dataesr/scientific-paragraphs-categorization huggingface dataset:
Each signal was measured during the 1. 5 s before-answer and after-answer periods at a frequency of 500 Hz, providing 2 data sets of 750 data points each. For each of the 8 resulting data sets (4 signals * two epochs), we extracted a set of 40 basic features before the answer and after answer onset (listed, respectively, in lines 1-40 and 84-123 in Supplementary Table  1 ). These values were based on statistical properties of the measurements, including the minimum, maximum, mean, median, variance, range (max-min), and the first quartile, third quartile and interquartile range (difference between the first and second quartiles). In addition, to capture aspects of gaze dynamics during the two epochs, we computed a vector of local derivatives (differences between successive time points) and extracted the mean of the absolute value of the derivatives in each epoch.
A B C Fig. 5. Effects of state curiosity on eye movements correlate with trait curiosity. To measure the correlation between questionnaire ratings and oculomotor effects we computed for each subject the average eye distance to answer (over the 3-s of eye-movement recording for curiosity and confidence, and over 1. 5 s after answer onset for surprise). We then computed an effect size for each subject as the average difference between trials with ratings of 5 versus 1, and plotted it against the subject's aggregate questionnaire score (see Section 2). Linear regression (black line) was significant for the first panel but not for the other two (omitted). Average Percent Classification  data set, pooled across observers. This produced above-chance classification accuracy of 69. 79%, 72. 81% and 63.
Acknowledgments This work was funded by a  Fulbright visiting scholar grant (AB), HSFP Cross-Disciplinary Fellowship   LT000250  (AB), and  Inria Neurocuriosity  grant (AB, PYO, JG). Special thanks to  Drs. Hakwan Lau  and  D. Graham Burnett  for the use of the Eyelink eye tracker. We thank  Latoya Palmer  and  Cherise Washington  for expert administrative assistance, and to  Manuel Lopes  and the members of the  Mahoney Center  who read and had helpful suggestions on the manuscript.
Currently, the prevailing standard for the analysis of multitemporal MBES surveys is, however, visual analysis by trained experts delineating the crest line of the dunes at several time steps using commercial or open-source GIS software  (Xu et al. , 2008 , Van Landeghem et al. , 2012). A few studies have demonstrated the feasibility of using image correlation techniques for measuring dune migration  (Franzetti et al. , 2013, Duffy and Hughes-Clarke, 2005)  but to the best of our knowledge integrated workflows that enable the derivation and interactive visualization of sub-marine dune migration are not yet available. To fill this gap a new workflow for the automatic detection of marine sand dune displacements was implemented within the IQmulus project utilizing a cloud infrastructure to process the large input data and GPU technology for efficient interactive visualization.
DISPLACEMENT MEASUREMENT WORKFLOW Measuring vertical deformation from multi-temporal point cloud datasets is generally a straight forward operation for globally flat * Corresponding author surfaces, whereas the derivation of 2D displacement comprises greater ambiguity and requires a robust matching technique. The overall implemented workflow is presented in Figure  1  and includes several processing steps that are available as services in the cloud IQmulus cloud infrastructure. Preceding the bi-temporal matching Locally Refined B-Splines  (Skytt et al. , 2015 , Dokken et al. , 2013)  are used to approximate the point cloud with a continuous surface representation (Service 09). Since the LR B-Splines also extrapolates a surface beyond the extent of the input point clouds the surface is trimmed in a subsequent step to the spatial extent of the input (Service 90).
ACKNOWLEDGEMENTS Research leading to the results presented here is carried out within the project  IQmulus (A High-volume Fusion  and  Analysis Platform  for Geospatial Point Clouds, Coverages and Volumetric Data Sets) funded from the 7th  Framework Programme  of the  European Commission , call identifier  FP7-ICT-2011-8 , under the grant agreement no. 318787 , started in November 2012.
Mammalian TET Family Gene Sequences We retrieved the available TET sequences using the Ensembl Compara database and the Blastp program. Our query of the human TET amino acid sequences in major databases helped us identify a number of homologous proteins in mammalian species. At least 39 species were available for each gene, including a broad range of mammalian species (Table  S1 ). The wide distribution of TET1, TET2 and TET3 genes in the variety of mammalian species suggests that the TET genes underwent two successive duplications before mammalian diversification  [4, 7]. Since the sequence identity among the TET paralogs was relatively low other than in the catalytic domains, we analyzed each paralog separately in this study. Anisimova et al. [21]  showed that recombination hampers the detection of positive selection.
