dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sets As our purpose is to compare the frequentist methodology to the Bayesian one, we focus on exactly the same data and parameters as in Planck Collaboration XVI (  2013 ) and refer the reader to Planck Collaboration XV (2013) for their exact definitions. Since the CMB, baryon acoustic oscillation (BAO), and CMB-lensing data were found to be in excellent agreement, we will consider the following likelihood combinations. The CMB data set consists of the following likelihoods: the Planck 2013 data in both low and high ranges; the WMAP low-polarization data (referred to as WP in the Planck paper); the SPT  (Reichardt et al. 2012 )+ACT  (Das et al. 2013)  highdata, referred to as highL. The combined likelihood, obtained by multiplying the three, includes 31 nuisance parameters, related to the characterization of the unresolved foregrounds, the effective beam, and to the inter-calibration of the Planck and highL power spectra.
The BAO data set consists of a Gaussian likelihood based on the scale measurements from the 6dF  (Beutler et al. 2011) , SDSS  (Padmanabhan et al. 2012) , and BOSS  (Anderson et al. 2012 ) experiments, combined as in Planck Collaboration XVI  (2013). For the neutrino mass case, we will also use the Planck lensing likelihood (Planck Collaboration XVII 2013), based on the measurement of the deflection power spectrum.
The base ΛCDM model We begin by revisiting the global best-fit solution (MLE) using this new minimizer, over all 37 parameters, on the CMB and CMB+BAO data sets. We use the Hubble constant (H 0 ) instead of the CMB acoustic scale (θ MC ), which is not available within class, and z re instead of τ since it is more stable as discussed in Sect. 2. 1. The new minimum is given in Table  3  and compared to the results previously released in Planck Collaboration XVI (2013) , which were obtained with another minimizer. 6 In both cases we find a slightly lower χ 2. On the cosmological side we find very similar parameters, except for z re which is slightly shifted. On the nuisance parameters side, results are also similar, but we are now sensitive to the SZ-CIB cross-correlation parameter ξ tSZ-CIB while the Planck Collaboration minimum was not shifted from its zero initial value.
Additionally the estimated kinetic SZ amplitude A kSZ is more stable when including the BAO data set. We then build the profile likelihoods by scanning each cosmological parameter and computing the χ 2 minimum in the remaining 36 dimensions at each point. Fig. 2  shows the reconstructed profiles. They are found to be mostly parabolic, but we still fit them with a third-order polynomial in order to measure any deviation from a symmetric error, and threshold them at unity in order to obtain the 68% frequentist confidence level interval as explained in Sect. 2. 3. Results are reported in Table  4  and compared there to the Planck Collaboration posterior distributions. In most cases the values and errors we obtain are in good agreement with the Bayesian posteriors, demonstrating that the Planck Collaboration results, for the ΛCDM model, are not biased by a particular choice of parameters (implicit priors) or by the marginalization process (volume effects).
66 to 0. 84). How can the addition of new information weaken the limit? Is this an effect of the Bayesian methodology, which computes credible intervals and where such effects may arise when combining incompatible data? Naively, in a frequentist analysis adding some information (in the Fisher sense, see James 2007) can only lower the size of confidence interval, since the profile-likelihood "error"  (its curvature at the minimum) can only decrease and thresholding it at a constant value should only lead to a smaller region. We construct the profile likelihood for m ν. It is shown in Fig. 3  for the CMB, CMB+lensing and CMB+lensing+BAO data sets. We observe an intriguing feature with the CMB data set. Even though the parabolic fit of the profile likelihood is poor, the minimum lies at about -2.
