiles). in Fig. 4. The model results were interpolated to the times and locations of the MOZAIC observations, afterwards the single profiles were combined to monthly means.
MOPITT atmospheric columns The MOPITT (Measurement Of Pollution In The Troposphere) instrument is flying on board the NASA Terra satellite and has been operational since March 2000  (Drummond and Mand, 1996; Deeter et al. , 2004). It measures upwelling radiation in the thermal infrared spectral range using gas-filter correlation radiometry. At nadir view, MOPITT offers a horizontal resolution of 22 km × 22 km and allows a global coverage in two to three days. MOPITT data used in this study correspond to the daytime CO total columns from the level 2 version 5 product. MOPITT version 5 offers CO data retrieved in both near-infrared (NIR) and thermal-infrared (TIR) radiances  (Deeter et al. , 2013). The MOPITT products are available through the following web server: http://www2. acd. ucar. edu/mopitt/products.
For evaluation we use the multispectral CO product which is based on both NIR and TIR radiances. Exploiting TIR and NIR radiances together increases the sensitivity to CO in the lowermost troposphere  (Worden et al. , 2010; Deeter et al. , 2011 Deeter et al. , , 2012)). The observations are area-averaged monthly means from the regions defined in Fig. 4  (blue boxes). The modelled CO total columns were calculated by applying the MOPITT averaging kernels. Details about the method of calculation are given in  Deeter et al. (2004)  and  Rodgers (2000). estimation approach, implemented in the Fast Optimal Retrievals on Layers for IASI (FORLI) software  (Hurtmans et al. , 2012). The spectra are processed in near real time and the CO profiles are distributed with error covariance, averaging kernels and quality flags information.
MOZART sensitivity simulations In the following, we use MOZART sensitivity simulations to identify the potential role of the various CO surface fluxes for the underestimation of the near-surface concentrations. First we compare our basic simulation (MI), using MACCity anthropogenic and natural emissions, to observations gathered from the World Data Center for Greenhouse gases (WDCGG) database. In January, the model underestimates the concentrations for the arctic stations by about 50 ppb. In the SH model and observations match better, with an overestimation of up to 10 ppb being typically seen. The global distributions of atmospheric CO can be derived with high spatial resolution from the satellite total column observations. As shown in Fig. 6 , the MOPITT and IASI products agree well for most regions.
Emissions First, we compare monthly means of near-surface model concentrations with monthly mean surface station data from the WDCGG database averaged over the Northern and Southern Hemisphere as well as for the regions Europe, North America and East Asia. Model data was interpolated to the station location. In Fig. 7  the model bias is shown for the base simulation MI (MACCity emissions) and for the sensitivity simulations MI+AN, MI+BIO, and MI+BB. The simulation with MACCity emissions (MI) underestimates the observed surface concentrations in the NH in all seasons except summer, with low biases exceeding 20 ppb from December to April and exceeding 40 ppb in January. The largest underestimation of about 75 ppb is found over Europe. During boreal summer, the modelled CO concentrations match on average the observations, with a slight underestimation for Europe and overestimation for North America.
Conclusions Despite the developments in the global modelling of chemistry and of the parameterization of the physical processes, CO concentrations continue to be underestimated in NH winter by most state-of-the-art model simulations. Even with extensive data assimilation of CO satellite retrievals, as it is applied in the MACC forecasts and reanalysis, a significant low CO bias in the lower troposphere remains. Such a bias can in principle originate from either an underestimation of CO sources or an overestimation of its sinks. We addressed both the role of surface sources and sinks with a series of MOZART model sensitivity studies for the year 2008 and compared our results to observational data from ground-based stations, satellite observations, and tropospheric profiles from MOZAIC. Our basic simulation employed the global emission inventories MACCity, GFASv1 and MEGANv2, of which MACCity and GFASv1 have been developed in the MACC project.
