dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulation results The results of our simulations conducted with synthetic data are illustrated in Figures  1  and 2  (clustering of gene trees defined on the same set of taxa), Supplementary Figures S3 and S4   Figure  3  illustrates the performance of our supertree clustering algorithm applied to gene trees with different numbers and sets of leaves using the OFST_EA objective function with approximation by Euclidean distance and CH cluster validity index. In this experiment, the value of the penalization parameter  in Equation 12 varied from 0 to 1, with the step of 0. 2. Obviously, when no species are removed from the dataset, the penalization term in Equation 12 equals 0 and has no impact on the clustering performance. However, in all other cases, the presented ARI curves showed different behaviour and reached its maxima at different values of  (i.
Exploring the patterns of evolution of SARS-CoV-2 genes In this section, we first describe the SARS-CoV-2 dataset examined in our work, then give some details regarding the applied multiple sequence alignment and tree inference methods, and finally present and discuss the results of our supertree clustering analysis. Several recent studies provide evidence of recombination events in different genes of betacoronavirus organ-   (2006). Supplementary Table  S1  (see Supplementary Material) provides the organism names, host species, and Gisaid or GenBank accession numbers for the 43 coronaviruses considered in our study.
Methods details Our analysis was conducted for 11 main genes of the SARS-Cov-2 genome (i. e. genes ORF1ab, S, ORF3a, E, M, ORF6, ORF7a, ORF7b, ORF8, N and ORF10) as well as for the RD domain of the spike protein because of its key evolutionary importance. Thus, 12 gene phylogenies were inferred and analyzed in our study. It is worth mentioning that some gene annotations were absent in the GenBank and Gisaid databases (i. e. annotations for genes ORF6, ORF7a, ORF7b, ORF8 and ORF10 for taxa from sub-groups 9 to 17 and annotations for gene ORF8 for taxa from sub-group 8 were unavailable), thus leading to gene phylogenies having different, but mutually overlapping, sets of leaves. The VGAS program  (Zhang et al. 2019 ), intended to identify viral genes and carry out gene function annotation, was executed to validate all betacoronavirus genes found in GenBank and Gisaid.
3 ), the optimal value of the penalization parameter  could vary between 0 and 0. 2 for such data. Here, we present the clustering results obtained with the value of  = 0. 1 (very similar clusterings were obtained with  = 0 and  = 0. 2; only one tree changes its cluster membership with  = 0. 2). The obtained clustering solution with 3 disjoint clusters is presented in Figure  4 (a, b  and c ). This solution encompasses three main patterns of evolution of betacoronavirus genes. The phylogenies of genes E, M, ORF6, ORF7a, ORF7b, ORF8 and ORF10 were assigned to Cluster 1, those of genes ORF1ab, S, ORF3a and N to Cluster 2, and that of the RB domain to Cluster 3. The consensus supertrees for each cluster were then inferred using the CLANN program (Creeve and McInerney 2005). The supertree for Cluster 1 (Fig.
This property makes our method perfectly suitable for analysis of large evolutionary datasets. In order to compute the RF distance between pairs of trees defined on different, but mutually overlapping, sets of leaves we propose to reduce them pairwise to common sets of leaves and then to normalize the obtained distance value. In case of supertree clustering, we also added to the objective function of the method the term including the penalization parameter , which is used to create well-balanced clusters that contain trees with both high topological and species content similarity. This is a common way of addressing the problem of missing data in clustering, and in machine learning in general  (Pan and Shen 2007). Penalized and weighted k-means have been efficiently used in computational biology as well  (Tseng 2007).
Fortunately, the Fréchet mean tree is unique and its approximation can be calculated in polynomial time by an iterative algorithm  (Sturm 2003). However, the Fréchet mean may also demonstrate a non-Euclidean sticky behaviour, as changing an input tree does not necessarily change the mean tree of the dataset, in contrast to Euclidean space  (Miller et al. 2015; St. John 2017). Alternatively, the median (which is a more robust estimator than the mean) of a set of trees can also be considered when clustering tree. The BHV distance median of a set of trees is the tree that minimizes the sum of non-squared BHV distances to those trees (see  Benner et al. 2014  for an algorithm computing the BHV median). Another widely-used criterion for assessing differences between trees is the Subtree Pruneand-Regraft (SPR) distance  (Hein et al.
