dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This study was supported by the  Agence Nationale de la Recherche  and  Université Côte d' Azur  under a young researcher award granted to SA as a part of the  Initiative of Excellence scheme  ( ANR-15-IDEX-01 ). SA was also supported by an  Academy of Korean   Studies research Grant  ( AKS-2019-R22 ) and by funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Individual Fellowship  (Grant Agreement No. 838602 ). The authors acknowledge support from the  intrediciplinary project support program  of the  Institute for Modeling in Neuroscience and Cognition (NeuroMod) ,  Université Côte d' Azur. We express our gratitude to the  Cognition and Complexity Lab Platform (CoCoLab) administration  for their assistance, to our participants for their patience, and to our research assistants,  Juliette Cheng  and  Lisa Demontier , for their help during data collection.
The amplitude of the postural reaction is known to vary with GVS intensity until reaching a plateau  (Séverac Cauquil et al. , 1998). For that reason, on top of obvious avoidance of tactile or even painful stimulation, we chose to use low, 1 mA, GVS intensity. Therefore, because the subjects were not always aware of the stimulation, a beep sound informed them every time a stimulation was delivered. As a baseline, we used a no-GVS condition that started with a beep but without any stimulation. Data were collected using an event-related design within which runs lasted 5 min (300 s) and comprised 40 events (eight for each of our five conditions). The time interval between the two condition onsets was fixed to 7. 5 s. Behavioral responses were recorded during the scans: subjects were instructed to perform a forced-choice task using a 4-button box.
Data Acquisition All the data were collected on a 3T scanner (Philips Achieva), using a standard 32 channels head coil. The functional data were acquired using (T2 * -weighted) echoplanar imaging (EPI). The data for the main experiment (GVS) were collected during the first session. The data for the functional localizers were collected during a second session. For the GVS experiment, we used the following prescription that is quite generic for whole-brain recordings: repetition time (TR) = 2. 5 s, echo time (TE) = 30 ms, voxel size 3 × 3 × 3 mm, no gap thickness, flip angle (FA) = 77 • , SENSE factor = 2. 8. Each run comprised 120 volumes of 41 transversally oriented slices that covered the whole brain. In total, we collected 10 runs (eight runs with the four main conditions and the baseline and two additional runs with the sham stimulations, see the ''Galvanic Stimulation'' section above).
Pre-processing All the fMRI data were analyzed using the Brain Voyager QX software (v2. 8, Brain Innovation) and Matlab. Pre-processing included slice scan time correction, 3D motion correction using trilinear/sinc interpolation, and high-pass filtering (0. 01 Hz). For each subject, functional data were co-registered on the anatomy. Functional and anatomical data were brought into ACPC space using cubic spline interpolation and then transformed into standard Talairach (TAL) space  (Talairach and Tournoux, 1988).
ACKNOWLEDGMENTS We would like to thank  Maxime Rosito  for the development of the galvanic stimulation system and its adaptation to the scanner environment. We thank the INSERM U825 MRI technical platform for the MRI acquisitions and  Jean-Pierre Jaffrézou  for his thorough revision of the English. This manuscript has been released as a  Pre-Print at BioRxiv  ( Aedo-Jury  et al. , 2019).
CPAP Prescription, Initiation and Follow-Up CPAP was prescribed to OSA patients with AHI > 30 events per hour, or an AHI between national consensus statement. Before starting CPAP, patients benefited from a standardized one-hour educational program including a 10-min video about OSA definitions, symptoms, and the benefits of CPAP usage  [21]. Objective CPAP adherence data were downloaded at day 120 from the CPAP device's software (ResScan ResMed, San Diego, CA, USA).