(6) We then search for an interval [x 1 , x 2 ] such that R(x 1 ) = R(x 2 ) and x 2 x 1 L(x|µ)dx = α, (7) with α = 0. 95 as the confidence level. These intervals are tabulated in  Feldman & Cousins (1998). We obtain the confidence interval [µ 1 , µ 2 ] for each x = m 0 /σ ν extracted from the parabolic fit to the χ 2 profile as given in Table  5. The upper limits are then simply µ 2 × σ ν. We give our final results in Table  6  and compare them to the Planck Bayesian ones of Planck Collaboration XVI (2013). The agreement is impressive, despite the use of two very different statistical techniques. Finally, we varied the range of points used in the parabolic fit and the limits we obtain are always lower than the one reported in Table  6 , meaning that our results are conservative. 3. Neutrino mass profile likelihood for the CMB (red), CMB+lensing (blue), and CMB+lensing+BAO (green) data sets.
Conclusion The use of Bayesian methodology in cosmology is partly motivated by the fact that one observes a single realization of the Universe, while, in particle physics, one accumulates a number of events which leads more naturally to using frequentist methods. This argument is of a sociological rather than scientific nature, and nothing prevents us from using one or the other methodology in these fields. We demonstrated that a purely frequentist method is tractable with the recent Planck-led high-precision cosmology data. It required lowering the numerical noise of the Boltzmann solver code and we have provided a set of precision parameters for the class software that, in conjunction with a proper Minuit minimization strategy, allowed us to perform the roughly 40 parameter optimization efficiently.
We re-determined the maximum likelihood solution, obtaining essentially consistent results but with a slightly better χ 2 value. We built profile likelihoods for each of the cosmological parameters of the ΛCDM model, using the CMB and CMB+BAO data sets, and obtained results very similar to those from the Bayesian methodology. This confirmed, in this model, that the Planck results do not depend on the choice of base parameters (implicit priors) and are free of volume effects in the likelihood projection during the marginalization process. When including the neutrino mass as a free parameter, the profile likelihood helped us to understand why the computed upper limit increases when including the extra information from CMB lensing. This is not due to the Bayesian methodology, but is related to the physical boundary m ν > 0.
Acknowledgements. We thank  F. Le Diberder  for discussions about the Feldman-Cousins method. We gratefully acknowledge  IN2P3 Computer Center  (http://cc. in2p3. fr)for providing the computing resources and services needed to this work. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN  and  JA (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). A description of the Planck Collaboration and a list of its members, including the technical or scientific activities in which they have been involved, can be found at http://www.
A) Preliminary study When analyzing a depth video bitstream coded in an Intra configuration using HTM-0. 3 (reference software for 3D-HEVC) and the same testing conditions as described in Section IV-A, we find that the Intra mode signaling (including DMMs) represents 25 of the total depth bitrate. It is the element that has the largest coding cost in depth videos. Hence, there is much to gain if the bitrate needed to code the depth Intra modes is reduced. The MPM tool reduces this cost. In an MVD system, a depth video is always associated with a texture video, and we can further improve the efficiency of the MPM tool if we exploit the dependency between the Intra modes of texture and depth. In practice, this means that an additional MPM candidate or predictor, which is the Intra mode of the corresponding texture PU, is added to the MPM candidate list of a depth PU.
A) Experimental setting In a previous work  [15] , we had implemented our tool in HM-3. 3, the HEVC reference software. We used a traditional "2D" video codec to compress depth sequences. Exploiting inter-component correlations was entirely left to the proposed Intra mode inheritance tool. Moreover, the depth coding mode was selected to minimize the depth distortion, which is not the optimal strategy. A first new contribution of the present paper is that we implement our tool in a full 3D codec: HTM-0. 3  [16] , based on the HM-3. 0 reference software for HEVC. It includes multiview functionalities (inter-view prediction) and other inter-component coding tools, on top of which our tool is added. One of the most important functionalities of the software is the view synthesis optimization (VSO) tool.