e. centroid) of all objects in the given dataset X and Nk is the number of objects in cluster k. In the context of the Euclidean distance, the SSW index can be calculated using the two following equivalent expressions:     = = = - = + = - = - = K k N i K k N i N i j kj ki k k ki W k k k x x N m x SS 1 1 1 1 1 1 2 2 , ) ( 1 ( 17 ) where xki and xkj are the objects i and j of cluster k, respectively  (Caliński and Harabasz 1974). To use the analogues of Equations 16 and 17 in tree clustering, we need to define the concept of centroid for a given set of trees. The median tree  (Barthélemy and Monjardet 1981; Barthélemy and McMorris 1986)  plays the role of this centroid in our tree clustering algorithm. The median procedure is defined as follows  (Barthélemy and Monjardet 1981). The    = - = + = = K k N i N i j kj ki k W k k T T RF N SS 1 1 1 1 ) , ( 1 , (18) where Tki and Tkj are trees i and j of cluster k, respectively.
e. tree leaves) from the generated gene trees. The branches adjacent to the removed leaves were collapsed. The following intervals of missing data were considered: 0% (no species were removed), 10% (5% to 15% of species were randomly removed), 25% (16% to 35% of species were randomly removed) and 50% (36% to 65% of species were randomly removed).    - = + = = -  1 1 1 1 ) , ( ) 1 ( 1 ) , ( The exact number of species to be removed from each gene tree and each data interval was drawn randomly using a uniform distribution. We also made sure that every pair of trees in each input dataset had at least 4 species in common. The value of the penalization parameter  in Equation  12 was set to 0 in this experiment. Two independent simulations were carried for the supertree version of our algorithm using the OFST_EA objective function (Equation  12 ) and the CH-E (Equations 13-14) and BH cluster validity indices adapted for supertree partitioning (see Supplementary Figs.
ACKNOWLEDGMENTS This work was supported by  Natural Sciences and Engineering Research Council of Canada and Fonds de Recherche sur la Nature et Technologies of Québec. We would like to thank  Nicolas Lartillot  for providing us with the  MPC tree clustering program ,  Pierre Legendre  and  Bogdan Mazoure  for helping us with the analysis of SARS-CoV-2 data, and  Louise Laforest  and  Alexander Friedmann  for insightful conversations and helpful comments. We also thank  Compute Canada  for providing access to high-performance computing facilities.
DATA AND CODE AVAILABILITY All relevant data are within the paper, its Supplementary Material files, and at: http://www. info2. uqam. ca/~makarenkov_v/Supplementary_Material_data. zip. The program (written in C++) that implements the described method intended for clustering trees and inferring multiple consensus trees and supertrees is freely available at: https://github. com/TahiriNadia/KMeansSuperTreeClustering.
Facilities: LBT(LMIRCam) Software: pyKLIP  (Wang et al. 2015) , diskFM  (Mazoyer et al. 2020) , MCFOST  (Pinte et al. 2006 (Pinte et al. , 2009)) ,  Astropy (Astropy Collaboration et al. 2013 , 2018) , numpy, scipy, matplotlib  (Hunter 2007) , Astrolib PySynphot (STScI Development Team 2013), Dewarp  (Spalding & Stone 2019) , DebrisDiskFM  (Ren et al. 2019) , pymcfost (https://github. com/cpinte/pymcfost), mcfost-python (https://github. com/swolff9/mcfost-python) Note-PA is along the semi-minor axis, measured east of north. "Acceptable Range" indicates parameter ranges that give χ 2 r ≤ χ 2 r,min + 2/ν. the large deviation in TP, it is still quite small, ranging from 0. 001 in L to 0. 15 in K s. At most, the TP varies by 40% (at 100 AU in K s , at the AO control radius). However, on average, the TP only varies by 2.
ACKNOWLEDGMENTS We thank  Esther Buenzli ,  Vanessa Bailey ,  Timothy Rodigas , and  Jared Males  for contributing to or obtaining the original imagery. We thank the anonymous referee for their careful review. We thank  Kellen Lawson  for his helpful suggestion to look into the differential evolution algorithm as a way to fit the forward modeled disks. SKB and KBF acknowledge support from  NSF   AST-2009816. S. J. acknowledges support from the  National Agency for Research and Development (ANID) ,  Scholarship Program ,  Doctorado Becas  Nacionales/2020 -21212356. L. P. gratefully acknowledges support by the  ANID   BASAL  project  FB210003 , and by  ANID , - Millennium Science Initiative Program - NCN19 171. We acknowledge the use of the  Large Binocular Telescope Interferometer (LBTI)  and the support from the  LBTI team.
