dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS The authors thank  Dr Aziz Zaanan ,  Dr Céline Lepère ,  Dr Simon Pernot , and  Dr Anne-Laure Pointet  from  Georges Pompidou European Hospital  for their help with elicitation. The authors thank  Angelika Geroldinger  as well for her useful suggestions. The simulations were performed at the HPCaVe at  UPMC-Sorbonne Université. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: A part of this project was supported by  French National Cancer Institut (INCa)  under grant  INCA_10801. Moreno Ursino  was funded by  INCa  grant  INCA_9539.
Acknowledgements. Research of C. C. and C. L. was supported by the  French "Investissements d'Avenir" program , project  ISITE-BFC  (contract  ANR-15-IDEX-03 ). Research of C. C. was also supported in part by  NSF  grant  DMS-1554456. Research of A. S. was supported in part by  NSF  grant  DMS-1500704. Research of F. S. was supported by  ARC Discovery  grant  DP150100920.
Acknowledgements We would like to thank  Professor Jean-Michel Bismut  for helpful discussions. B. L. is supported by  Science and Technology Commission of Shanghai Municipality (STCSM) , grant No. 18dz2271000. X. M. is partially supported by  NNSFC  No. 11528103 , No. 11829102 ,  ANR-14-CE25-0012-01 , and funded through the  Institutional Strategy of the University of Cologne  within the  German Excellence Initiative.
1. 1. 6. Acknowledgements. For various discussions and remarks, we thank Sylvain Brochard, Xavier Caruso, Brian Conrad, Bernard Le Stum, Brian Osserman, and Tobias Schmidt. We acknowledge the help of the referee to make the article much more incisive. We are also grateful to the CIRM in Luminy where part of this research was done. Finally, the first author would like to thank the executive and administrative staff of IRMAR and of the Centre Henri Lebesgue ANR-11-LABX-0020-01 for creating an attractive mathematical environment.
The R package rotasym allows practitioners to perform the proposed tests and reproduce the data application. 2 Testing rotational symmetry about a specified θ θ θ A random vector X with values in S p-1 is said to be rotationally symmetric about θ θ θ ∈ S p-1 if and only if OX Note also that u θ θ θ (X) and the cosine v θ θ θ (X) are then mutually independent. This multivariate sign is therefore a quantity that is more appealing than the "projection" Γ Γ Γ T θ θ θ X, that is neither distributionfree nor independent of v θ θ θ (X). If X admits a density, then this density is of the form x → f θ θ θ,g (x) = c p,g g(x T θ θ θ), where c p,g (> 0) is a normalizing constant and g : [-1, 1] -→ [0, ∞) is henceforth referred to as an angular function. Then, v θ θ θ (X) is absolutely continuous with respect to the Lebesgue measure on [-1, 1] and the corresponding density is v → gp (v) := ω p-1 c p,g (1 -v 2 ) (p-3)/2 g(v), (5) where ω p-1 := 2π p-1 2 /Γ( p-1 2 ) is the surface area of S p-2.
Real data application We illustrate in this section the practical relevance of the proposed tests with a novel case study. The data we analyze is based on the Debrecen Photoheliographic Data (DPD) sunspot catalogue  (Baranyi et al. , 2016; Győri et al. , 2016). It contains observations of sunspots locations since 1974 and is a continuation of the Greenwich Photoheliographic Results (GPR) catalogue, which spanned 1872-1976. Sunspots are darker, cooler regions on the Sun's photosphere that correspond to solar magnetic field concentrations. They are temporary phenomena that experience continuous change, lasting for hours to days, and with their shapes and areas varying notably along their lifespans. Prototypical sunspots come in pairs with opposite magnetic polarity, forming the so-called Bipolar Magnetic Regions (BMR), and they are usually clustered in groups that evolve with time.