Statistical Analysis Data were described by using median and interquartile range (Q1, Q3) for quantitative variables and frequency and percentage for qualitative variables. A latent class analysis (LCA)  [22]  was used to identify homogenous clusters of spousal relationships in 290 patients. LCA is a probabilistic clustering method which allows homogeneous sub-groups of phenotypes, called latent classes, to be identified from a larger heterogeneous population. Patients are classified into clusters based on their higher probability of belonging to one cluster than to another, which is directly estimated by the model. The VarSelLCM R package was used for LCA  [23]. This R package considers missing data in the clustering process. The following variables corresponding to the spousal relationship were considered for the clustering: patient gender, age (defined as the patient's age), marital situation (married, common-law or civil union), presence of children at home, same bed time, shared bedroom, number of years together, and professional activity (active, retired, unemployed).
The optimal number of clusters was defined by the integrated completed likelihood criterion. Clusters were compared using the non-parametric Kruskall-Wallis test for quantitative variables and the Chi-square test for qualitative variables. Multivariable mixed linear regressions with a random effect on patient were performed to investigate the association between spousal clusters and quantitative outcomes at day 120 (CPAP adherence and ESS score) by adjusting on patient baseline variables including age, body mass index (BMI), gender, AHI, Epworth sleepiness score, type of CPAP mask, and type of CPAP. Statistical analyses were performed with both SAS v9. 4 (SAS Institute Inc. , Cary, NC, USA) and R v3. 6. 1 software (R Foundation for Statistical Computing, Vienna, Austria).
Discussion In this multicenter prospective study, we used a latent class analysis to identify homogenous groups of spousal relationships. This analysis resulted in the identification of three couple clusters: older retired couples, young working couples, and mature active couples. Our analysis evaluating the impact of the couple cluster on CPAP adherence showed that the older retired couples cluster presented significantly higher CPAP adherence compared to the two other couple clusters. Interestingly, the older retired couple cluster presented a lower ESS score and an alteration in quality of life related to OSA at baseline compared to the two other couple clusters. Nevertheless, our data are consistent with previous findings showing that increased age is associated with higher adherence to CPAP  [24, 25]  even though other authors have found inconsistent findings  [26].
Funding: This study was funded by unrestricted grant from  Elia Medical. J. -L. P. , R. T. , and S. B. are supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ) and the "e-health and integrated care and trajectories medicine and  Multi-disciplinary Institute in Artificial Intelligence (MIAI ) Chairs of excellence from the  Grenoble Alpes University Foundation. This work has been partially supported by  MIAI @ Grenoble Alpes  ( ANR-19-P3IA-0003 ).
The population for ITT analyses included patients with no deviation, minor deviation, and major deviation. The population for PP analyses excluded patients with major deviation. Missing data were assessed for each variable and considered using linear mixed-effects models. As the data involved repeated measures, the evolution of primary and secondary variables across time was analysed using linear mixed models (lme4 package). Each outcome variable was studied separately for an explanation by the model. Variables were explained by group exposure, time, (group*time) interaction, and DQ score at baseline, subject and centre (subject and centre were used as random intercepts, with the subject nested within centre). This should allow individual heterogeneity, site heterogeneity, variable inclusion scores, and DS1-EI-specific changes within the same statistical regression to be considered.
Participants We recruited 41 graduate/undergraduate students from the University of Nantes (21 females and 20 males, M age = 22. 59, SD = 4. 72, range of age = 18-33). The sample size was based on previous research on destination memory  (El Haj, 2017; El Haj, Omigie, et al. , 2015). The sample size was also determined as the maximal number of participants who were willing to participate to the study. Participants provided their informed consent prior to the experiment and completed a form about their age, gender, and history of neurological or psychiatric disorders. This history served to exclude participants with neurological/psychiatric disorders, which was the case of four participants who were excluded from the original sample (n = 50). Data of two participants from the original sample were also discarded because they were not French-native speakers, an exclusion based on the argument that some of the study material was verbal (see below).