MCFOST  is funded from the  Australian Research Council  under contracts  FT170100040  and  DP180104235 , from  Agence Nationale pour la Recherche (ANR) of France  under contract  ANR-16-CE31-0013. This research has made use of the  SVO Filter Profile Service  (http://svo2. cab. inta-csic. es/theory/fps/)supported from the  Spanish MINECO  through grant  AYA2017-84089. This publication makes use of data products from the  Wide-field Infrared Survey Explorer , which is a joint project of the  University of California, Los Angeles , and the  Jet Propulsion Laboratory/California Institute of Technology , funded by the  National Aeronautics and Space Administration.
Final remarks In summary, our results extend previous findings on high-order interdependencies of the ageing brain using a novel framework that incorporated whole brain modeling and connectome datasets along the lifespan. Whole-brain models have enhanced our understanding of the brain across different conditions, and therefore provide a highly promising avenue of research in the field of ageing neuroscience. In this context, our work constitutes the first step towards mechanistic explanations on how functional high-order interdependencies in the human brain are affected by the age-connnectome degeneration. Future work should validate our modeling approach in the presence of other forms of brain degeneration, such as the interplay between aging and pathologies like Alzheimer or Parkinson diseases.
One extra 3D diffusion image was acquired for b = 0 s/mm 2 , needed for the diffusion imaging preprocessing. Although full details are given in Ref. [59] , the pipeline consisted in eddy current correction, motion correction, tensor estimation per voxel, fiber assignment, and functional partition projection to the individual diffusion space. This resulted in SC matrices of dimension 2514 × 2514, one per participant, and each matrix entry corresponding with the number of white matter streamlines connecting that given region pair. Finally, we reduced complexity of these matrices by grouping the 2514 × 2514 matrix into 20 × 20 using the BHA, and averaging the BOLD signals of all regions within a given atlas region. The BOLD signals and connectomes used in this study are available at https://github.
The excitatory and inhibitory firing rates are denoted by r ðEÞ n and r ðIÞ n respectively. The feedback inhibitory control weight, J FIC n was adjusted for each node n in a way such that the firing rate of the excitatory pools r ðEÞ n remains fixed at about 3 Hz, using the linear fitting strategy proposed by Herzog and colleagues  [38]. The precise values of the parameters used here are given in Table  1. The complete DMF implementation was performed in Matlab, and is freely available at https://gitlab. com/concog/fastdmf. Haemodynamic model. The excitatory firing rates r ðEÞ n were transformed into BOLD-like signals, following a well-known hemodynamic model  [87]. It is assumed that an increment in the firing rate r ðEÞ n triggers a vasodilatory response s n , that produces a blood inflow f n , and changes the blood volume v n and the deoxyhemoglobin content q n.
Using these quantities, we calculated the following metrics for redundancy and synergy, for each subject k, interacting region m, and interaction order n: R m n ðkÞ ¼ 1 N þ n;m X i 1 � � � X i nÀ1 O þ ðkÞ ðX m ; X i 1 ;. ; X i nÀ1 Þ ; ð10Þ S m n ðkÞ ¼ 1 N À n;m X i 1 � � � X i nÀ1 O À ðkÞ ðX m ; X i 1 ;. ; X i nÀ1 Þ ; ð11Þ where N Finally, the average of these quantities over all subjects and regions can be also calculated as R n ðkÞ ¼ 1 M X M m¼1 R m n ðkÞ ; ð12Þ S n ðkÞ ¼ 1 M X M m¼1 S m n ðkÞ : ð13Þ The code to compute all these metrics is available at https://github. com/brincolab/High-Order-interactions.
MG was partially supported by  CONICYT-PFCHA/ Doctorado Nacional/2019-21190577. FR acknowledges the support of the  Ad Astra Chandaria Foundation. PM was funded by the  Wellcome Trust  (grant no. 210920/Z/18/Z ). SPS was supported by the  FWO Research Foundation Flanders  ( G089818N ), the  Excellence of Science funding competition (EOS ;  30446199 ) and the  KU Leuven Special Research Fund  (grant  C16/15/070 ). PO is funded by  Fondecyt Regular  grant  1211750  and  ANID-Basal  Project  FB0008. RC acknowledges financial support from the  Human Brain Project ,  H2020-945539. JMC acknowledges financial support from  Ikerbasque (The Basque Foundation for Science)  and from the  Ministerio Economia, Industria y Competitividad (Spain)  and  FEDER  (grant  DPI2016-79874-R ), as well as from the  Department of Economic Development and Infrastructures of the Basque Country  ( Elkartek Program ,  KK-2018/00032 ,  KK-2018/00090 , and  KK-2021/00009 ).