Hence, this phenomenon may trigger non-rotational symmetric patterns, of unclear significance, in the emergence of sunspots. We offer a quantification of this significance via the proposed tests. To that aim, we analyze the rotational symmetry of the appeared sunspots during the 23rd solar cycle (August 1996 -December 2008), currently the last fully-observed cycle with curated measurements. The corresponding data X 1 ,. , X n , shown in the left panel of Figure  6 , consists of n = 5,373 central positions of groups of sunspots, understood as the first-ever observations of each group. The DPD catalogue delimits the central position of a group of sunspots through an area-weighted average of the sunspots within the group. Note that, within this setting, both the temporal (observations belong to a single cycle that aggregates the different latitude-appearance regimes) and spatial (clusters of related sunspots are treated as a single observation) dependency of the data is mitigated, therefore better accommodating the independent and identically distributed framework considered in the paper.
Acknowledgments Eduardo García-Portugués  acknowledges support from project  PGC2018-097284-B-I00 ,  IJCI-2017-32005 , and  MTM2016-76969-P  from the  Spanish Ministry of Science, Innovation and Universities , and the  European Regional Development Fund. Davy Paindaveine 's research is supported by a research fellowship from the  Francqui Foundation  and the  Program of Concerted Research Actions (ARC) of the Université libre de Bruxelles. Thomas Verdebout's research is supported by the  ARC Program of the Université libre de Bruxelles , the  Crédit de Recherche  J. 0134. 18 of the  FNRS (Communauté Française de Belgique ), and the  National Bank of Belgium. The authors would like to thank the Associate Editor and the two anonymous referees for their insightful comments and suggestions that led to a substantial improvement of this work.
Study design and population We conducted a retrospective cohort study by comparing TB trends through routine TB control data from periods before (2011-2013), during (2014-2015) and after (2016-2018) of Ebola virus disease. The data for these periods were extracted from the national health information management system based on the DHIS2. The NTP provides this database for TB surveillance data. We also assessed the current state of the surveillance system compared to the 2015 assessment during the Ebola virus disease outbreak.
Data used The overall TB surveillance data used in this study were extracted from the National Health Information System (DHIS2), which contains a module for the collection and analysis of TB data. Tuberculosis cases are collected and reported from the diagnostic and treatment centers available in all health districts. TB surveillance reports were collected and quarterly captured in the DHIS2 set up in 2016. Historical aggregate surveillance data (2009-2015) of the NTP have been imported into this system, and data entry continues at the national level. We have included all comprehensive epidemiological surveillance data recorded in the National Surveillance System (DHIS2) from 2011 to 2018. However, unregistered paper statements and data from some diagnostic and treatment centers that were not communicated to the NTP were excluded.
Differentiation is the sequential subtraction of the xt value of xt +1 from a time series to get subsequent changes over time (8). Stationarity is a prerequisite for modeling and removes spurious correlations based on time dependencies between adjacent values in the input time series and removes these in uences from the output time series  (10). The DHIS2, Excel, and R 3. 5. 1 software was used for data analysis.
We discuss the counterintuitive, from the practitioner point of view, lack of power of the maximum likelihood version of our test in a neighborhood of challenging non-identifiable situations. Several level and power studies are numerically conducted on models close to those considered in the literature, such as in  McLachlan et al. (2006) , to validate the suitability of our approach. We also implement our testing procedure on the Carina galaxy real dataset which low luminosity mixes with the one of its companion Milky Way. Finally we discuss possible extensions of our work to a wider class of contamination models.
As we will discuss along this paper, the approach of  Suesse et al. (2017) , restricted to model (1. 1), does not allow to investigate the asymptotic behavior of the test statistic under alternative assumptions (possibly contiguous) since the asymptotic behavior of the maximum likelihood estimator cannot be controlled properly under distribution misspecification. Another aspect of our nonparametric approach is that it can easily deal with situations where f 0 is only known through a training data. This situation is illustrated in Section 9 through a real dataset collecting the radial velocity of the Carina galaxy and its companion Milky Way. The paper is organized as follows: in Section 2 we describe our two-step test methodology; in Section 3 we state the assumptions and asymptotic results under the null hypothesis; Section 4 is dedicated to the test divergence under the alternative; Section 5 is devoted to the study of our testing procedure under imsart-generic ver.
