dataesr/scientific-paragraphs-categorization huggingface dataset:
Unfortunately, very little is observationally known on this so-called reheating period. Let us stress that adiabatic initial conditions, as favored from the current CMB measurements, naturally stem from such a setup within single field models. Another constraint is that the reheating temperature, T reh , must be higher than the nucleosynthesis scale (i. e. a few MeV). If, however, one restricts oneself to specific models, then one can obtain better bounds on T reh , as was recently shown for the first time in Ref. [64]. But, so far, these constraints concern a few models only. We see that, despite the fact that it has become a cornerstone, the inflationary era is not as observationally known as the other parts of the standard model of Cosmology. However, there is now a flow of increasingly accurate astrophysical data which gives us a unique opportunity to learn more about inflation.
In particular, the recently released Planck satellite data  [65, 66]  play a crucial role in this process. The mission complements and improves upon observations made by the NASA WMAP satellite  [67, 68]  and is a major source of information relevant to several cosmological issues including inflation  [69, 70]. But the flow of new data does not only concern the CMB. The Supernovae projects  [71] [72] [73] [74]  continue to measure the distances to the nearby exploding SN1A stars while the large scale galaxy surveys such as the Sloan Digital Sky Survey (SDSS)  [75, 76]  are providing an unprecedented picture of the structure of the universe. SDSS is planned till 2014 and has recently provided the measure of the so-called Baryonic Acoustic Oscillations (BAO). They are the red-shifted version of the acoustic oscillations observed in the CMB anisotropies which have been transferred to the galaxy power spectrum.
These ones will be sensitive to the red-shifted 21cm line absorbed by hydrogen clouds before the formation of galaxies  [91] [92] [93] [94] [95] [96] [97]. With such data, we will have a complete tomography of the universe history from the time of CMB emission at the surface of last scattering to the distribution of galaxies today. The main goal of this article is to develop methods that will allow us to constrain the inflationary scenario at a level matching the accuracy of these new data. Since we have now entered the era of massive multi-data analysis, the project aims at a change of scale compared to previous approaches. In particular, one way to deal with this question is to perform systematic and "industrial" studies of this issue. Our ability to see through the inflationary window turns the early universe into a laboratory for ultra-high energy physics, at scales entirely inaccessible to conventional experimentation.
As argued before, the Planck data drive us towards testing inflation with the simplest models first and such a method would therefore need to be implemented for the class of scenarios IA only. More precisely, instead of inferring the posterior distributions of the Hubble flow parameters ǫ n only, as one would naturally do in the approximate approach discussed before, we take advantage of the fact that the ǫ n 's can be computed in terms of the parameters describing the reheating and V (φ). In particular, for each model, this permits a quick and efficient extraction of the posterior distributions of those parameters. In our opinion, this third technique should not be viewed as a competitor of the two others mentioned earlier but rather as complementary and the corresponding results should be compared.
But, precisely, we have shown that ln R rad in the Einstein frame can be expressed according to the standard formula, provided the energy densities in the argument of the logarithm are Jordan frame energy densities. Therefore, in practice, we have ∆N * ∈ ∆N nuc * , ∆N end * with ∆N end * = -N 0 + ln H * M Pl - 1 4 ln ρ end M 4 Pl , (3. 30) where all the quantities in the above equation are calculated in the Einstein frame and, hence, are directly available since they are, by definition, the outcomes of the ASPIC library code. The other limit can be expressed as ∆N nuc * = -N 0 + ln H * M Pl - 1 3(1 + w) ln ρend M 4 Pl - 1 -3w 12(1 + w) ln ρnuc M 4 Pl. (3. 31) The quantity ρnuc is defined in the Jordan frame but its value is explicitly known, see above. On the other hand, we need to evaluate ρend since the code only delivers ρ end.
