dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE167245, accessed on 22 March 2021). Supplementary Data: RNA-Seq and methylome data are available at GSE167245 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE167245, accessed on 22 March 2021).
) or else derived from the data. A wide range of data-driven techniques are available, such as PCA, ICA (Independent Component Analysis,  Hyvärinen et al. 2009 ), beamforming,  (Sekihara et al. , 2006) , CCA  (Canonical Correlation Analysis, Hotelling 1936) , CSP (Common Spatial Patterns,  Koles et al. 1990) , regression, and so-on. Two phases should be distinguished: training, in which the model is fit to the data, yielding the analysis matrix U, and testing in which that matrix is applied. These phases are confounded if the matrix is trained and applied to the same data set (as in common parlance "PCA was applied to the data"). Training instead on one dataset and testing on another (cross-validation) allows to control for overfitting, which occurs when a model fits details of the training set that do not generalize to new data.
In this way, SCA serves as a "plug-in replacement" for PCA, retaining dimensions most shared rather than those with greatest variance. Implementation. Computation is saved by operating on the data covariance matrix rather than the data themselves, producing the analysis matrix U. The computation cost is dominated by eigenvalue decomposition in step 2, O(J 3 ) or better, yielding a cost of O(J 4 ), or O(J J 3 ) if J components are calculated. A Matlab implementation is provided by the function nt_sca. m of the NoiseTools toolbox (http://audition. ens. fr/adc/NoiseTools/).
Projections of all segments are concatenated, and the correlation is calculated between concatenated signal and concatenated projections. Estimates Simulations with synthetic data. Four simulations were carried out using three synthetic datasets (A to C) to better understand the problems addressed. The first simulation illustrates spurious correlation and its dependency on dimensionality, number of samples, and spectral content, using dataset A. The second illustrates the effect of overfitting on the empirical correlation between a known signal vector and a data matrix in which that signal is mixed, using dataset B. The third compares PCA and SCA, also using dataset B. The fourth also compares PCA and SCA, using dataset C. Dataset A (noise, no signal) consisted of a "data" matrix of size T × J, J = 1024, and a "probe" signal matrix of size T × 1, with T taking values from 1000 to 10000 samples.
Probe and data were unrelated, i. e. their samples were taken from uncorrelated Gaussian distributions. The data matrix was submitted to PCA and subsets of N pcs PCs were selected, with N pcs varied as a parameter. The probe was then projected on the span of the selected PCs, and the empirical correlation between the probe vector and the projection vector was estimated, with and without cross-validation. In one condition, probe and data were additionally low-pass filtered with a Butterworth filter of order two and cutoff 10 or 1 Hz (assuming a sampling rate of 500 Hz), to illustrate the effect of serial correlation. Dataset B (single signal) consisted of a data matrix of size T ×J, T = 5000, J = 1024, obtained by mixing a matrix of "noise" sources of size T × J, via a mixing matrix M n of size J × J, together with a "signal" vector of size T ×1 via a mixing matrix M s of size 1×J.
The data matrix was submitted to PCA (resp. SCA) and the empirical correlation between the signal and subsets of N pcs PCs (resp. N scs SCs) was estimated. Dataset C (multiple signals) consisted of a "signal" source matrix of size T × I, T = 10000, I = 128, consisting of sinusoids with different frequencies (thus mutually orthogonal), mixed into a data matrix of same size via a specially-crafted mixing matrix. The first source was mixed into all J channels with amplitude 1, the second two sources were mixed into randomly-chosen subsets of J/2 channels also with amplitude 1, the next four sources into subsets of J/4 channels, and so-on. The last 64 sources were each mixed into one channel. This simulates a dataset for which some sources are shared among channels, and the rest are channel-specific.
The number of sources per channel depended on the random assignment of sources to channels; here, it ranged from 0 to 11. Uncorrelated Gaussian noise was added to all channels, also with amplitude one, so the data were full rank. Each channels was normalized to unit variance and then multiplied by a normallydistributed random number, to ensure that variance is a non-informative cue. The data matrix thus obtained was submitted to PCA (resp. SCA) and the empirical correlation between selected signals and subsets of N pcs PCs (resp. N scs SCs) was estimated. Real data. Two datasets are used, both from a study that aimed to characterize cortical responses to speech for both normal-hearing and hearing-impaired listeners. Sixty four channel EEG responses to speech and tones were recorded in 44 subjects  (Fuglsang et al.