Research has shown that prosody plays an important role in the intelligibility, comprehensibility and accentedness of non-native discourse (Munro and Derwing, 1995,  1998). Yet prosody is deemed difficult to teach  (Setter et al. , 2010). Previous studies have used software such as PRAAT (Setter et al. , 2010 , Olson, 2014 , Imber et al. , 2017,)  but they can be complex to use  (Setter et al. , 2010; Setter and Jenkins, 2005). Could a more comprehensive tool be useful to L2 learners? Englishville is a website where it is possible for the learner to see a real-time 3D spectrogram. An experiment was set up to determine whether multi-sensorial input, available via Englishville can help learners of English. Eight French students enrolled in a BA in English took part in this initial trial experiment (2 per group).
Research on visual and auditory tools Seeing speech is surprisingly not that new. The kymograph (or wave writer) dates back to 1847. From then onwards, the different devices used to write down speech flourished, all the way to the sonograph and spectrograms. Following the rapid progress in computer science and technology several software became available, for example  Visi-pitch (1975); Speech Viewer (1985) ;  WinPitch (Martin, 1996) ; and Praat  (Boersma and Weenink, 2001) (c. f. Chafcouloff, (2004) , for further discussions). Major advances in speech technology have led to the increasing use of language software and technology such as computer assisted language learning (CALL) and computer assisted pronunciation training (CAPT). A few examples of these are: WASP (https://www. speechandhearing.
net/laboratory/wasp/), Better Accent Tutor (http://www. betteraccent. com/), Ampitch (https://www. speechandhearing. net/laboratory/ampitch/). These examples are particularly interesting in regard to L2 prosody because they enable the speaker to see their intonation patterns, as can be seen in figures 1 -3. In order to compare the different technologies and how they represent intonation, the same sentence was recorded using each of these tools. The sentence "He said what?" was said with rising intonation. The first software is the Better Accent Tutor (Figure  1 ) which analyses the L2 speaker's intonation, stress, and rhythm. It enables learners to visualise these patterns in a simple manner and offers corrective feedback. However, it is only available in American English, and it is not free.
Since the mid-1970s, there has been an on-going stream of studies which have used computer-based methods in order to test and improve the perception and production of prosody  (James, 1976; De Bot, 1983 ). One of the first studies carried out in the early 1980s by de Bot (1983) concluded that visual feedback was more effective than auditory feedbackin other words, when the subject saw speech (in this experiment, the pitch contour in Praat was used) rather than just hearing it, the subject's intonation improved. Pitch visualisers (such as Praat and similar software) have been used in more recent research  (Setter et al. , 2010 , Olson, 2014 , Kartushina, et al. (2015 ) Imber et al. , 2017,). Gorjian et al. (2013) , compared 2 methods of teaching stress and intonation: a traditional one that uses repetition and explanations about acoustic properties of speech, the second, using Praat.
The results showed that learning prosody with Praat was significantly beneficial. As the authors point out, the first method is generally teacher-centred, leaving students passive in the classroom. With the use of multi-media tools and multi-sensorial software, it places the student at the centre of their acquisition. Some software, like Praat, require practice and training to use and some research concluded that combining sound and image led to slightly more mixed results in learning prosody, often due to the complexity of the software used  (Setter et al. , 2010). Due to the technical side of these tools, it can be difficult to motivate students to familiarise themselves with this kind of software and then work on intonation. We believe that using real-time displays of intonation is more comprehensible.
Method: From conception to implementation The desire to create a free, user-friendly real-time tool for prosody in the domain of multi-sensorial learning of L2 English, and the idea that prosody should be at the centre of second language teaching motivated the creation of our website Englishville. Englishville uses a 3D spectrogram and allows the capture of the audio stream so that it can be recorded on a server. These tools are then integrated in a website where it is possible to record corpus, set up experiments and also participate in them. The spectrogram representations are not recorded, they are always interpreted on the go. The sounds are saved in an Ogg format to minimize bandwidth usage but with a high enough bit rate so that they can be reused in other software. One advantage of Englishville is that L2 learners can see (and hear) utterances and then repeat them and even save their productionscontrary to some of the other software available.