Then, it increases again, blows up at x V =0 and, finally, asymptotically vanishes when x → ∞. Since inflation proceeds at x > x V =0 it always stops by violation of the slow-roll conditions. Unfortunately is not possible to find an analytic expression for x end but one can provide the following approximated formula, x end ≃ - 5 4β W -1 - 4 × 9 2/5 5 × 8 2/5 α -4/5 β 1/5 3/4 , (5. 55) where W -1 is the Lambert function. It is compared to the numerical solution for x end implemented in the ASPIC code in Fig. 46. The agreement is excellent. Let us now turn to the slow-roll trajectory. Unfortunately, KMIII is one of the rare cases for which it cannot be integrated by quadrature. As such, in the ASPIC library, the slow-roll trajectory is numerically integrated. However, in the large field limit x ≫ β -3/4 , one can obtain an approximate analytic formula given by N end -N ≃ 9 16αβ 2 e βx 4/3 x 2 - e βx 4/3 end x 2 end , (5.
Therefore, according to our classification, LPI is a three parameters model with p, q and φ 0. Finally, the parameter M is fixed by the amplitude of the CMB anisotropies to M 4 M 4 Pl = 720π 2 M Pl φ 0 2 (q + p ln x * ) 2 x 2+p * ln 2+q x * Q 2 rms-PS T 2. ( 6. 82) The reheating consistent slow-roll predictions for the LPI1 models with p = 4 are represented in Figs 182, 183, and 184 for q = 2, q = 1 and q = 3, respectively. The predictions for LPI2 are displayed in Figs 185, 186, and 187 for (p = 1, q = 2), (p = 2, q = 2) and (p = 3, q = 4), respectively. For the LPI3 scenario, the predictions have been plotted in  Figs 188, 189, and 190  for (p = 1, q = 2), (p = 2, q = 2) and (p = 3, q = 4), respectively. One can see that the current CMB data generically require LPI inflation to take place with super-Planckian values for φ 0 while some combinations of p and q are already disfavored at more than two-sigma.
For instance, it does not tell us rigorously which constraints exist on the parameters of a given model. Indeed, suppose that we are interested in LFI, V (φ) ∝ φ p. It is obvious that we would like to know for which values of p this class of models is compatible with the data and for which values it is not. In order to complement the slow-roll approximation and to address the above mentioned issues, we have argued that it is interesting to scan the inflationary landscape model by model and have provided the public code ASPIC to do so. Such a strategy has to be done for all the inflationary scenarios since it would be arbitrary to consider only a restricted class while ignoring the others. In fact, this question deserves to be discussed in more detail. One could indeed imagine that it is not necessary to consider all the models one by one and that considering a representative for each class is sufficient.
This allows us to identify three different regions: ǫ 2 > 0 and 2ǫ 1 < ǫ 2 (region 1), ǫ 2 < 2ǫ 1 (region 2), ǫ 2 < 0 < 2ǫ 1 (region 3). These three regions are identified in Fig. 79  together with Planck and WMAP9 bounds 8. If we use the first order slow-roll expressions, the condition ǫ 2 > 0 is equivalent to r < 8(1-n S ) while ǫ 2 > 2ǫ 1 amounts to r < 4(1n S ). These two lines are also represented in Fig. 79  (solid black lines). We have also superimposed the predictions of LFI, SFI and VHI (upper panel). We see that the three regions defined above roughly correspond to the cases large field, small field and hybrid. However, the correspondence is not perfect and we notice, for instance, that the predictions of VHI can penetrate region 2. 79 ). Despite the fact that they are in the same broad class, the accuracy of the CMB data allows us to discriminate among them thereby justifying a detailed navigation within the inflationary landscape.
Figure2. Observational predictions for the LFI models, V (φ) ∝ φ p , in the plane (n S , r) (i. e. scalar spectral index and gravity wave contribution) compared to the Planck data [66, 69, 70, 133, 134, 153]. Each continuous line and each color represent a different value of p. Along each line, each point (i. e. each small "cross") denotes a different reheating temperature compatible with the constraint ρ end > ρ reh > ρ nuc (the annotations give the logarithm of the reheating temperature in GeV). We see that the details of the reheating stage now matter: along a given line, some reheating temperatures are compatible with the observational constraints while others are not. This means that the CMB observations can now put constraints on T reh.