ACPD  ACPD ACPD ACPD ACPD ACPD 14 ,  245-301 ,  2014 ACPD ACPD ACPD 14 ,  245-301 ,  2014 ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD  strong support of the  European Commission, Airbus , and the airlines (Lufthansa,  Air-France ,  Austrian ,  Air Namibia ,  Cathay Pacific  and  China Airlines ), who carry the MOZAIC or IAGOS equipment and have performed the maintenance since 1994. MOZAIC is presently funded by  INSU-CNRS (France) ,  Météo-France ,  CNES ,  Université Paul Sabatier (Toulouse, France)  and  Research Center Jülich (FZJ, Jülich, Germany ). IAGOS has been, and is additionally, funded by the  EU projects IAGOS-DS and IAGOS-ERI. The MOZAIC-IAGOS data are available via CNES/CNRS-INSU Ether web site http://www. pole-ether. fr. ECMWF and  Forschungszentrum Jülich  are acknowledged for providing supercomputer time, and we thank  Claire Granier  and  Hugo Denier van der Gon  for helpful discussions.
Transforming the instantiated prefab can control the appearance of the objects prior to cue presentation. For example, the exact position of the presentation can be set, and the presentation can be scaled to compensate for certain factors that may influence cue visibility, such as fixation point and screen size. Furthermore, the objects within the instantiated prefabs can be controlled directly; for example, it is possible to rotate the backward mask (an alternative to using distinct forward and backward masks) or to disable the backward mask entirely (to test the effectiveness of the masking). Unity3D supports extension scripts that allow seamless integration between the underlying game engine and bespoke user-defined components. We implemented various C# scripts that provide behaviors within the environment.
The model of the refrigerator consists of individual components that can each be transformed individually. The scripts select from a collection of specific animations that manipulate the position and rotation of the components to realize many of the expected lowlevel behaviors. The scripts are triggered by events identified from user behavior (using ray casting to identify the target). The animations are controlled in distinction from the physics engine to support integration with deliberative control systems (for example, a planning system). As a consequence of these integrated behaviors, the refrigerator can be fully manipulated: the doors can be opened, the drawers can be pulled out, and the shelves can be repositioned or folded up (which is an area for tall food items). We also implemented several C# classes that managed the transactions involved in the experiment task.
An outline of the system architecture is presented in F4 Figure  4. 2. 1. 4 Materials and Equipment. The experimental software was implemented in Unity3D (version 4. 0. 1f2) and was run on a Dell Precision T7600 computer (CPU: Intel Xeon E5-2609 2. 40 GHz; 32 GB RAM; GPU: NVidia GeForce GTX 680; OS: MS Windows 7 Enterprise 64-bit) with a 24-min Dell U2412M monitor (60-Hz refresh rate, 1920 by 1200 resolution). Participants were seated 65 cm from the screen. The timing of cue presentation was checked by recording presentations with a Panasonic Lumix TZ30 high-speed camera (220 fps) and counting the number of frames the cues were present on the screen. We counted six clear and two partially visible frames for each cue, corresponding to between 27. 27 ms and 36. 36 ms exposition time, which we deemed an acceptable fit to the intended 33 ms.
We used IBM SPSS Statistics Version 20 software for all statistical analyses. Examples of experimental stimuli are presented in F5 Figure  5. Ten food items were used in the study: apple, burger, cheese, fish, lemon, pear, pepper, pie, pizza, and tomato (see Appendix B). Each object was constructed as a 3D model with an average of 500 polygons and detailed using a uniquely mapped 8-bit RGB diffuse and 8-bit grayscale specular texture. Colors used in the diffuse textures were chosen from real-life photographic reference and their histogram levels were clamped so that they could be represented consistently in both RGB color and grayscale within Unity3D. We created a corresponding cue for each object with the following properties: (1) in grayscale, matched for luminosity and contrast across cues, (2) normalized in size, and (3) displayed from an angle so that it appeared roughly circular (see Figure  5a ).