B) Proposed criterion We have developed a new criterion, DominantAngle, that takes into account the direction of the edges present in the texture PUs. To compute the metric associated with the criterion, we first perform a gradient calculation on the texture PU. Besides the gradient module, we also compute its angles A as follows: A = arctan G y G x. ( 2 ) The module matrix shows the magnitude of the edges in the texture PU. The angles matrix gives the direction of these edges. For the computation of the metric, we establish the histogram of these angles, but only the angles corresponding to edges with a relatively high module value (50 in our method, as in GradientMax) are considered. The histogram will thus list the number of occurences of the angles corresponding only to sharp edges. To establish the histogram, a number of bins has to be set.
A) Experimental setting We implement DominantAngle in the same experimental framework and testing conditions as the GradientMax criterion (see Section IV-A) to allow for a fair comparison between the two. The threshold for each PU size is empirically determined. These thresholds are fixed and are known by both the encoder and decoder so they do not need to be transmitted. In HTM-0. 3, PU sizes vary from 64 × 64 to 4 × 4. Hence,  there are five different PU sizes, and five different thresholds to determine. Here also, the optimization is done only on the first frame and the best thresholds obtained are used to code the rest of the frames. Indeed, the optimization process here is even more complex than in GradientMax since it has to be done independantly for each PU size. Thus, in this section, we also present coding gains on the first frame, and on the entire set of frames.
The best value of the threshold is 50, which gives 1. 3 rate reduction. If this value is halved, we still have 0. 9 rate reduction. On the other hand, when the threshold is doubled we only gain -0. 3, and when it is quadrupled, all the gains are lost. We conclude that the Gra-dientMax method, not only has a smaller best gain, but is also a bit less robust with respect to the threshold selection. When further analyzing the gains we obtain on SV for each proposed criterion, considering the more realistic scenario where we code all the frames, we can see (from Table  7 ) that the results are somewhat coherent across sequences. The standard deviation equals 0. 50 for Dom-inantAngle and 0. 29 for GradientMax which are relatively small values. For DominantAngle, the gains vary between 50 and 210 of the average gain value whereas in GradientMax the gains vary between 56 and 144.
It was therefore not possible to assess the accuracy of dosing in either group. A study in Malawi showed that intermittent treatment provided dosages that were sometimes too low, and weight bands used for prescribing needed to be re-examined. 13 he study has a number of additional limitations. This was a retrospective study and concerns about the completeness and validity of the data have been mentioned. Many factors in addition to drug action, such as drug management and patient behaviour, can influence treatment outcomes. As this is a before-and-after study, the treatment groups represent different time periods. Data for all registered patients could not be collected due to unavailability of folders and incomplete recording in some treatment centres. Human immunodeficiency virus (HIV) co-infection is known to be associated with worse outcomes  14, 15  ; however, HIV data were not available for this study.
However, HIV is unlikely to be a major factor for determining treatment outcome in Fiji, as HIV prevalence among TB patients in Fiji is relatively low. In 2012, 58% of all TB cases were HIV-tested and 4% were HIV-infected. 1  The sample size was only sufficient to show considerable worsening of treatment outcomes with the introduction of FDCs, and given the excellent outcomes at baseline with single-drug preparations, was certainly insufficient to show significant improvements. Finally, the data of TB relapses following treatment were not recorded, and some studies have shown higher relapse rates in those receiving FDC treatment. 16, 17 he study also has strengths. Data were collected from the three treatment centres in Fiji and represent around 80% of the national TB caseload during that time period; the majority of the patient records were validated against treatment cards.
Study population and design Data were collected as part of a randomized placebo-controlled trial investigating the impact of multi-micronutrient fortified rices on health and development of Cambodian schoolchildren (FORISCA UltraRice+NutriRice study, FOrtified RIce for School meals in CAmbodia). Baseline data collection was conducted in November 2012 in 20 primary schools from 5 districts of Kampong Speu province in Cambodia. Kampong Speu is one of the 23 provinces of Cambodia, situated 60 km west of the capital Phnom Penh. Agriculture is predominant, with rice farming as the main occupation and income source. The schools were randomly selected from primary schools participating in school meal or take-home ration programs of the UN World Food Program (WFP). Children attending the selected schools were eligible to be part of the study if they were between 6-16 y of age, had written informed consent from parents/caregivers and did not have any mental or severe physical handicap.