, 2020). Full details can be found in that paper, and the data are available from http://doi. org/10. 5281/zenodo. 3618205. The EEG data were preprocessed by the Zapline algorithm to remove power line artifacts (de Cheveigné, 2019), high-pass filtered at 0. 5 Hz using an order-2 Butterworth filter, and lowpass filtered at 30 Hz using an order-2 Butterworth filter. To remove eyeblink artifacts, a temporal mask was derived from the absolute power on a combination of two EOG channels and three frontal channels (F1, F2, Fz), and the eyeblink components were removed as described by de Cheveigné and  Parra (2014). We apply PCA and SCA prior to applying two data-driven algorithms (DSS and CCA, as defined below), to evaluate and compare their ability to avoid overfitting. between each trial and the average over trials, both normalized to unit variance.
This measure ranges from 0 (all trials identical) to 2 (uncorrelated signals, i. e. no repetition across trials): the smaller the value of d 2 , the better. The DSS algorithm is data-dependent and, like regression, prone to overfitting. To control for overfitting, the DSS model was fit to subsets of M -1 trials, the optimal spatial filter was applied to the M th trial, and d 2 was calculated between that trial and the average over the M -1 other trials. This was repeated for all M permutations, and the resulting estimates were averaged. To reduce overfitting, prior to DSS the data were first submitted to PCA (resp. SCA), and DSS was then applied to a subset of N PCs (resp. SCs). The relative merits of PCA and SCA are assessed by comparing the minima of d 2 across N : the lower, the better.
Dataset E (speech-evoked EEG) consisted of EEG in response to M =22 segments of continuous speech, each of 50' duration, of which 16 are used here. In addition to data in response to a single talker, the database included also data for two talkers ("concurrent speech"), not used here. The EEG data are provided together with the temporal envelope of the speech stimulus, calculated by a model of instantaneous loudness. The speech temporal envelope has been shown to be a predictor of cortical responses  (Lalor et al. , 2009; Ding and Simon, 2012; Di Liberto et al. , 2015; Crosse et al. , 2016). The EEG data were cut into 16 trials of 50 s duration, each individually detrended by subtracting a 2nd-order polynomial robustly fit to each channel (de Cheveigné and Arzounian, 2018). The stimulus envelope was filtered by the same combination of high-pass and low-pass filters as the EEG, to avoid introducing a convolutional mismatch.
Synthetic data The dataset consists of a "data" matrix of Gaussian noise and a "probe" signal vector nominally uncorrelated with the data (Methods). The data matrix was submitted to PCA, and the empirical correlation between the probe and subsets of selected PCs was estimated with and without cross-validation. For finite T , this empirical correlation is expected to be non-zero. Figure  2  (left) shows correlation as a function of N pcs without (thin line) and with (thick line) cross-validation. Without cross-validation, the correlation score approaches 0. 5 for large N pcs , despite the fact that the probe and data are drawn from independent processes. This is a classic example of overfitting: more channels or components makes it easier for the fitting algorithm (regression) to find some linear combination of the data that is well correlated with the probe.
With cross-validation, the correlation estimate is not biased in this way, although it hovers at some distance from zero due to residual (unbiased) correlation. The spurious empirical correlation is larger when there are fewer samples (small T ) than more (Fig. 2 , center). Furthermore, for a given T , it is larger if the data are not spectrally white (Fig. 2 , right). This is because the serial correlation characteristic of spectrally-colored data reduces the "effective" number of samples in the data. This simulation illustrates the well-known fact that overfitting increases with the number of dimensions (N pcs ), decreases with the number of data samples (T ), and is exacerbated if the data are not spectrally-white. Regression and overfitting. The second simulation used dataset B (single signal in noise).