A. 54 Figure 171. 54171 Figure 171. Reheating consistent slow-roll predictions for the valley hybrid inflation models with p = 0. 5, in the plane (n S , r) (top panel) and the plane (ǫ 1 , ǫ 2 ) (bottom panel). The two pink solid contours are the one and two-sigma Planck confidence intervals (marginalized over second order slowroll). The color of the data points encodes the value of µ, while different data blocks correspond to different values of x end. Inside a given bock, the annotations trace the energy scale at which reheating ends and correspond to log(g 1/4 * T reh /GeV).
The investigation of infectious disease outbreaks relies on the analysis of increasingly complex and diverse data, which offer new prospects for gaining insights into disease transmission processes and informing public health policies. However, the potential of such data can only be harnessed using a number of different, complementary approaches and tools, and a unified platform for the analysis of disease outbreaks is still lacking. In this paper, we present the new R package OutbreakTools, which aims to provide a basis for outbreak data management and analysis in R. OutbreakTools is developed by a community of epidemiologists, statisticians, modellers and bioinformaticians, and implements classes and methods for storing, handling and visualizing outbreak data. It includes real and simulated outbreak datasets.
, 2012) , TreePar  (Stadler and Bonhoeffer, 2013)  and Epi-Estim  (Cori et al. , 2013)  for reproduction number estimation, and outbreaker  (Jombart et al. , 2014)  for transmission tree reconstruction. To ensure coherence between these different approaches and promote further developments, basic tools for storing and handling outbreak data are needed. In order to fill this gap, a community of epidemiologists, modellers, statisticians and bioinformaticians has developed the R package OutbreakTools. Here, "outbreak data" is defined as the above-described collection of data originating from a set of outbreak cases. This software, initiated during a hackathon for the analysis of disease outbreaks in R (http://sites. google. com/site/hackoutwiki/), provides object classes implementing a flexible and coherent representation of outbreak data, alongside procedures to manipulate, summarize and visualize these data.
Results The main purpose of OutbreakTools is to provide a coherent yet flexible way of storing outbreak data. To achieve this goal, a new formal (S4) class 'obkData' (short for 'outbreak data') has been developed. This class uses different slots (Table  1 ) to store individual meta data (e. g. age, sex), time-stamped observations made on the individuals (e. g. fever, swab results, or answers on food exposures from questionnaires), contacts between patients, DNA sequences of the pathogen, phylogenetic trees, and contextual data at the population level (e. g. school closures, climatic variables). Complex data structures such as dynamic contact networks or DNA sequences from different genes are respectively stored using the new classes 'obkContacts' and 'obkSequences'. To promote interoperability, okbData objects can be created from standard input files via procedures already available in R.
Data tables can be imported from text files (extensions '. txt' and '. csv'), from other statistical software using the package foreign (R Core Team, 2013b), or from XML files using the package XML  (Butts, 2008). Aligned DNA sequences in FASTA format can be read using ape  (Paradis et al. , 2004)  or adegenet  (Jombart, 2008; Jombart and Ahmed, 2011) , and phylogenetic trees can be imported from Newick or NEXUS format using ape  (Paradis et al. , 2004). To ensure that obkData objects are readily compatible with other R packages, existing classes have been used for storing data whenever possible: the class 'DNAbin' for DNA sequences  (Paradis et al. , 2004) , the classes 'network' and 'networkDynamic' for contact data  (Butts, 2008) , and the class 'phylo' for phylogenetic trees  (Paradis et al.
, 2004). Considerable efforts have been made to ensure that these different pieces of information are stored in a coherent way. The use of a formal (S4) class system offers multiple advantages in this respect, as it allows one to accurately define the object's content, and to perform consistency checks between the different data sources when the object is created. This means, for instance, that individuals documented in the contact or symptom data will be linked, through unique individual identifiers, to available individual meta-data, or that tips of the trees will be linked to existing DNA sequences whenever possible. Similarly, dates provided in different formats are automatically standardized, and sequences of the same genes are checked for consistent length. As obkData objects allow for coherent data storage and can be saved easily as compressed R objects (using the function save), they also offer a new and efficient way of sharing data amongst collaborators and making studies reproducible after publication.
