dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to evaluate the average performance, we plot the balanced accuracy averaged across all subjects in a database for each method as a function of the number of alignment trials. Since we are averaging across subjects, for this analysis we include only those subjects featuring at least 60% accuracy in a classical train-test cross-validation. This restriction excludes about half of the subjects, leaving 178 subject out of 349. It's important to note that subjects with an overall 60% accuracy usually have more than 70% accuracy with all available training trials. Figure  4  shows a representative plot for each paradigm. All figures are available as Supplementary material. Then, we summarize all the pair-wise accuracy information in the accuracy tables such as Table  2. These tables give for each target subject the accuracy averaged over numbers of alignment trials and source subjects.
For this database, there are 30 numbers of alignment trials considered and 18 possible source subjects, which makes an average over 540 values. This makes the standard error low in general. Accuracy tables for all databases are given as Supplementary material. The accuracy tables confirm what can be evaluated visually in the average accuracy plots and seriation plots; on the average there is about 1% difference between classical train-test crossvalidation accuracy and TSA and about 5% between classical train-test cross-validation and RPA. This speaks in favor of a clear improvement of the TSA method over the RPA method. Table  3  summarizes all the balanced accuracy for each dataset. On the average across databases there is no loss of accuracy using a TSA as compared to the optimal train-test accuracy.
This is not true for the RPA. Finally, we performed statistical tests on all pair-wise source/target accuracy results we have collected. To this end, we follow the procedure introduced in  Rodrigues et al. (2021). In a nutshell: we first compute signed paired t-test for every target subject comparing the accuracy between methods, yielding T statistics T m,i and p-values p m,i for each pair of methods m and target subject i. In order to correct for the multiplicity of statistical tests we use Holm's sequential rejection multiple test procedure  (Holm, 1979)  for each target subject. This produces tables such as Table  4. Corresponding tables for each database are available as Supplementary material. Then, we combine the p-values we obtain using the Stouffer's Z-score method  (Zaykin, 2011)  for each database, yielding multiple p-values corresponding to each pair of methods for each database.
Those p-values are also corrected by means of Holm's procedure and are summarized in Table  5. In this table we can see that among the 18 databases we have analyzed, 11 show a significant improvement of TSA as compared to RPA. paradigm are computed and corrected with Holm's procedure (Table  6 ). So far we have focused on cross-subject transfer learning, however the method we propose can also be used to transfer different information, such as from one session to another with the same subject or from one task to the another. In order to ensure the ability of our method to reduce the inter-session variability, we used the dataset having multiple sessions and  processed results for inter-session cross-validation. In order to do so, we used the data of one session as a source, then for each other sessions with 80% test and 20% training data split we trained the transfer learning model and tested the results.
The  processed is then repeated using each session as the source. We compared four different methods: • Tangent Space Alignment (TSA), our method, • Riemannian Procrustes Analysis (RPA) used as a comparison in this article,  • Usual train-test method using only target data, • Direct testing (DCT) using algorithms trained on the source without aligning target data by a rotation (recentering only). The results are given in Figure  5  and presented numerically in Table  7. These results are coherent with the previous ones. They yield accuracies slightly improved as compared to sessions mixed all together, which is expected. Moreover, the proposed method performs better than RPA on all databases with the exception of Zhou 2016.
Discussion The extensive analysis we have carried out shows that for the ERP paradigm TSA clearly outperforms RPA. For the MI paradigm we observe that TSA performs better than RPA and that the classical train-test cross-validation outperforms both TSA and RPA. For SSVEP, the classical train-test cross-validation outperforms both TSA and RPA. In global, RPA is outperformed by both TSA and the classical train-test cross-validation. Based on Table  6 , we do not conclude on the superiority of TSA as compared to train-test in terms of accuracy as the global value that can be found is mainly due to very large values found for the ERP paradigm. To conclude with the results, it has been shown that TSA outperforms RPA for the large majority of the databases we have used in this analysis, reaching an accuracy pretty close to the optimal train-test method.