That dataset consists of a "signal" vector and a simulated data matrix within which this signal has been mixed together with noise (Methods). The purpose of this simulation is to illustrate the fact that some datasets benefit from dimensionality reduction, others not: there is no "one size fits all" strategy. Real  data can differ widely in how signal components are distributed over dimensions within the data, in particular whether they coincide with dimensions of low noise variance, or instead high noise variance. To simulate these two situations, the noise mixing coefficients were randomly distributed between 0 and 1 (i. e. some channels had high noise variance, others low), and the signal was mixed with a gain factor that depended on the noise variance. In Fig. 3  (left), the signal was present only within channels with high noise variance (the signal gain factor was 0 for channels in which the noise mixing coefficient was less than 0.
SCA can be more effective than PCA. This third simulation also used dataset B (single signal in noise). Figure  4  compares the values of correlation obtained with PCA (black) and SCA (red) as a function of the number of components retained. Dotted lines are without cross-validation, full lines are with cross-validation, and the two panels are for data with different properties. For the left panel, the signal was carried by the 30% of channels with lowest variance (gain=0. 02). For the right panel, it was carried by the 30% of channels with highest variance (gain=0. 1) (Methods). These parameter values are chosen for visual clarity. Focusing on cross-validated correlation (full lines), at N = 1024 the values for PCA and SCA are equal as expected because for both the N components span the full data.
This simulation used dataset C (multiple signals in noise), in which 128 sinusoidal sources are mixed into 128 channels together with Gaussian noise, such that the first source is mixed into all channels, the second two are each mixed into 64 randomly-chosen channels, and subsequent sources into progressively smaller numbers of channels (Methods). This mimics EEG or MEG data where some sources have a wider spatial extent than others. The data were submitted to SCA (resp. PCA), subsets of contiguous SCs (resp. PCs) starting at 1 were selected, individual sources were projected on these subsets, and cross-validated empirical correlation was estimated between source and projection. Figure  5  (right, dotted lines) shows, for the first 12 sources, the correlation between that source and its projection on subsets of PCs (black) or SCs (red).
Real data EEG response to tones. This simulation used dataset D (tone-evoked EEG) which consisted of EEG responses to 180 repetitions of a tone, measured from 44 subjects (Methods). PCA (resp. SCA) was applied to the preprocessed data and subsets of N PCs (resp. SCs) were selected before applying the DSS algorithm (Methods) to find an optimal spatial filter to enhance repeatability (de Cheveigné and  Parra, 2014). The DSS algorithm is effective to suppress the background EEG activity that masks the regularly repeating brain response to each tone (compare Fig. 6  left and center). As a data-driven algorithm, DSS is prone to overfitting (the algorithm can find particular linear combinations that reoccur by chance over trials), which is why it is worth considering reducing dimensionality before applying it.
Dataset D (tone-evoked EEG), from that study, consisted of EEG in response to a sequence of M =180 tones (tone-evoked response). Those data were cut into 180 trials, each starting 1s before and ending 2s after tone onset. Each trial was individually detrended by subtracting a 1st-order polynomial robustly fit to the first and last second of the trial (de Cheveigné and Arzounian, 2018). The first and last seconds worth of each trial were then discarded, leaving a one-second interval post onset for each trial. Outlier trials (contaminated by glitches) were removed based on their Euclidean distance from the average over trials. The trial matrix was processed by the DSS (denoising source separation) algorithm (de Cheveigné and Parra, 2014) to obtain a transform (spatial filter) to maximize repeatability across trials, thus maximizing the SNR of our observation of the brain response (based on the assumption that this response is the same on every trial).
In the final session they then explicitly evaluated emotional attributes of the stimuli by rating their perceived valence, arousal, and intensity along four emotional categories and performed a 4-alternative forced-choice categorization for Anger, Fear, Disgust, or Pleasure. Emotional stimuli consisted of brief (796ms) synthetic vocalizations generated by morphing between recordings from a validated database of affective bursts  16  that portrayed angry, fearful, disgusted, pleased, and neutral expressions of the vowel /a/. Morphing combined pairs of expressions with weights varying in 25% steps from 0 to 100% as well as each emotional expression with the neutral expression weighted between 100% (neutral) to 0 (original emotion) to -25% (emotional caricature). This resulted in 39 stimuli that sampled densely the space of perceived emotions (Fig.