In each school, 132 children were randomly selected after stratification by sex and grade, hence 2640 children. 197 children were not recruited because they were absent on the day of data collection or refused to participate. Hence, a total of 2443 schoolchildren aged 6-16 y participated in the study. The study was approved by the National Ethic Committee for Health Research (NECHR) of the Ministry of Health, Phnom Penh, Cambodia, the Ministry of Education, Youth and Sports, Phnom Penh, Cambodia, and the Research Ethics Committee of PATH, Seattle, USA.
Anthropometric measurements Height and weight were measured using standardized procedures  [17, 18]. Children wore minimum clothing and no shoes. Height was measured in duplicate to the nearest 0. 1 cm with a wooden stadiometer. Weight was measured once, to the nearest 0. 1 kilogram using an electronic scale (Seca, 881 U, Germany). Zscores for height-for-age (HAZ) were calculated with AnthroPlus software version 1. 0. 4 using the WHO 2007 standards. Moderate and severe stunting were respectively defined as 23,HAZ,22 and HAZ,23 respectively.
Data management and statistical analysis Data entry, including quality checks and validation by double entry of questionnaires, was performed with EpiData version 3. 1 Association, Odense, Denmark). Data management and analyses were performed using SPSS version 20. 0 software (SPSS, Inc. , Chicago, IL). Normality of distributions was evaluated using Kolmogorov-Smirnoff test. Not normally distributed data were considered to be reasonably close to normality to allow parametric tests when skewness and kurtosis values ranged between 21. 0 and.
Methodology This paper reports findings from the first stage of an ongoing project in which the unit of analysis is the organization. These data were captured from three, in-depth case studies conducted at three different large organizations in three different industries. To understand how the CCs are formed and evolved, we sought to understand the viewpoint of the key stakeholders that are important decision makers in forming and shaping CC. Since case studies allow the researcher to become familiar with the data in its natural setting and the context  [23]  and allows for a deeper understanding of a particular phenomenon  [24] , we chose this research approach to maximize the richness and accuracy of data, transferability of the findings and to identify candidate constructs and variables for follow on study.
For this paper, we selected three large distinct institutions in South East USA to explore and investigate the emergent conditions and evolution of governance structure. Interviewing, snowball sampling and coding were done simultaneously as is the practice in qualitative research where data collection and analysis is intertwined  [26]. Our data collection has involved on-site observation, structured and semi-structured interviews, document collection, and follows up interviews after initial data coding. We conducted semi-structured interviews based on an interview template developed beforehand and approved by our university's Institutional Review Board (IRB) human subjects research protocols by the study team and pre-reviewed with key informants who were experienced project leaders for ERP and BI implementations projects as well as ongoing support efforts.
Interviews were recorded and transcribed. During coding fields notes and other data were compared to the transcriptions. The semi-structured interview questions were designed to solicit the participant's recollection of the formation of CC and evolution of CC in their own words without "guiding" them through it. In each instance at least two of the study investigators were present. The protocol involved having one of the researchers responsible for capturing field notes and monitoring the trajectory of the interview. When offered additional evidence in the form of diagrams, policy documents, organograms and the like were also collected. Where possible these data were augmented by publicly available documents. The transcriptions have been annotated and enriched by reference to these additional data.
Our findings are derived from the analysis of third level of coding. Stage 1: Our Starting Point -Leximancer Analysis. Leximancer, a latent semantic analysis software tool, converts text to bit patterns and then presents a complete concordance of terms in the text(s) under study. Researchers can choose to remove, exclude and combine terms for further analysis. The tool then conducts various analyses on these patterns identifying measures such as frequency of occurrence, frequency of cooccurrence with other words (bit patterns) thus identifying idioms that are also coded for frequency of occurrence and co-occurrence and other measures of interconnectedness. These are presented in tables, including frequencies and linking concepts to the actual underlying text and as concept maps displaying the relative strength of its existence and relationship measures along different thresholds.