It is also a clear improvement from a methodological point of view in comparison to RPA as it naturally allows transfer learning between datasets with different number of channels, where RPA needs some extensions in order to do so  (Rodrigues et al. , 2021). In this paper, we have introduced a new method for transfer learning inter and intra subject for brain computer interfaces. Our study indicates that it outperforms a state-of-theart analogous method (RPA). However, it still does not reach the same accuracy that can be achieved with a classical test-train cross-validation procedure for the motor imagery and SSVEP paradigm. Further research is needed to understand why the performance of the TSA method is clearly superior for the ERP paradigm. In terms of computation time, since we have a closed form for the rotation of TSA method, it is way faster than the RPA method, where an optimization on the Grassmann manifold is performed.
However, even if our method can be faster by one order of magnitude, with N c being the size of the pre-covariance signal (number of channels plus number of channels of the average target for ERP, number of channels for MI, number of classes times number of channels for SSVEP) we compute rotations with size [ N c * (N c +1) 2 , N c * (N c +1) 2 ] where RPA computes rotation with size (N c , N c ). This means that for datasets with a significant amount of channels the computational advantage of TSA will tend to vanish. Table  8  shows the average time of computation for each dataset if no spatial filter were to be applied before the covariance estimation, sometimes yielding big covariance matrices. Usually spatial filter are applied so the low time values are those that are to be usually encountered.
For the same reason, we are using all the data of a subject without making any adaptation from one session to the other. This choice obviously leads to reduced overall accuracy, resulting in an important decrease in the number of subjects used for the final average results. It is important to do so, however, since, as it has been found in previous studies, there is a large gap between "good" and "bad" subjects in transfer learning accuracy  (Zanini et al. , 2018; Rodrigues et al. , 2019). Another point to mention is that even though our method has been tested extensively on many databases, there are even more databases to test on. Additionally, some paradigms such as affective BCI have not been investigated in this article. Investigation on cross-database transfer learning is still to be done.
Sampling Protocol Data presented in this study were collected either in snowpits or along transects. Snowpits: Albedo was measured first since it requires a pristine area. A minimum of 3 measurements were made depending on sky conditions and light variations. All of them were performed facing the sun to avoid any shadow from the operator 195 and the equipment. Irradiance profiles were them measured when thick snowpacks were studied, with minimal perturbation to the snow surface. When irradiance profiles were performed, the pit was then dug so as to intersect the hole made for the irradiance profile. All stratigraphic measurements were carried out along a one-meter-long shaded trench. Our objective was to conduct all samplings at the same place in order to fully characterize physical and optical properties of the snow at each station.
Data processing Upwelling and downwelling irradiance raw acquisitions require several processing steps before the albedo can be obtained. During the field campaigns, spectra were visually checked at the end of the sampling day. Unrealistic data, based on 210 qualitative criteria (spectra shape, range of values, signal to noise ratio), were rejected. The first step of processing was to remove the systematic offset in both acquisitions caused by dark current and stray light effects. This offset was approximated for each acquisition as the mean signal at short wavelength (between 200 nm and 260 nm), because there is no incoming photon in this wavelength range. Dark current was assumed to be constant over the entire wavelength range. Then, spectra were divided by their corresponding integration time.
For each measurement site, it was checked that all spectra correctly overlapped before being averaged. For the 2015 dataset, the average standard deviation of all integrated albedos (over the 400-1000 nm wavelength range) measured at each snowpit is 0. 3% with a maximum of 1%. Thus, in most cases, it is reasonable to assume that the 225 precision in albedo measurements is better than 1%. This uncertainty does not account for external error sources such as varying illumination conditions or unexpected shadows or reflections.
440 Furthermore, LAPs and in particular MD particles were probably for the most part deposited during wind events and they may have been diluted in the thicker dune layers relative to elsewhere. Lastly, LAP concentrations in 2016 may have been slightly different from those in 2015. The values used, although slightly higher than measurements, are thus reasonable. Only data from the 2015 campaign are presented since this dataset is more comprehensive and covers the first three phases. As mentioned in the previous section, snow grain size impacts albedo mostly in the near infrared while the impact of snow 445 thickness is mainly observed in the visible. Thus, both measurements and simulation results are compared at 500 nm and 1000 nm. They are presented in Table  3  and Figure  9  where simulations with and without LAPs are shown.