The analyses of this study did not consider replicates as done in e. g. , biological research on animal models. In other words, we did not assess statistically our questions on a separate group of participants investigated with the same experimental design as all others that was then discarded from the analyses reported in this study to prevent distortions in the inferential framework  26. However, we aimed to boost the replicability of our main conclusions by relying on the analysis of three different datasets collected on each of the experiment participants (behavior, fMRI and MEG).
Stimuli Stimuli consisted of nonverbal emotionally expressive vocalizations from the Montreal Affective Voices database  16  and were produced by two actors (one male, one female). Each actor produced five vocalizations (vowel /a/) either with an emotionally neutral intention, or expressing anger, disgust, fear or pleasure. Vocalizations normalized in root mean square (RMS) amplitude were then used to generate the stimulus set by morphing between each pair of vocalizations from the same speaker (each stimulus morphed to the same duration of 796 ms, corresponding to the average duration of the sound stimuli in the ten selected unmorphed samples from the MAV database). Voice morphing was performed using STRAIGHT  27  in Matlab (Mathworks, Inc, Natick, USA). STRAIGHT performs an initial time-by-time pitch-adaptive spectral smoothing in each stimulus to separate the contributions to the voice signal arising from the glottal source or from the supra-laryngeal filtering.
948, SEM = 0. 108, percentile bootstrap 95% CI = 0. 784/1. 176; T(9) for disgust = 12. 95, p < 0. 001, Fisher Z correlation = 1. 024, SEM = 0. 079, bootstrap 95% CI = 0. 887/1. 180; T(9) for fear = 12. 72, p < 0. 001, Fisher Z correlation = 0. 898, SEM = 0. 071, bootstrap 95% CI = 0. 753/1. 011; T(9) for pleasure = 11. 45, p < 0. 001, Fisher Z correlation = 0. 829, SEM = 0. 072, bootstrap 95% CI = 0. 694/0. 964). Following previous studies, all subsequent analyses considered the category intensity ratings to avoid confounding genuine differences between perceived category and dimensions with differences in the measurement scale and reliability of categorization and rating data  14, 15. We subsequently assessed the effect of voice morphing on perceived emotion attributes (five morphing % parameters describing each experimental stimulus -one for each expressed emotion and one for the neutral vocalization; six measures of perceived emotion -four emotion category intensity ratings and valence and arousal ratings).
Preprocessing of neuroimaging data Analyses were carried out in Matlab using SPM12, Fieldtrip  33  , GLMdenoise  34  and custom code. The initial preprocessing of fMRI and MEG data produced for each participant the stimulus-specific responses further analyzed to assess the representation of perceived emotions (see below). Functional MRI images from all runs were realigned to the first image in the first run, unwarped to correct for movement-by-distortion interactions (full width at half maximum -FWHM = 5 and 4 mm for realignment and unwarp, respectively; for both 7th degree B-spline for interpolation), and slice time corrected to the onset of the temporally central slice. Anatomical volumes were co-registered to the grand-average of the preprocessed functional volumes and segmented into grey matter, white matter, and cerebro-spinal fluid.
Infrequent SQUID jumps (observed in 2. 3% of the channels, on average) were repaired using piecewise cubic polynomial interpolation. For each participant independently, we then removed channels that consistently deviated from the median spectrum (shared variance < 25%) on at least 25% of the runs (N removed channels = 8. 4 on average; STD = 2. 2). Runs associated with excessive head movements or MEG channels noise or containing reference channel jumps were finally discarded, leaving on average 75. 9 runs per participant (range = 65-84; average maximum coil movement across blocks and participants = 5 mm; STD = 1 mm). Environmental magnetic noise was removed using regression based on principal components of reference channels. Both the MEG and reference data were then filtered using a forward-reverse 70 Hz FIR low-pass (-40 dB at 72.