2014/10/16 file: DParxiv-V2. tex date:  March 11, 2019  contiguous nonparametric alternatives (inspired from the parametric contiguous alternative concept); in Section 6 we discuss the choice of the reference measure when considering orthogonal bases for the unknown density decomposition; in Section 7 we conduct a power comparison between the semiparametric and maximum likelihood versions for our test, this section enlightens interestingly the fact that a maximum likelihood approach could force, in certain setups of the  McLachlan et al. (2006, Section 6)  Gaussian mixture model, to consider the number q of components defining f equal to 1 when in reality q = 2; Section 8 is dedicated to a simulation-based empirical and power levels study; in Section 9 we proceed with the application of our testing method to the datasets (breast cancer, colon cancer, HIV) previously studied in  McLachlan et al.
As a conclusion, this set of numerical exper- iments shows the clear interest, in terms of testing power, of considering the semiparametric versus the maximum likelihood approach especially in a close neighborhood of non-identifiable type (1. 1) Gaussian models. simulated examples is reported in Fig. 2  of the supplementary file. It appears that a significant number of observations is needed to get close to the theoretical level. This drawback can be balanced by the fact that today, as mentioned in the Introduction, genomic datasets usually contain thousands of genes which makes our methodology in practice suitable for a wide class of standard (from the sample size view point) microarray analysis problems. q q µ = 1 µ = 2 µ = 3.
6. The power is larger with the alternative L(1, 2) than with the alternative L(1, 1). Indeed the L(1, 2) distribution has a stronger shape departure from the Gaussian than the L(1, 1), and the associated mixture densities inherit these characteristics as we can see in Fig. 3  of the supplementary file. These alternatives are globally very well detected by our method and the power increases strongly when p gets closer to 1 (see Fig. 6  curve in green). here by the fact that the nonparametric and the maximum likelihood estimators lead to notably different values especially on p. HIV data. We consider the HIV dataset of vant' Wout et al. (2003). It contains expression levels of n = 7680 genes in CD4-T-cell lines, after infection with the HIV-1 virus. The maximum likelihood estimations of the parameters are p n = 0.
98, µ n = -0. 15, s n = 0. 79. The semiparametric method provides pn = 0. 99, μn = 0. 20 and s = 0. 80. The p-value given by our testing procedure is equal to 0. 64, associated with the decision S n = 1. As a consequence the normality under H 0 cannot be rejected despite the fact that the maximum likelihood and semiparametric estimations of µ are quite different but both close to 0, meaning a strong overlap of the mixed distributions (see the almost symmetry of the third probability density function in Fig. 7 ). imsart-generic ver. 2014/10/16 file: DParxiv-V2. tex date:  March 11, 2019  Galaxy data. We consider here the Carina dataset, see  Walker et al. (2007) , studied previously in  Patra and Sen (2016). Carina is a low luminosity galaxy companion of the Milky Way. The data collects n = 1266 measurements of the radial velocity of stars in Carina.
Left side: estimated density of the Milky Way Radial velocities. Right side: in black, the plot of the Carina dataset nonparametric density estimate. In red, resp. in blue, the plot of the model (1. 1) probability density function under f = f (µ,s) and obtained by plugging (p n , μn , sn ), resp. ( p n , µ n , s n ), into (p, µ, s).
Left side: estimated density of the Milky Way Radial velocities. Right side: in black, the plot of the Carina dataset nonparametric density estimate. In red, resp. in blue, the plot of the model (1. 1) probability density function under f = f (µ,s) and obtained by plugging (p n , μn , sn ), resp. ( p n , µ n , s n ), into (p, µ, s). 0. 008 0. 012 0. 006 0. 010 0. 008 0. 004 0. 006 0. 002 0. 004 0. 002 0. 000 0. 000 -200 -100 0 100 200 300 400 500 -200 0 200 400 Fig 8: imsart-generic ver. 2014/10/16 file: DParxiv-V2. tex date: March 11, 2019.
Acknowledgments The authors would like to thank  Yoshiyuki Kabashima ,  Marylou Gabrié ,  Bertrand Eynard  and  Jorge Kurchan  for many insightful discussions. This work is supported by  "Investissements d'Avenir" LabEx PALM  ( ANR-10-LABX-0039-PALM ) (EquiDystant project,  L. Foini ), as well as by the  French Agence Nationale de la Recherche  under grant  ANR-17-CE23-0023-01 PAIL , the  European Union 's  Horizon 2020 Research and Innovation Program 714608-SMiLe , and the  ERC 307087 SPARCS. Additional funding is acknowledged by AM from ' Chaire de recherche sur les modèles et sciences des données ',  Fondation CFM pour la Recherche-ENS.