The largest differences are for the last two dates. These are during phase III when melting was extensive and rapidly evolving, with visible short-scale variations. Since albedo measurements cover about 1 m 2 while density and SSA values 455 are from point measurements, an imperfect coincidence between both data sets is expected. Albedo and snowpit measurements are also done in sequence, so that variations between both measurements are possible, perhaps even likely. If we remove these last two measurements from the comparison, Figure  9  shows that at 500 nm, the difference between measurement and model is <1% (absolute value) for 25 out of 31 values. At 1000 nm, the difference is <0. 04 (absolute value) also for 25 out of 31 values, and <0. 02 for 16 values. During phase I, average simulations at 500 nm are essentially 460 identical to measurements, while during phase II they are below measurements by 0.
Acknowledgements 620 The field campaign was successful thanks to the contribution of  J. Ferland ,  G. Bécu ,  C. Marec ,  J. Lagunas ,  F. Bruyant ,  J. Larivière ,  E. Rehm ,  S. Lambert-Girard ,  C. Aubry ,  C. Lalande ,  A. LeBaron ,  C. Marty ,  J. Sansoulet ,  D. Christiansen-Stowe ,  A. Wells ,  M. Benoît-Gagné ,  E. Devred  and  M. -H. Forget  from the  Takuvik laboratory ,  C. J. Mundy  and  V. Galindo  from  University of Manitoba & F. Pinczon du Sel  and  E. Brossier  from  Vagabond. We also thank  Michel Gosselin ,  Québec-Océan , the  CCGS Amundsen  and the  Polar Continental Shelf Program  for their in-kind contribution in terms of polar 625 logistics and scientific equipment.
The glass transition temperatures (T g s) were taken as the midpoint of the heat capacity changes. Vibrational Spectroscopy: Attenuated total reflectance Fouriertransform infrared spectroscopy (ATR-FTIR) was performed using a Bruker Alpha II compact spectrometer, with a diamond crystal, 2 cm -1 resolution, 300 scans, and a range of 400-2000 cm -1. All the measure-ments were performed inside an argon-filled glove box. Fourier-transform Raman spectroscopy (FT-Raman) was performed using a Bruker Multi-Ram spectrometer equipped with a Nd-YAG 1064 nm laser at an operating power of 400 mW. The spectra were recorded with 2 cm -1 resolution, for 6000 scans, and between 200 and 2000 cm -1. The region of the Raman spectrum containing the TFSI "all breathing mode," 700-800 cm -1 , was deconvoluted using the PeakFit software and the second derivative method, employing Voigt functions, to account for the Gaussian and Lorentzian contributions from the instrument and different environment effects.
INTRODUCTION Quantifying macroevolutionary processes, for example, diversification rates, and testing macroevolutionary hypotheses, such as whether speciation rate shifts are driven by environmental changes and/or trait evolution, require robust reconstructions of the genealogical relationships of the members of the clade in question. It is increasingly clear that it is preferable to reconstruct evolutionary histories based on both extant and extinct organisms  (1, 2)  and to combine morphological and molecular data  (3, 4). Yet, empirical datasets that combine molecular, morphological, and substantial amounts of fossil data are still rare. This is, in part, because molecular sequencing efforts have been disproportionately focused on organisms with relatively poor fossil records (e. g. , birds, some insects, and plant groups), while those with good fossil records are somewhat neglected (e.
Sampling and taxon identification The procedure summarized herein follows  (20)  closely with minimum modifications. Colonies whose sequences are presented here were collected and preserved in 70 to 96% ethanol (table  S1 ). Each colony, preliminarily identified to the lowest possible taxonomic level using a stereoscope, was subsampled for DNA isolation and scanning electron microscopy (SEM). While we aimed at sequencing a colony for each distinct species, uncertainty in initial taxon identification using a stereoscope combined with a realization that within-taxon replicates are important for sequence verification compelled us to include such replicates. The scanning electron micrographs (see SEM cards deposited in Zenodo, https://zenodo. org/record/5721078#. YZz39VMo_fY), taken with a Hitachi TM4040PLus after bleaching to remove tissue where appropriate, are required for species-level identification and serve as digital vouchers, in addition to physical vouchers deposited at the Natural History Museum in Oslo (table  S1 ).