Analysis of cerebral representational geometries We implemented a whole-brain searchlight representational similarity analysis (RSA  17  ; Fig. 2 ) to assess the spatiotemporal representational dynamics of perceived emotions. To this purpose, we relied on a method of statistical multimodal fusion of representations in multivariate spatial (fMRI) and spatiotemporal (MEG) cerebral response patterns. Importantly, we combined the complementary strengths of fMRI (high spatial resolution) and MEG (high temporal resolution) by adopting a method that builds on recent work on the fusion of same-participant fMRI and MEG data 3738. For this purpose, we used an initial mask of fMRI correlations to constrain spatially subsequent tests on time-varying MEG RDMs at searchlight-center locations within the fMRI masks (see below for details; note that the constraining procedure operates only on the selection of the RDM-specific searchlights, i.
Visualization The non-metric 2D MDS models in Fig. 1 , 3 and 4 were computed using the R-package SMACOF  43. We modeled the dissimilarity ratings and the emotion RDMs (Fig  3 ) and the cerebral RDMs (Fig  4 ) using an inter-individual difference scaling model (INDSCAL; participant-and speaker-specific RDMs as inputs), avoiding well known distortions of the representational geometry associated with group averaging of distance data  44. Importantly, before being modelled with INDSCAL the cerebral RDMs were biased towards the cerebral variance diagnostic of perception (emotion and perceived dissimilarity RDMs) using the leave-one-participant-out cross-validation scheme described in 31 meant to minimize the impact of perception-independent variance (e. g. , measurement noise) on the INDSCAL solution.
Acknowledgements Supported by  UK's Biotechnology and Biological Sciences Research Council  (grants  BB/M009742/1  to JG, BLG, SAK, and PB, and  BB/L023288/1  to PB and JG), by the  French Fondation pour la Recherche Médicale  (grant  AJE201214  to PB), and by Research supported by grants  ANR-16-CONV-0002  ( ILCB ),  ANR-11-LABX-0036  (BLRI), and the  Excellence Initiative of Aix-Marseille University  (A*MIDEX). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank  Olivier Coulon  and  Oliver Garrod  for help with the development of the 3D glass brain, as well as  Yinan Cao ,  Ian Charest ,  Carlos Crivelli ,  Beatrice De Gelder ,  Guillaume Masson ,  Robin A. A. Ince ,  Flor Kusnir ,  Stephen McAdams , and  Robert Zatorre  for useful comments on previous manuscript versions.
Data availability The following materials are available at a Dryad repository (https://datadryad. org/stash/ dataset/doi:10. 5061/dryad. m905qfv0k): single-trial behavioural data, single-cross-validation fold fMRI data, and single-trial MEG data for all participants; anonymized anatomical information required to reconstruct the MEG sources and deform native-space statistical maps to Dartel and MNI space; sound stimuli and modulation transfer function representations.
Historically, virus taxonomy has been limited to describing viruses that were readily cultivated in the laboratory or emerging in natural biomes. Metagenomic analyses, singleparticle sequencing, and database mining efforts have yielded new sequence data on an astounding number of previously unknown viruses. As metagenomes are relatively free of biases, these data provide an unprecedented insight into the vastness of the virosphere, but to properly value the extent of this diversity it is critical that the viruses are taxonomically classified. Inclusion of uncultivated viruses has already improved the process as well as the understanding of the taxa, viruses, and their evolutionary relationships. The continuous development and testing of computational tools will be required to maintain a dynamic virus taxonomy that can accommodate the new discoveries.
Introduction Few viruses are readily cultivated under laboratory conditions and even fewer cause noticeable outbreaks of disease. Over a century of virus research has resulted in an extremely biased view of global virus diversity and a limited, patchy, and non-systematic picture of the taxonomy of viruses, but viral metagenomic datasets can illuminate the true extent of the virosphere. Specifically, when previously described viral genome sequences are included in a clustering analysis together with the viral sequences obtained from metagenomics, the known sequences tend to fall within a limited subset of the clusters  [1] [2] [3] [4] [5] [6]. In less than a decade, the analysis of genomic sequences of uncultivated viruses, mostly derived from metagenomes, has led to a surge in virus discovery, providing invaluable new data with limited bias for the identification and characterization of viruses.