Despite this complex data structure, accessing information stored in obkData objects is facilitated by a large number of accessors. These functions allow for the retrieval of specific data (get. data), including sampling dates (get. dates), contacts (get. contacts), individual meta-data (get. individuals) or DNA sequences from given genes (get. dna), without requiring knowledge about the internal data structure. Importantly, decoupling the access to information from the internal data storage also ensures long-term code portability: future changes in the data structure will not affect results as long as accessors return the same information. This approach will enable future developments of the obkData class and allow for the incorporation of new types of data. Besides accessors, data handling is also facilitated by a subsetting procedure (function subset) which allows one to isolate data for given sets of individuals, samples, genes, sequences, or from a given time window.
The information contained in obkData objects can be easily visualized using options of the generic function plot, or directly using dedicated functions. Individual timelines can be used to visualize course of illness and collection dates of samples for each individual (function plotIndividualTimeline, Fig. 1 ), maps can be drawn to assess the geographic distribution of the cases (function plotGeo), contact data can be visualized as graphs (function  plotfor obkContacts objects), and genetic data can be visualized as phylogenies (function plotggphy, Fig. 2 ) and minimum spanning trees (function plotggMST). Most of these graphs take advantage of the high-quality customisable graphics implemented in ggplot2  (Wickham, 2009). While OutbreakTools focuses on storing, handling and visualizing data, the package also implements basic tools for data analysis.
Adapted summaries (function summary) have been implemented to provide quick insights into the data, make. phylocan be used to obtain phylogenies for all genes of the dataset, and get. incidence can be used to compute incidence from dates of symptom onsets, but also from any time-stamped data. In the latter situation, positive cases can be defined from either quantitative or categorical data, by specifying a range of numerical values, a list of character strings or even regular expressions. In practice, this allows for the computation of incidence based on any symptom data or sample analysis. This feature therefore allows for a direct use of procedures implemented in R0  (Obadia et al. , 2012)  or EpiEstim  (Cori et al. , 2013)  for estimating reproduction numbers. To illustrate its features, OutbreakTools is released with both simulated and empirical datasets, including 514 annotated DNA sequences of the 2009 influenza pandemic (dataset FluH1N1pdm2009, Fig.
2 ) and data from a large Newmarket (UK) outbreak of equine influenza (dataset HorseFlu; Hughes et al. , 2012, Fig. 1 ). Finally, OutbreakTools also includes a simulation tool (function simuEpi) which allows for the generation of outbreaks (including pathogen genome sequences) under a standard SIR model (Fig. 3 ), and can easily be extended to use other models (e. g. SIS, SEIR). OutbreakTools is documented in a 50-page manual and released with a tutorial introducing the data structures and the main functionalities of the package.
Discussion While a number of packages for infectious disease epidemiology have recently been developed in the R software  (Jombart et al. , 2014; Obadia et al. , 2012; Stadler and Bonhoeffer, 2013; Cori et al. , 2013) , basic tools for storing, handling and visualizing outbreak data have so far been lacking. OutbreakTools fills this gap by implementing new formal classes allowing for a coherent yet flexible representation of disease outbreak data, alongside a number of functions for manipulating and visualizing that data. As such, it represents a significant step towards building a comprehensive platform for outbreak analysis in R. The collaborative and open nature of this project, together with the possibility of modifying internal data structures seamlessly for the user, ensures that OutbreakTools will be able to evolve and adapt to incorporate new types of data and approaches used for outbreak analysis.
Resources Availability: OutbreakTools 0. 1-0 is distributed on CRAN (http://cran. r-project. org/) and available for R 3. 0. 2 on Windows, Mac OSX, and Linux platforms. It can be installed as any other package using the graphical user interface or typing the instruction: install. packages("OutbreakTools") Licence: GNU General Public Licence (GPL) ≥2. Website: http://sites. google. com/site/therepiproject/r-pac/about Documentation: besides the usual package documentation, OutbreakTools is released with a tutorial which can be opened by typing: vignette("OutbreakTools"). More documentation can be found on the project's website. Development.