Study data Antibody titre data were obtained from a previously published ferret study  [43]. The experimental protocol was originally designed to reflect different possible human infection and vaccination histories at the time of the 2009 pandemic. Here we present a secondary analysis of these data, with the intention of characterizing underlying immunological processes. Briefly, five experimental groups each of three ferrets underwent different combinations of infection with seasonal influenza A and/or vaccination with Northern and Southern Hemisphere trivalent inactivated influenza vaccine (TIV), with or without Freund's incomplete adjuvant (IFA), over the course of 70 days (Table  1 ). Serum samples were collected at days 0, 21, 37, 49 and 70 from all ferrets (Fig  1 ). HI titres were used to determine antibody titres to each infection and TIV strain.
A full description of each of these mechanisms and their implementation is described in S1 Supporting Protocol. We fit each of the 64 potential model variants in a Bayesian framework using parallel-tempering Markov chain Monte Carlo (PT-MCMC) to estimate the posterior medians and 95% credible intervals (CI) of all free model parameters. For each model, we ran 3 chains each for 5000000 iterations. Where the effective sample size (ESS) was <200 or the Gelman-Rubin diagnostic ( R) was <1. 1 for any estimated parameter (calculated using the coda R package  [63] ), we ran 5 chains each for 10000000 iterations and obtained upper 95% confidence intervals for R of <1. 1 for all estimated parameters presented here. ESS and R estimates for all parameters are provided in S5 Table. We then performed a model comparison analysis using Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO) with the loo R package  [64, 65].
Briefly, the purpose of this analysis was to compare the expected log point-wise predictive density (ELPD) of different model fits to compare their out-of-sample prediction accuracy. Comparing ELPD estimates serves a similar purpose to comparing other information criteria, where a lower ELPD suggests greater predictive power penalised by model complexity. Results shown in the main text are from the most complex model (most free parameters) variant with δELPD<1 compared to the lowest ELPD. Parameter estimates from all model variants with a δELPD<20 are shown in S7-S14 Figs. Posterior parameter estimates are shown as medians and 95% CIs. Further details of the model fitting and comparison are described in S1 Supporting Protocol. All code and data are available as an R package at https://github.
at the receptor binding site), has been proposed as a mechanism for the recall bias of subsequent responses  [19, 31, [80] [81] [82]. A model that captures the contribution of MBCs and naïve B cells to antibodies targeting a variety of epitopes may explain how immune imprinting contributes to observed antibody titres, and may include additional insights such as decreased cross-reactive breadth and magnitude with each repeated exposure. Our data included only trivalent vaccination with and without IFA, and estimates of any boosting parameters are therefore conditional on the presence of three antigens in a single vaccination. It would be interesting to compare the inferred homologous and cross-reactive boost of different vaccination strategies (eg. a three antigen TIV compared to a monovalent vaccine, or comparison by inoculum dose), and for adjuvants more relevant to human vaccination such as MF59  [83, 84].
Acknowledgments Thanks to my former PhD advisor  Etienne Roquain  for his help in many areas of the paper. Notably, he simplified and strengthened the proof of Lemma C. 2, found the proof of the concavity in Lemma B. 2 by introducting w, and improved my algorithm to compute W *. Thanks to my other former PhD advisor  Pierre Neuvial  for his advices and for introducing me to Rcpp. Also thanks to  Christophe Giraud  and  Patricia Reynaud-Bouret  for useful discussions and advices. Finally, thanks to two referees and an associate editor for improving comments. In particular, the idea of crADDOW comes from both one referee and from Etienne. This work has been supported by  CNRS  ( PEPS FaSciDo ) and  ANR-16-CE40-0019  (SansSouci).