Positive Selection and the Diversification of TET Family Genes Table  1  summarizes the output of the codeml program  [18, 19]  obtained using the site models. We used model M0, which assumes a single ω for all of codon sites in the alignment, to estimate a general ω value for each data set. The ω values were quite similar for TET1 and TET2 (ω = 0. 385 for TET1 genes and ω = 0. 346 for TET2 genes), while that of TET3 was distinct, with an estimated value of ω equal to 0. 115. These relatively low ω values for TET genes suggest that the evolution of these genes was dominated by a type of "purifying" selection on most sites and lineages. Positive selection can be accounted for by codon models M2a and M8, by providing an additional class of sites in which ω > 1. For the TET1 and TET2 data sets, significantly better fit for the data is obtained with the M8 model (p < 0.
Shifts in the Site-Specific Selection Process along Lineages In order to clarify the processes of site-specific selection along the different lineages, we conducted maximum-likelihood analyses by employing the software program Fitmodel, using stochastic branch-site codon substitution models in a nested set  [20, 26]. These models are relevant in cases where no prior evidence is available about the lineages that evolved under positive selection  [27]. The results of our analyses with the M0, M3, M3 + S1, and M3 + S2 substitution models are provided in Tables  2  and 3. For the TET1, TET2, and TET3 data sets, as parameters were added to the nested substitution models, the log likelihoods were greatly improved (Table  2 ), suggesting that the best codon substitution model for the TET1, TET2, and TET3 data sets is the model M3 + S2-in which there are unequal switching rates among the three classes of rate ratios.
For the TET2 alignment which included 1905 codons, 128 (6. 7%) sites were inferred to have ω 3 = 1. 54 at some point in the history with high posterior probabilities (Figure  2B ). As is the case with several site models described above (Figure  1 ), evidence of positive selection of one or more branches was observed for only a few codon positions in the Cys-rich, DSBH and CXXC domains (Figure  2A, B ). In contrast, moderate positive selection was inferred on several branches for many codon sites in the N-terminal half and low-complexity insert regions, respectively (Figure  2A, B ). We inferred that 77 (4. 3%) sites in the TET3 dataset had undergone relaxed selection (ω 3 = 1. 07) at some stage in the TET3 gene family's history, with high posterior probabilities. The TET3 codon positions for which we observed evidence of relaxed selection (ω 3 ) were also distributed mainly outside the Cys-rich, DSBH and CXXC domains (Figure  2C ).
6 on several branches for many codon sites in the N-terminal half and low-complexity insert regions, respectively (Figure  2A, B ). We inferred that 77 (4. 3%) sites in the TET3 dataset had undergone relaxed selection (ω3 = 1. 07) at some stage in the TET3 gene family's history, with high posterior probabilities. The TET3 codon positions for which we observed evidence of relaxed selection (ω3) were also distributed mainly outside the Cys-rich, DSBH and CXXC domains (Figure  2C ). Among the TET gene tree branches, the number of sites in the ω3 rate ratio class showing high PPs on each branch varied from 0 to 168 for TET1, from 0 to 107 for TET2 and from 0 to 64 for TET3 (Figure  S4 ). The branches with a greater number of sites in the ω3 rate ratio class were concentrated on deep, internal branches of the TET1, TET2 and TET3 gene trees (Figures  S1-S3 ).
3D Structure Mapping We next sought to clarify the roles that positive selection played. We created a map of the positively selected amino acid sites, on a three-dimensional (3D) model of the TET protein. Since the crystal structure has been elucidated only for the catalytic core region of TET2, we focused on the 3D model of this structure. Figure  3  shows the 3D structures of the catalytic region of TET2 (PDB ID: 4NM6), which was based on the crystal structure of a catalytically active form of human TET2 bound to DNA  [12]. Based on the results of our analysis using the Fitmodel program, we inferred that only four sites in the TET2 catalytic core region had ω > 1 with high PP values (>0. 90): 1203S, 1204S, 1205S and 1892N. Under the site models in the PAML and Datamonkey web server, we also deduced that one site, 1312L, had undergone positive selection.