Acknowledgments We are thankful to  Tanja Stadler  and two anonymous referees for their thorough reviews and useful comments. We thank Sourceforge (http://sourceforge. net/) for providing a great platform for software development. OutbreakTools has been created during "Hackout: a hackathon for the analysis of disease outbreaks in R" (http://sites. google. com/site/hackoutwiki/),an event funded by the  Medical Research Council. T. Jombart  and  C. Collins  are funded by the  Medical Research Council  and  MIDAS. D. Aanensen  is funded by the  Wellcome Trust  (grant  099202/Z/12/Z ). M. Baguelin  and  A. Camacho  are supported by the  Wellcome Trust  (project grant  WR/094527 ). A. Camacho is also funded by the  Medical Research Council  (fellowship  MR/J01432X/1 ). A. Cori thanks the  NIH  for funding through the  NIAID  cooperative agreement  UM1 AI068619.
M. A. Suchard  is supported through  National Science Foundation  grants  DMS 126153  and  IIS 1251151. N. Hens  acknowledges support from the  University of Antwerp  scientific chair in Evidence-Based Vaccinology, financed in 2009-2014 by a gift from  Pfizer. O. Ratmann is supported by the  Wellcome Trust  (fellowship  WR092311MF ). C. Colijn acknowledges support from the  Engineering and Physical Sciences Research Council  ( EP/I031626/1 ;  EP/K026003/1 ). S. Frost is supported in part by the  Royal Society , the  ERSC  ( ES/J011266/1 ), and the  MRC  ( MR/J013862/1 ).
We discuss in detail the overall selection function that results from the combination of the different constituents of the project. This includes the masks arising from the parent photometric sample and the spectroscopic instrumental footprint, together with the weights needed to account for the sampling and the success rates of the observations. Using the catalogue of 53 608 galaxy redshifts composing the forthcoming VIPERS Public Data Release 1 (PDR-1), we provide a first assessment of the quality of the spectroscopic data. The stellar contamination is found to be only 3. 2%, endorsing the quality of the star-galaxy separation process and fully confirming the original estimates based on the VVDS data, which also indicate a galaxy incompleteness from this process of only 1. 4%. Using a set of 1215 repeated observations, we estimate an rms redshift error σ z /(1 + z) = 4.
8. The central goal of this strategy is to build a data set capable of achieving an order of magnitude improvement on the key statistical descriptions of the galaxy distribution and internal properties, at an epoch when the Universe was about half its current age. Such a data set would allow combination with local samples on a comparable statistical footing. Despite being centred at z ∼ 0. 7, in terms of volume and number density VIPERS is similar to local surveys like 2dFGRS and SDSS. All these surveys are characterised by a high sampling density, compared to the sparser samples of the recent generation of BAO-oriented surveys. In this paper we provide an overview of the VIPERS survey design and strategy, discussing in some detail the construction of the target sample. The layout of the paper is as follows: in Sect.
2, we discuss the survey design; in Sect. 3 we describe the properties of the VIPERS parent photometric data and the build-up of a homogeneous sample over 24 deg 2  ; in Sect. 4 we discuss how from these data the specific VIPERS target sample at z > 0. 5 has been selected, using galaxy colours; in Sect. 5 the details of the VIMOS observations and the general properties of the spectroscopic sample are presented; in Sect. 6 we discuss the various selection effects and how they have been accounted for; finally, in Sect. 7 we present the redshift and large-scale spatial distribution of the current sample, summarising the scientific investigations that are part of separate papers currently submitted or in preparation. As a public survey, we hope and expect that the range of science that will emerge from VIPERS will greatly exceed the core analyses from the VIPERS Team.
The outstanding imaging dataset that was available for these purposes was the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) Wide photometric catalogue, as described below in Sect. 3. The desired redshift range was isolated through a simple and robust colour-colour selection on the (ri) vs. (u -g) plane (as shown in Fig. 3 ). This is one of many ways in which we have been able to benefit from the experience of previous VIMOS spectroscopic surveys: we could be confident in advance that this selection method would efficiently remove galaxies at z < 0. 5, while yielding >98% completeness for z > 0. 6, as verified in the results shown below. A precise calibration of this separation method was made possible by the location of the VVDS-Wide (i AB < 22. 5) and VVDS-Deep (i AB < 24) samples within the W4 and W1 fields of CFHTLS, respectively.