Materials Materials consisted of 24 French proverbs and 24 colored pictures. Proverbs were chosen according to their familiarity, as controlled in our previous research, and each proverb referred to a complete sentence in formal language (e. g. , the pen is mightier than sword, better late than never, when in Rome do as the Romans do). Proverbs with archaic or vernacular language were excluded  (El Haj, Omigie, et al. , 2015). The pictures depicted eight attractive faces, eight unattractive faces, and eight neither-attractive-nor-unattractive faces. Half of the faces were males and the other half were females. All the pictures were taken from Ebner, Riediger and Lindenberger's (2010) FACES database and represented faces with eye-gaze forward and head position forwards. Half the faces represented females and the other half represented males in order to avoid an own-gender bias (e.
Procedures Destination memory assessment and response recording were controlled with the software package Psychopy  (Peirce, 2007)  coupled with a laptop computer and a 17-inch LCD display. Procedures replicated previous research on destination memory  (Gopie et al. , 2010; Gopie & Macleod, 2009; El Haj, Altman, Bortolon, Capdevielle & Raffard, 2017; Wilu Wilu, Coello & El Haj, 2018). They included a study phase, a distractor task and a recognition phase (see Fig. 1 ). The study phase included 24 trials, each beginning with a 1-sec white fixation cross followed by a proverb presented in white Times New Roman 40-point font below a (12 9 12 cm) face. After telling each proverb to its corresponding face, participants pressed any key to continue with another white fixation cross for one sec. This procedure was repeated until participants had told 24 proverbs to the eight attractive faces, eight unattractive faces, and eight neither-attractive-nor-unattractive faces.
On a recognition test, they were invited to decide to whom each piece of information had been previously told. Note: The image was taken from FACES database. and rapidly because there are fewer similar faces to confuse the retrieval processes. Although this model provides a useful framework for understanding the effects of distinctiveness on face recognition, no experimental consensus has been reached on the relationship between attractiveness and face recognition. While several studies demonstrated the association between perceived attractiveness and better facial recognition  (Cross et al. , 1971; Deblieck & Zaidel, 2003; Ellis et al. , 1973; Marzi & Viggiano, 2010) , other research failed to do so  (Light et al. , 1981; Lin, Fischer, Johnson & Ebner, 2019; Wickham & Morris, 2003; Wiese et al.
Ship Modeling Validation The present ship modeling was compared to Experimental Fluid Dynamic (EFD) data and to the STF strip theory results on the David Taylor Model Basin (DTMB) 5512. The model tested is a 1:46. 6 scale model. The hull form and its characteristics are respectively plotted in Figure  3  and summarized in Table  1. The experimental data are provided by the University of Iowa [23] and are presented in Irvine et al. [24]. The EFD data concern the heave and pitch motions in regular head waves, with and without forward speed. The computed ship motions were performed at zero forward speed and at a Froude number of 0. 28, which corresponds to U = 1. 53 m. s -1 and with frequency head waves from 1 rad. s -  to 7. 5 rad. s -1. O s was defined at the ship center of gravity, hence l x = LCG and l z = VCG.
Validation and Comments on the Kite Aerodynamic Characteristics On the basis of the experimental data obtained by Behrel et al. [19, 33] , the zero-mass modeling is compared to the experimental data. The zero mass kite modeling is dependent of two parameters, the kite lift coefficient C lk and the glide angle k. These coefficients must be adapted in order to fit the data. The onshore full scale trials  [19, 33]  were performed with a classical kite Cabrinha ® Switchblade2016 of 5 m 2 designed for kite-surfing. The tether length was 80 m long. During the run, the kite performed eight shaped trajectory controlled by an autopilot based on the algorithm proposed in  [34, 35]. The experimental kite position is determined with a 3D load cell assuming that the tethers are straight. The evolution of the wind velocity with the altitude was identified thanks to a SOnic Detection And Ranging (SODAR).
The kite flight trajectory corresponded to a Lissajous trajectory as defined in Section 2. 3. 4. The amplitudes of the trajectory were arbitrarily set to ∆φ 8 = 20°and ∆θ 8 = 8°. According to the formulation of the lift coefficient in Equation (  26 ), the tether length condition to obtain a positive lift coefficient was L t 360 m. The kite excitation was studied by varying the tether length. Tether length between 360 m and 1000 m were investigated. The center of the trajectory [φ 8 , θ 8 ] and the angle of the trajectory χ 8 around the axis C 8 A were determined optimizing of the longitudinal kite towing force with the same code used by Leloup et al. [1]. In this configuration, the kite provided about 16% of the propulsive power, provided the data from  [40]. Figure  10a-c  show respectively the evolution of the φ 8 , θ 8 and χ 8 with the tether length.