Systematic reviews require perfect recall Systematic reviews are typically expected to identify all relevant literature. In the Cochrane Handbook for DTA Reviews  [4] , we can read: "Identifying as many relevant studies as possible and documenting the search for studies with sufficient detail so that it can be reproduced is largely what distinguishes a systematic review from a traditional narrative review and should help to minimize bias and assist in achieving more reliable estimates of diagnostic accuracy. " Thus, the requirement to retrieve all relevant literature may just be a means to achieve unbiased and reliable estimates in the face of, e. g. , publication bias, rather than an end in itself. In this context, "as many relevant studies as possible" may be better understood as searching multiple sources, including gray literature, in order to mitigate biases in different databases  [4].
Missing a single study in a systematic review could result in the systematic review drawing different conclusions, and recall can therefore, in general, only guarantee an unchanged systematic review if it is 100%. For some systematic reviews, finding all relevant literature may be the purpose of the review, i. e. , when the review is conducted to populate literature databases  [15]. On the other hand, for systematic reviews addressing diagnostic accuracy or treatment effects, the review may be better helped by identifying an unbiased sample of the literature, sufficiently large to answer the review question  [16]. In systematic reviews of interventions, such a sample is often substantially larger than can be identified with the systematic review process  [17] , but we hypothesize that it can also be substantially smaller.
There are multiple potential sources of bias that can affect a systematic review, including publication bias, language bias, citation bias, multiple publication bias, database bias, and inclusion bias  [18] [19] [20]. While some sources of bias, such as publication bias, mainly occur across databases, others, such as language bias or citation bias may be present within a single database. However, bias (i. e. , only finding studies of a certain kind) is often conflated with the exhaustiveness of the search (i. e. , finding all studies). While an exhaustive search implies no bias, a non-exhaustive search may be just as unbiased, provided the sample of the existing literature it identifies is essentially random. If the goal of the systematic review is to estimate the summary diagnostic accuracy of a test, the recall (the sensitivity of the screening procedure) may therefore be less important than the number of studies or total number of participants identified, provided the search process does not systematically find, e.
The impact of rapid reviews on meta-analysis accuracy Screening prioritization aims to decrease the workload in systematic reviews, while incurring some (presumably acceptable) decrease in accuracy. Similarly to screening prioritization, rapid reviews also seek to reduce the workload in systematic reviews and produce timelier reviews by taking shortcuts during the review process and is sometimes used as an alternative to a full systematic review when a review needs to be completed on a tight schedule  [21]. Examples of rapid approaches include limiting the literature search by database, publication date, or language  [22]. Unlike screening prioritization, the impact of some rapid review approaches on meta-analyses have been evaluated  [22] [23] [24] [25] [26] [27]. However, a 2015 review identified 50 unique rapid review approaches, and only a few of these have been rigorously evaluated or used consistently  [21].
Marshall et al. also evaluated a PubMed-only search for meta-analyses of interventions and demonstrated changes in result estimates of 5% or more in 19% of meta-analyses, but the observed changes were equally likely to favor controls as interventions  [22]. Thus, a PubMed-only search appears to be associated with lower confidence, but not with consistent bias. Halladay et al. have reported significant differences between PubMedindexed studies and non-PubMed indexed studies in 1 out of 50 meta-analyses including at least 10 studies  [24]. While pooled estimates from different database searches may not be biased to favor either interventions or controls, Sampson et al. have reported that studies indexed in Embase but not in PubMed not only exhibit consistently smaller effect sizes, but also reasoned that the prevalence of such studies is low enough that this source of bias is unlikely to be observable in meta-analyses  [25].
Related methods for screening prioritization The earliest known screening prioritization methods were published in 2006, and a number of methods have been developed since then  [9]. Similar work on screening the literature for database curation has been published since 2005  [29, 30]. A wide range of methods (generally from machine learning) have been used to prioritize references for screening, including Support Vector Machines, Naive Bayes, Voting Perceptrons, LAMBDA-Mart, Decision Trees, EvolutionalSVM, WAODE, kNN, Rocchia, hypernym relations, ontologies, Generalized Linear Models, Convolutional Neural Networks, Gradient Boosting Machines, Random Indexing, and Random Forests  [6] [7] [8] 31]. Several screening prioritization systems are publicly available, including EPPI-Reviewer, Abstrackr, SWIFT-Review, Rayyan, Colandr, and RobotAnalyst  [31] [32] [33] [34] [35].