Software such as Praat have been found to be too complicated for the uninitiated to use by some researchers. Our objective is to provide a tool which is easy to use for both teachers and learners and a fun way to raise awareness and practice prosody for L2 learners. This is motivated by the pedagogical analysis that teaching and learning prosody is not an easy task. It is also a quest for a more modern, student-centred approach. It is impossible draw any significant conclusions from such a small-scaled experiment but this is just the first step in our thought-process and the implementation of this tool. More experiments are planned, as is a training session, this time with first year university students. We are also currently working on an algorithm to automatically compare the students recording.
Additional spectral estimates of the decorrelation of ensemble members over the first 60 days can be found in  Leroux et al. (2021). In what follows, we will focus our analysis on day 60, which is about 20 days before the saturation of IKE. As shown in the following, day 60 exhibits a well organized spatial structure in the eddy-mean flow KE interactions that nicely illustrates non-local processes. Such processes are nonetheless observed all along the 120-day long simulation. The interested reader is referred to the following animation: https://doi. org/10. 5281/zenodo. 6221153. The 120 days of simulation cover the period 6 February-5 June, and a weakened submesoscale activity associated with spring time is observed toward the end of the simulation. It is thus likely such a seasonal cycle will imprint onto eddy-mean flow kinetic energy transfers, a signature observed for instance by  Uchida et al.
Data Availability Statement Further details on the MEDWEST60 ensemble simulations are available at https://zenodo. org/record/4570159, and the NEMO code used for the MEDWEST60 configuration is available at https://github. com/ocean-next/ MEDWEST60/tree/main/src_config. Python scripts used to produce the figures of this manuscript are available at https://github. com/quentinjamet/publications-codes/tree/master/Jamet_etal_JAMES2022. Dedicated CDFTOOLS are available at https://github. com/quentinjamet/CDFTOOLS/tree/cdf_medwest.
Protocol All the participants taking part in the present study gave their written informed consent to data collection and data analyses (approved number ID-RCB: 2021-A00368-33). The experimental procedure lasted 15 min and was administrated at the workplace by the same unique experimenter in all groups. After putting a chest belt with bipolar electrodes (Polar H10, Polar Electro France S. A. S) equipped with a transmitter for HRV measurements, each participant was comfortably seated in front of a computer breathing at a spontaneous rate and used the mouse/keyboard to fill out psychological and sociodemographic and lifestyle questionnaires. The procedure offered a number of advantages to obtain reliable HRV recordings while people were filling out the questionnaires. Filling a questionnaire at the workplace is a quiet mental activity that could advantageously replace the situation currently called 'rest' in controlled experiments.
Physiological Characteristics: Analyses of Heart Rate Variability Heart rate variability (HRV) was assessed by a series of successive cardiac interbeat (RR) intervals duration, obtained thanks to 1 kHz sampling of electrical cardiac activity by the belt Polar H10. The transmitter was connected via Bluetooth to an android smartphone, and successive intervals delivered by the transmitter were recorded and stored in the smartphone. The Polar device has the capacity to record the heart electrophysiological signal associated to ventricular contraction (R-peak) with a quality similar to ECG  [44, 45]. Individual RR time series were imported to Matlab (Matlab Release 2021a, Mathworks, Natick, MA, USA) for subsequent analyses using both existing functions and customdesigned algorithms. The raw data were inspected for artifacts.
Statistical Analysis Throughout the document, values are expressed as mean and standard deviation to the mean (SD). Outliers were identified using the ROUT method  [51]. The group sizes were determined by power calculation (GPower version 3. 1. 9. 6, https:// www. psychologie. hhu. de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/ gpower, accessed on 2 August 2021) based on our preliminary data  [40, 50]  and obtained during pre-testing on caregivers (α error probability: 0. 05, power 0. 8) mean RMSSD value, 24 ms; standard deviation, 12 ms; mean difference, 9%. This resulted in n = 17 for one experimental group (Cohen's d effect size: 1. 18). Inter-group differences (MOB, N-MOB, ADM, RES) in main variables, HADS scores, coping strategies scores, and cardiac markers were evaluated by repeated measures analysis of variance (ANOVA) and post hoc Tukey correction.