Annotation Mitogenomes for each of the samples were annotated with Mitos2 using a metazoan reference (RefSeq 89) and the invertebrate genetic code  (56)  to identify two rRNA [mitochondrial large-subunit (16S) ribosomal RNA (rrnL) and mitochondrial small-subunit (12S) ribosomal RNA (rrnS)] and 13 protein coding genes (atp6, atp8, cox1, cox2, cox3, cob, nad1, nad2, nad3, nad4, nad4l, nad5, and nad6). Two nuclear rRNA operon genes (18S and 28S) were also identified and annotated using RNAmmer  (57). A total of 315 published cheilostome sequences  (20, (58) (59) (60)  and the mitogenomes and rRNA operons of 31 non-cheilostome out-group taxa, both bryozoan and nonbryozoan, were aligned with our sequences to compile a broader out-group taxon sample (table  S3 ). Sequence alignment MAFFT (61) was used for alignment with default parameters: For the four rRNA genes (nucleotide), the Q-INS-i model, considering second ary RNA structure, was used; for the 13 protein-coding genes, in amino acid format, the G-INS-I model was used.
The 17 separate align ments were edited manually using Mesquite v3. 61 to remove any uncer tain characters  (62). Ambiguously aligned characters were removed from each alignment using gBlocks  (63)  with least stringent parameters. The single-gene alignments were concatenated to a supermatrix using the catfasta2phyml perl script  (64). The alignments (both masked and unmasked) are available at Dryad (https://doi. org/10. 5061/dryad. 2v6wwpzp9).
Datasets for phylogenetic reconstruction As mentioned in the "Sampling and taxon identification" section, cheilostomes are small and attached to substrata, so even the most macroscopically pristine sample may have sequences of nontarget Downloaded from https://www. science. org on  March 30, 2022  species included. Where the contaminants are non-cheilostome, they are removed bioinformatically (see the "DNA isolation, sequencing, and assembly" section). Here, we use the nontarget cheilostome sequences (see also the "Sampling and taxon identification" section). Hence, two concatenated datasets are presented: (i) "Full alignment" is the alignment with our largest taxon sample. It includes both UNKNOWN [cheilostomes lacking both a voucher (physical and/or SEM) and an inferred taxonomic identity] and SEQ [cheilostomes lacking a voucher (SEM) but with an inferred taxonomic identity], constructed to show the hidden diversity within the phylum (fig.
S1 ). Full alignment also includes cheilostomes previously sequenced and available from NCBI and non-cheilostome and non-bryozoan out-groups (table  S2 ). (ii) "Trimmed alignment" is the alignment where "UNKNOWN", SEQ, and those taxa with less than three genes and "rogue taxa" are pruned using RogueNaRok  (65). We picked a three-gene cutoff after preliminary analyses showed that this is the best compromise between the number of taxa included and BS support for our tree inference. Rogue taxa are those with unstable phylogenetic affinities based on evaluation of the extended majority rule consensus threshold, optimized for support and with a maximum dropset size of 1. Those with a sum >0. 2 were pruned from the trimmed alignment dataset. Note that the ML tree topologies are termed as "full tree" and "trimmed tree" from the full alignment and trimmed alignment datasets, respectively.
Phylogenetic reconstruction and congruence test Maximum likelihood phylogenetic analyses were carried out for each single-gene alignment using the "AUTO" parameter in RAxML v8. 0. 26  (66)  to establish the evolutionary model with the best fit. The general time reversible (GTR + G) was the preferred model for the four rRNA genes (18S, 28S, rrnS, and rrnL), and MtZoa + G for all 13 protein coding genes. The two concatenated datasets (see the "Datasets for phylogenetic reconstruction" section) were divided into four separate rRNA and 13 protein gene partitions each (17 partitions in total), with its own distinct gamma distribution to accommodate for different substitution patterns among sites, and were analyzed using RAxML. For comparison, a partitioning scheme based on Akaike Information Criteria with small sample correction (AICc) and a greedy search scheme suggested by PartitionFinder2 (67) was also analyzed using RAxML.