However, research indicates that the effect of masked cues can degrade very quickly with time, and cueing effects can decrease substantially if the subliminal cues are not acted upon in the first second or so  (Dupoux, Gardelle, & Kouider, 2008; Greenwald et al. , 1996; Kouider & Dehaene, 2007). Therefore, we used the collected reaction-time data to refine the analysis of cueing effectiveness. First, we considered only those trials where participants selected an object within one second following the end of cue presentation. The resulting improvement in success rate was substantial and statistically significant. In the 1x condition (222 trials with RT 1 second), success rate improved by 13. 05% (M ¼ 66. 06, SD ¼ 19. 70), t (13) ¼ 3. 051, p ¼. 009, which represents 32% of trials attained above chance and a large effect size (r ¼.
In addition, the cross sections in the LBE physics list make direct use of shell cross-section data [Geant4 collaboration, 2012a]. The cross sections in the LHEP physics list does not take the shell cross-section data into account. It is clear from these differences that the LBE physics list contains more accurate descriptions of low-energy interactions, in particular, between 250 eV and 1 keV. To determine the effect of theses differences on the energy loss of the electrons, we initiated a continuous monoenergetic beam of 50,000 electrons of 1 MeV at a vertical position of 200 m without the influence of an electric field. We then found the vertical stopping position of each individual electron. The result is shown in Figure  9. On average, the electrons in the case of the LHEP simulations make it further than in the case of the LBE simulations.
Visual displays of time-oriented data often lack readability and force the viewer to focus on the methodology and the graphical design rather than on the substance. First of all, we give an illustrated state of the art of existing graphical visualizations used to analyze longitudinal data, presenting step by step four graphical displays implemented in the well-known TraMineR R package  (Gabadinho et al. , 2011a) : sequence distribution plot, sequence index plot, sequence frequency plot and parallel coordinates plot for sequence data. Then, we introduce the proposed "slide plot" visualization. It is a new graphical display which complements the existing ones and is particularly useful to identify dominant trends among individual trajectories of people or spaces. At last, the paper gives a technical description of a web application called SLIDER, implemented using the R software (R Core Team, 2013) and the shiny R package  (RStudio Inc.
Datasets Two datasets are used to exemplify this state of the art: the first one is taken from Mc Vicar's paper about young peoples' transitions from school to work  (Mc Vicar et al. , 2002). The data comes from a cohort survey implemented in Northern Ireland which follows a sample of 712 young individuals aged 16 in 1993. Monthly labor activity is collected during six years, from 1993 to 1998. Six activities are coded: school, further education, higher education, employment, joblessness and training. In this example, the statistical units are persons and the variables are categories of labor activity declared at each time step (we only take into account the activity in September of each year). The second dataset is the land use basemap produced by the planning institute of Île-de-France  (IAU-IdF -Institut d'Aménagement et d'Urbanisme d'Île-de-France).
The data consists of a time series of geolocated polygons describing the land use changes from 1982 to 2008 for the whole Île-de-France region  (IAU-IdF, 2013). Six aggregated land use categories are coded from the original typology of 80 categories: forests, agricultural lands, water, parks and recreation spaces, transportation facilities, built areas. In this example, the statistical units are portions of land and the variables are categories of land use at each time step. Two precisions are needed to understand this example. First, we work on a selection of the portions of land which have experienced a change of land use between 1982 and 2008. This selection represents 7% of the whole area: 833 km2 for a total area of 11,200 km2. Then, each geolocated polygon is weighted by its area. The weighting variable affects all the numeric and graphic outputs.
(1981). It consists of a set of cards with colored edges: each card represents an individual and the colored segments on its edge show the trips and the activities carried out during the day. The following graphical displays (Figure  1 , Figure  2 , Figure  3 , Figure  4 ) illustrate the main existing possibilities to visualize longitudinal data. Each display is applied to both datasetsyoung persons' professional trajectories in Northern Ireland and changes of land use in the Île-de-France region. Display 1 -The first example is the sequence distribution plot (Figure  1 ) which shows, at each time step, the cross-sectional frequencies within the defined categories. This display highlights the main categories at each time step and the evolution of their respective importance during the studied period.