The identification of the kite modeling was carried out with experimental data on a single run of 5 minutes. A more extensive validation should investigate the influence of the true wind speed and the influence of the trajectory position and orientation. Moreover, the determination of the kite aerodynamic coefficients was entirely based on experimental data. It could be interesting to be able to estimate by simulation the kite aerodynamic characteristics for a deeper understanding. In case of a dynamic flight, the high tensions developed lead to small tether sags, justifying the assumption that the lines are straight and inelastic. However, the tether acts as an interface between the kite and the ship. The dynamic tether effect may have a significant influence on the interaction between the kite and the ship.
At zero frequency, the sectional damping is linearly extrapolated to zero. The sectional added mass are linearly extrapolated. At high frequency, the sectional added mass remains almost constant, consequently, a constant extrapolation is performed. The identification of H ij can be identified either into the frequency domain or into the time domain, see  [18]. Here, a time domain identification method is used to initialize the frequency domain identification method. The time domain identification is performed with the singular value decomposition method proposed by  Kung [51]. This step is performed with a modified Matlab ® function "imp2ss" to control the order. This method is efficient but the identified transfer function has the following form: H ij = a n p n +. + a 1 p + a 0 p n + b n-1 p n-1 +.
+ b 0. (A18) Consequently, to comply with the form imposed with Equation (A14), a n is set to zero, a n-1 is set to K ij (t = 0) and a 0 is set to a 0 = b 0 B ij (ω = 0) -B ij (ω = ∞). This first estimate of the transfer function is used as initial solution of the frequency domain identification method. The frequency identification step is performed with the "oe" function of the Matlab ® system identification toolbox. This function use local optimization under constraints algorithm based on gradient methods. The structure of the transfer function, as proposed in Equation (A14), can be imposed to the frequency domain optimization algorithm. These two steps are repeated for several transfer function orders, for instance from 2 to 10. Then, the best transfer function order is selected according to a criterion based on the normalized quadratic error e tot from:              e tot = 1 2 (e ω + e t ) e ω = ∑ k [|Hij(jω k )-K ij (jω k )|] 2 ∑ k |Kij(jω k )| 2 e t = ∑ k [Hij(t k )-K ij (t k )] 2 ∑ k K ij (t k ) 2 (A19) Appendix A.
Previous research (see, for example, De  Jong et al. 2012 ) has shown that vocabulary knowledge is a good predictor of general language proficiency. We used the vocabulary test as an independent measure of the learners' general proficiency level of L2 French. Only learners at the A2 and the B1 levels were included in the study. Each participant filled in a language background questionnaire including personal data and information on first and second languages, learning contexts, etc. The participants in the NLD group, coming from the Dutch-speaking part of Belgium, generally started learning French at school earlier than the other three groups. Furthermore, the mean age at testing is lower in the NLD and the ITA groups. This is because these students generally start their studies at the university at a younger age and because these groups do not include any learners over 30.
If the question focuses on comparing wings between bats and birds, however, different measurements might be more appropriate depending on the specific question. That is, if the focus is whether the aerodynamic properties of wings vary within bats or between bats and birds, the traits collected should reflect these aerodynamic properties (e. g. wingspan, aspect ratio, etc. ). However, if the focus is on convergence between different bats and birds, it would be preferable to use traits that have facilitated flight in both groups (e. g. digit length, integumentary system, etc. ). Where there is any doubt about which traits to analyse, it may be preferable to use several different kinds of data for the same feature to determine whether they capture the same pattern of disparity. The points above assume that researchers are collecting their own data for disparity analyses, but this is often not the case.