Fossil calibration and Bayesian divergence time estimation We use MCMCTree v4. 9 (70) for divergence time estimation, as it allows us to analyze amino acid and nucleotide partitions simultaneously and takes relatively less computational power than other comparable software. As input to MCMCTree, we use the trimmed tree (fig. S2 ); but to reduce computational burden further, we removed the following taxa: (i) those lacking species and/or genus designations and those assigned "cf. and aff. " and (ii) species duplicates with the largest number of alignment gaps. If a genus is represented by multiple species, then a maximum of three different named species were retained, choosing those with the least number of alignment gaps. Note that this dataset was created before minor changes detailed in the Supplementary Materials (section S4 in Supplementary Text for Methods).
Excluded taxa were deleted from the amino acid and nucleotide alignments, while corresponding leaves for the same taxa were removed using Dendroscope  (68) , thus maintaining the topological branching pattern of the original rooted input tree (fig. S2 ). The resulting dataset consisted of 363 taxa where 335 are cheilostomes. We applied a hard upper limit of 636 Ma ago to the root, representing the bilaterian maximum  (71, 72). We used 18 internal fossil calibrated nodes (see table  S4  and fig. S3  for details). To explore the impact of calibration prior choice, we ran different sets of analyses: (i) "L," with minimum constraints only (table  S4 ), using the truncated Cauchy distribution with a soft minimum and a diffuse tail; (ii) "B," with uniform constraints with soft minimum and maximum bounds corresponding to the fossil ages; and (iii) "ST," with a skew T distribution, such that the 1 and 99% probability tails correspond to the minimum and maximum constraints (table  S4 ).
Ancestral state reconstruction and HiSSE Data for nonbrooding species, with planktotrophic larvae (state = 0) and brooding species with nonfeeding larvae (state = 1) of all the cheilostomes species included in the calibration tree (N = 335), are provided in table S5. To estimate brooding states of the internal nodes, we use a standard Markov model of binary character evolution  (23)  implemented in ape  (73) , where a maximum likelihood joint estimation procedure was performed. Note that, although the tree for the analyses described here is pruned, the nonbrooding/brooding states that we are concerned are conserved at the genus level. To detect possible differences in diversification rates associated with the nonbrooding or brooding state, we applied Downloaded from https://www. science. org on  March 30, 2022  trait-dependent speciation and extinction models implemented in the R package HiSSE  (25)  to the fossil calibrated tree (N = 355), where we used the STL calibration with an independent clock model (see the "Fossil calibration and Bayesian divergence time estimation" section).
Acknowledgments: We thank  C. Seid  ( Scripps Institution of Oceanography ),  A. M. Bemis  ( University of Florida ),  S. Mills  ( NIWA ),  K. Moore  ( Tasmanian Museum and Art Gallery ),  U. S. Coast Guard, California Department of Fish and Wildlife , and all collectors named in the attached SEM cards, but especially  M. Capa ,  M. Dick ,  M. Zabala ,  C. Avila , and  R. Tan. The staff at  Heron Island Research Station (University of Queensland ) and at the  Research and Educational Station "Belomorskaya"  ( Saint Petersburg State University ) supported us for fieldwork. P. D. Taylor  and  J. E. Winston  discussed earlier versions of this phylogeny with us. We thank  A. -H. Rønning ,  B. L. G. Thorbek , and the  Norwegian Sequencing Centre  and the  High-Performance Computing Cluster at the University of Oslo  for assistance.
Avila ). B. F. was supported by  Beatriu de Pinós  ( 2019-BP-00183 ), funded by the  AGAUR (Government of Catalonia ) and by the  Horizon 2020 programme  under the  Marie Skłodowska-Curie grant  (no. 801370 ). The  CAML-CEAMARC cruise of RV Aurora Australis  ( IPY  project no. 53) were supported by the  Australian Antarctic Division , the  Japanese Science Foundation , the  French Polar Institute IPEV , and the  Muséum  national d'Histoire naturelle, led by voyage leader,  M. Riddle. Author contributions: R. J. S. O. performed the preparation for sequencing, the bioinformatics, and phylogenetic analyses. E. D. M. and M. H. R. did the vouchering work. E. D. M. provided the calibration points. E. D. M. and D. P. G. drafted the interpretations. R. C. M. W. contributed to the fossil calibration.