Convergent and Divergent Changes Using the codeMLancestral program  [28] , we attempted to identify the parallel and convergent amino-acid substitutions, based on branch-wise comparisons of the TET genes. Figure  4  shows the plots of the summed PP values of total convergent substitutions (that is, both the convergent and parallel substitutions) vs. the summed PP values of total divergent substitutions across all of the branch-pair comparisons for the TET1, TET2 and TET3 phylogenetic trees. Similar to other studies  [28, 29] , there was a linear relationship between the number of divergent and convergent substitutions, and the divergent substitutions were more abundant than convergent substitutions. In the case of TET3, although these relationships were noticeable, we also observed extremely lower divergence probabilities and somewhat higher convergence probabilities compared to TET1 and TET2.
According to model M8, TET1 and TET2 appeared to have undergone positive selection that acted on a subset of sites, whereas none of the site models implemented in PAML revealed positive selection within TET3. We then used the program Fitmodel to examine the potential differences in the site-specific selection patterns among the various branches in the TET phylogenies. Fitmodel uses a maximum-likelihood phylogeny-based codon substitution model with parameters for switching between the selection processes across the phylogeny and along sites  [20, 27]. The results obtained with Fitmodel, similar to those of the codeml analysis using site models, suggested that purifying selection played an important role in the evolution of TET genes.
According to model M8, TET1 and TET2 appeared to have undergone positive selection that acted on a subset of sites, whereas none of the site models implemented in PAML revealed positive selection within TET3. We then used the program Fitmodel to examine the potential differences in the site-specific selection patterns among the various branches in the TET phylogenies. Fitmodel uses a maximum-likelihood phylogeny-based codon substitution model with parameters for switching between the selection processes across the phylogeny and along sites  [20, 27]. The results obtained with Fitmodel, similar to those of the codeml analysis using site models, suggested that purifying selection played an important role in the evolution of TET genes across the greater part of their evolutionary history. However, our present findings indicate that branchand site-specific shifts in selection occurred in each of the TET subfamilies.
The catalytic domain of TET protein is located in the carboxyl-terminal region and recognizes 5mC in a CpG context for enzymatic oxidation. Previous structural and mutagenesis studies of TET protein  [4, 12]  revealed the amino acid residues essential for enzymatic activity and cofactor orientation. Our analysis using the Fitmodel program and the site models in the PAML and Datamonkey programs identified five codon positions with significantly elevated ω values in the TET2 catalytic domain. These findings indicate that these positions could be especially important for the adaptive processes. Interestingly, we observed no overlap between the amino-acid residues that were proposed to be crucial for catalytic activity and the positively selected sites. This is expected because these residues are probably highly constrained as part of the dioxygenase function of TET2 protein.
However, it is important to note that all of the amino-acid sites identified as having undergone positive selection in the catalytic domain are in the protein's outer surface. We speculate that these positive-selection patterns imply that selection is probably acting on the enzyme surface (which interacts with other TET-interacting proteins), and thus positive selection appears to shift the regulatory scheme of TET enzyme function. We also compared the positively selected sites to the human UniProt entries to obtain natural variant information. Some of the positively selected sites were found to be located at, or in close proximity to a natural cancer variant. For example, positions 123, 817, 1204, 1762 and 1811 of TET2 were inferred to be have undergone positive selection by the Fitmodel software program and located at natural variants found in myeloproliferative neoplasms  [33].
Sequence Retrieval and Phylogenetic Reconstruction All homologous TET sequences were retrieved essentially as described in  [26]. We first collected the mammalian TET paralogs and orthologs from the GenBank (http://www. ncbi. nlm. nih. gov/genbank/) non-redundant protein database, using the human TET1, TET2 and TET3 amino acid sequences as the query and conducting separate searches with Blastp (http://blast. ncbi. nlm. nih. gov/Blast. cgi) [34]. For a further analysis, we retrieved the nucleotide and predicted amino acid sequences. We also downloaded orthologs for human TET1, TET2 and TET3 (using the Ensembl API v80 program, http://www. ensembl. org/info/docs/api/index. html) from the Ensembl Compara database (http://www. ensembl. org/info/genome/compara/index. html) [35]. We then screened the data sets we obtained in order to delete the redundant sequences, incomplete sequences, and any sequences with ambiguous base calls.