Discrete characters are commonly recycled from phylogenetic studies (e. g. [11, 31] ). This approach may artefactually increase disparity between phylogenetically distinct groups, since phylogenetic characters are often collected to discriminate among groups. This needs to be considered when interpreting results, especially as synapomorphies can lead to apparent shifts or increases in disparity when new clades appear (particularly if the character-state distribution is skewed towards a particular clade). Furthermore, many datasets are limited to subsets of anatomy that are at least implicit samples of overall anatomy, but explicit tests of this assumption have shown that different aspects of morphology can exhibit different patterns of disparity  [30]. The influence of trait choice on resulting disparity patterns can be especially challenging where the available data have non-random missing anatomical parts, such as the absence of soft tissue in the fossil record  [25].
Ultimately, disparity analyses are characterized by the data they use. Unfortunately, trait data suffer from the same shortcomings as most biological datasets. The data within them can be non-overlapping, hierarchical, inapplicable, ambiguous, polymorphic and/or correlated  [32]. There are also issues of missing data, both where a particular character cannot be measured for a given taxon and where a given taxon cannot be sampled at all. Trait data may also be influenced by biological phenomena such as allometry and sexual dimorphism. More practically, data collection is constrained by the time and money available, making collating a 'perfect' dataset impossible. Even when care is taken, subsamples of the universe of possible data may not have the power to uncover the full patterns of disparity.
9]  for a detailed overview of ordination methods and properties). Note that for PCO, the distance metric used can have significant impacts on the resulting morphospace  [34]. The choice of distance metric is, therefore, crucial, and should not be overlooked when using PCO. One of the reasons why ordination techniques are common in disparity analysis is that they make it easier for researchers to comprehend patterns in two or three spatial dimensions at a time, which can be more intuitive than through disparity indices (see §3b). Additionally, after ordinating the data, it is possible to focus on just a subset of axes of the morphospace (i. e. selecting only those axes that describe the majority of the variation in the dataset-e. g. 95%). In the case of geometric morphometric data, some ordination techniques (e.
g. PCA) can be particularly useful as they conserve the mathematical properties of the data while efficiently reducing the dimensions  [35]. In practice, this facilitates interpretation of only the major axis of a highly dimensional dataset as major gradients of biological variation (e. g. the elongation and flattening of birds' beaks;  [36] ). Like most other aspects of disparity analyses, however, reducing dimensionality can be fraught. In the case of ordination, subsampling axes from the ordination can lead to misinterpretation of the results. Although a common technique is to consider the d-axes that encompass 95 or 99% of the variance in the dataset (either by manually selecting the d-axes that encompass the desired cumulative variance or using methods such as the broken stick model;  [33, p.
410] , the interpretation of these principal axes can miss some aspects of the structure of the data and lead to misinterpretation of the biological variation mapped on these axes  [37, 38]. Visual interpretations of multidimensional data can be particularly misleading, not least as multidimensional spaces might not possess the Euclidean properties one often intuitively assumes  [7, 25]. Interpreting biological variation along the axes is always a post hoc procedure and may have little relation to the overall question (for example, if the first few ordination axes represent elongation of the beak in birds, but the question is about wing disparity). Additionally, in some cases, reducing the dimensionality of a dataset can render its interpretation more problematic. For example, when the analysed dissimilarity data are non-Euclidean (e.
In general, categorical data are a good deal more problematic than continuous data, because the characters themselves are invariably non-equivalent, non-independent and the distribution of the variance is usually more evenly distributed across axes (i. e. contrary to a PCA, the first few axes do not encompass most of the variance in the dataset). Such non-Euclidean spaces often have non-intuitive properties, for example, straight lines viewed in bivariate plots of some dimensions are not actually straight and character coding and missing data can make the pairwise dissimilarity matrix lose its metric properties (i. e. the distance between A and B is not equal to the distance between B and A;  [39] ). Last, but not least, in many cases, ordination might not be necessary. For example, if an index characterizing disparity can use all of the data, it is not necessary to calculate it on the ordinated dataset (e.