As is typical in intensive qualitative studies, the researchers are immersed in the data. Analysis and sensemaking is a continuous versus a discrete process that arises from the continuing engagement with the data. Two formal approaches are being used to further interrogate the textual data. Researchers used independent methods for coding the transcripts and later exchanged notes to check for intercoder reliability with NVivo and the other MS Word as organizing and coding tools. In both instances open coding techniques described by Strauss and Corbin  [29]  were applied, where textual data were converted into codes that expressed or described specific categories, sub categories, or concepts. This allow us to further explore and develop the concepts initially identifies in the Leximancer analysis.
The genome sequence was obtained using a combined wholegenome shotgun and 8-kb paired-end strategy with a 454 GS FLX Titanium pyrosequencer at INDEAR (Argentina), resulting in a 21-fold genome coverage. Sequencing reads were de novo assembled (Newbler v 2. 8), resulting in 6 scaffolds (Ͼ160 kbp each; N 50 , 1,908,534 bp). The closure of the gap intra-and interscaffolds was achieved by detailed observation of relevant sequencing reads using the Geneious R7 software platform  (14). Optical mapping analysis was performed with an OpGen Argus optical mapper at TGAC (United Kingdom) to validate the final assembly. In agreement with the bioinformatic data, pulsed-field gel electrophoresis (PFGE) analysis of total DNA revealed the presence of six replicons in A. brasilense Az39, defined as one chromosome, three chromids, and two plasmids.
Nucleotide sequence accession numbers. The complete genome sequence of Azospirillum brasilense Az39 is available at NCBI GenBank under the accession numbers CP007793 for the chromosome and CP007794 to CP007798 for the other replicons. para la Investigación Científico Tecnológica (FONCyT), the Ministerio de Ciencia y Tecnología de la República Argentina (MINCyT) and MINCyT-FWO Cooperation Program, and the Spanish Ministerio de Ciencia e Innovación in the Programme Consolider-Ingenio (CSD2009-0006), including the ERDF (European Regional Development Funds) and the Biotechnology and Biosciences Research Council (BBSRC). D. R. and R. M. are recipients of a doctoral fellowship grant from CONICET, J. G. is a recipient of a doctoral fellowship grant from FOMCyT, and S. S. is a recipient of a postdoctoral fellowship grant from Research Foundation Flanders.
Numbers 1 -12 refer to loci used in the analysis of SNP associations. 2 ). PCR primers are given in the electronic supplementary material. All sequence traces were edited using SEQUENCHER 4. 6 (Gene Codes Corporation). SNPs and their allele frequencies were counted using the SNPatron Perl script  [23]. Genotype-phenotype associations were investigated with the R package SNPassoc  [24] , using the genetic model that assesses the association of each allele with a given variable site by testing the homozygous and heterozygous state of the major allele versus the homozygous alternative state against the phenotype. The expectation of a SNP associated with a phenotypically dominant morph is that it only occurs in the dominant phenotype, most likely in a heterozygous state, and is absent from all phenotypes recessive to this phenotype.
Only SNPs for which the major allele occurred at a relative frequency of less than 0. 97 were analysed, with each response variable (phenotype) tested against those lower in the dominance hierarchy combined. The test is based on a likelihood ratio test against permutated data. Bonferroni corrections for multiple tests were applied. Linkage disequilibrium (LD) was calculated using the composite likelihood method first described by Weir  [25]. This method can be applied on sequence data of unknown phase, as generated here. Calculations were performed with the package RxC  [26]. Significance of the allelic correlations was obtained using permutation tests, and Bonferroni corrections for multiple tests were applied. A custom Perl script was used to generate a LD heat map.