Thus, for the final analyses, 130 mammalian sequences were examined. Table  S1  lists the species and sequence accession numbers. We used the MAFFT program (http://mafft. cbrc. jp/alignment/software/) to align amino acid sequences along with parameter settings that were optimized for the iterative refinement method (FFT-NS-i)  [36]. We trimmed the resulting multiple sequence alignment by deleting the poorly aligned regions (using trimAl 1. 2 with the option "automated1")  [37]. A codon alignment was generated from the multiple sequence alignment of amino acid sequences (Figures  S5-S7 ) and the corresponding DNA sequences with the use of the PAL2NAL program  [38]. We used PhyML v3. 0 (https://code. google. com/p/phyml/) [39]  to build maximum likelihood trees after we identified the optimal model of sequence substitution by using jModeltest 2 (https://github.
Molecular Evolutionary Analysis We used a maximum likelihood approach to identify traces of positive selection and detect the amino acid sites that evolved under positive selection. The codeml program in the PAML package ver. 4. 8  [43]  and the Fitmodel program ver. 0. 5. 3 as described in  [20]  were used to this end. We performed the codeml analysis separately for the TET1, TET2 and TET3 alignments. We used site models that allow the ω ratio to vary among sites  [18, 19]. To investigate the significance of positive selection between each pair of models (i. e. , M1a vs. M2a and M7 vs. M8), we conducted likelihood ratio tests (LRTs) that employed a χ 2 distribution with two degrees of freedom. In the first pair of models, the null model M1a provides two codon site categories with corresponding proportions p 0 and p 1 , with ω 0 < 1 and ω 1 = 1 respectively.
As the criteria to identify codons under positive selection, we used a posterior probability value >0. 90 for the FUBAR method, the cut-off p-value of 0. 1 for the FEL, MEME and SLAC methods, and the Bayes Factor >50 for the REL method. For the separate analyses of the TET1, TET2 and TET3 alignments, we applied the Fitmodel software program, which uses Markov-modulated Markov models of codon evolution  [20] , or switching models. The switching models allow variations in the site-specific selection process along lineages of a phylogeny. For the present analysis we used the M0, M3, M3 + S1, and M3 + S2 models. In the M0 model, it is assumed that all of the sites have the same ω values. In the M3 model, it is assumed that there are three differing ω values (ω 1 < ω 2 < ω 3 ). If the switching rates between ω values (ω 1 to ω 2 , ω 1 to ω 3 and ω 2 to ω 3 ) are assumed to be equal, the switching model is designated as S1; if these values are not assumed to be equal, the switching model is designated as S2.
We conducted LRTs between each pair of nested models (i. e. , M0 vs. M3, M3 vs. M3 + S1, and M3 + S1 vs. M3 + S2). A χ 2 distribution was used to determine the significance of differences in the log-likelihoods. We evaluated the PP values of the third selection class (corresponding to ω 3 ) at each codon position and each branch by using the Fitmodel program, and the ouput was processed using a custom script in Perl. Natural variant information of sites under positive selection for each protein was obtained from UniProt Database  [46].
Quantifying the Convergent Amino Acid Substitutions and Divergent Amino Acid Substitutions We used the codeMLancestral package  [28]  and the Castoe et al. [28]  method to estimate the convergent and divergent amino acid substitutions. Using a JTT amino acid substitution model, we estimated the site-wise PP values of all of the possible convergent and divergent amino acid substitutions along each branch of the TET gene tree. The branch lengths of the TET gene tree were estimated using the PhyML v3. 0 software. To determine the convergence and divergence probabilities for all pairwise branch comparisons, we summed the probabilities of the convergent substitutions and the divergent substitutions for each amino acid site in the branch pair by using a custom script in Perl. We plotted the total PP values of convergence vs.
Localizer protocol and stimuli The voice localizer consists of 10 min and 20 s block design with forty 8-s long blocks of either vocal (20 blocks) or non-vocal (20 blocks) sounds from stimuli already used in  Belin et al. (2000). About 60% of the stimuli were recorded specifically for the localizer and the rest was taken from public databases available in year 2000 as well as from recordings of American English vowels from  (Hillenbrand et al. , 1995). For the current experiments, stimuli were presented using Media Control Functions (DigiVox, Montreal, Canada) via electrostatic headphones (NordicNeuroLab, Norway; or Sensimetrics, USA) at a comfortable level (80-85 dB SPL). The 40 blocks are intermixed with 20 periods of silence allowing the hemodynamic to relax (Fig. 1 ). The relative power spectra of voice and non-voice blocks after convolution are similar, avoiding bias due to data sampling.
fMRI scanning All scans were acquired on a 3 T Siemens (Erlangen, Germany) Tim Trio scanner at the Centre for Cognitive Neuroimaging (http://www. ccni. gla. ac. uk/), University of Glasgow. All fMRI data were acquired using a single-shot gradient-echo echo-planar imaging (EPI) sequence with the following parameters: field of view (FOV) = 210 × 210 mm 2 , 32 slices per volume, interleaved slices order, voxel size 3 × 3 × 3. 3 mm 3 , acquisition matrix 70 × 70, flip angle = 77°, echo time (TE) = 30 ms. The repetition time (TR) was 2 s with an acquisition time (TA) of 2 s resulting in quasi-continuous scanning noise. In total 310 EPI volumes were acquired. In addition to the EPI data, a highresolution 3D T1-weighted sagittal scan was acquired for each subject (voxel size 1 mm 3 isotropic; acquisition matrix 256 × 256 × 192).
fMRI data analysis Pre-processing MRI data were analyzed using SPM12b (r6080 -Wellcome Department of Cognitive Neurology, University College London). Pre-processing of functional scans consisted of slice timing (sinc interpolationreference slice 31, i. e. middle of the TR -  Sladky et al. , 2011) , motion correction (6 parameter affine transformation, realignment to the mean image, mean created using 4th spline interpolation), co-registration of the T1 image to the mean EPI (normalized mutual information), and of these images to the SPM average 152 T1 image (normalized mutual informationthis step allowed the zero coordinate to be as in the template space). Finally, functional (3 mm isotropic voxels) data were normalized to the Montreal Neurological Institute (MNI) space (segmentation, and diffeomorphic normalization using the forward deformation field computed during segmentation, data resampled at 2 mm isotropic with a 4th degree B-spline interpolation, i.
The contrast vocal N non-vocal was calculated at each vertex; (7) a threshold was obtained for these individual t-maps using a false discovery rate with q = 0. 05 and the default cluster forming threshold p = 0. 001  (Chumbley et al. , 2010) ; (8) the 10 largest peaks in each individual were identified in each hemisphere; this choice in the number of peak was arbitrary, but sufficiently large to reveal clustering of activity; (9) disks of 3 mm were created around each peak using functions from the Fast marching Toolbox (http://www. mathworks. com/matlabcentral/fileexchange/ file_infos/6110-toolbox-fast-marching) and the Surfing Toolbox (http://sourceforge. net/projects/surfing/) and a weight of 1; (10) the 10 disks were summed, resulting in a density map for each subject; (11) a permutation procedure was used in which the peaks were then randomly positioned within the surface (excluding the medial wall) 1000 times, and the max of the density was measured.
Analysis of test-retest reliability Ten subjects were scanned twice with a delay ranging from 1 to 27 days between scans. Intra-class correlation coefficients (ICC) have been computed for each voxel of the brain using the third class of ICC as defined by  (Shrout and Fleiss, 1979 ) ICC ¼ MSS-MSE MSS þ k-1 ð ÞMSE ð1Þ with MSS the mean square related to subjects and MSE the mean square error from a repeated measure ANOVA, and k is the number of scanning sessions. ICC can be viewed as a type of correlation. However, because it operates on data structured by groups rather than data structured as pairs, ICC is better seen as a measure of discrimination between subjects  (Bland and Altman, 1996). Theoretically, ICC describes how strongly observations from the same subjects resemble each other with value ranging from 1 (the signal amplitude is identical from session to session) to 0.