dataesr/scientific-paragraphs-categorization huggingface dataset:
The results of BAMM are congruent with our other estimates, supporting a diversification rate shift at the root of the coregroup, pointing at an increasing rate of diversification compared to the background dynamics. The configuration with two shifts of diversification rate corresponding to the partitioning analyzed in the manuscript (i. e. rate shifts at the root of the coregroup and the Melinaea-group) was recovered as the third most frequently sampled configuration but with the lowest support (~10%) after a configuration with no rate shift (19%) (Figure  S3. 3). For the time-dependent diversification models  (Morlon et al. 2011) , maximum likelihood searches consistently converged toward similar parameter estimates over a range of initial parameters (results not shown). The LTT plots of the backbone tree as defined by MEDUSA (i.
S3. 2 Diversification in the Andes To investigate the pattern of diversification in the Andes with respect to the rest of the Neotropical region, we classified species as Andean or non-Andean, based on a combination of published  (Chazot et al. 2016a , Chazot et al. 2016b , De-Silva et al. 2016 , Chazot et al. 2014 , De-Silva et al. 2010 , Elias et al. 2009 , Brower et al. 2006 , Mallarino et al. 2005 , Whinnett et al. 2005)  and unpublished (i. e. , databases generated from museum collections and our own field collections) georeferenced distribution data and elevation ranges of species (Appendix S1. 2). In general species can be unambiguously classified into either of the two categories, because Andean species are never found in the lowlands, whereas species that occur in the lowlands and that sometimes also occur at the Andean foothills never occur above ca.
This project was funded by an  ATIP (CNRS, France)  grant awarded to ME, with  LDS  as a postdoc. NC was funded by a doctoral fellowship from  Ecole Doctorale 227 (France)  and  BECC (Biodiversity and Ecosystem  services in a Changing Climate). ME acknowledges additional funding by the  ANR  grant  SPECREP  ( ANR-14-CE02-0011-01 ). JM acknowledges funding from  NERC  and  BBSRC. We thank the authorities of Peru, Ecuador and Brazil (SISBIO n°  10438-1 ) for providing research and collection permits, as well as many assistants and colleagues for their help in the field and with databasing museum specimens. Molecular work was performed at the  GenePool (University of Edinburgh, UK ),  CBMEG-Unicamp (Brazil)  and the  Service de Systématique Moléculaire   UMS2700  ( 2AD ) of the MNHN We thank  Fabien Condamine  and  Hélène Morlon  for constructive discussions about time-dependent diversification analyses.
Such a dataset is currently not available and its realization would be a great contribution to advance the current knowledge in cardiovascular physiology in health and disease. Reference values for blood flow rates in the eye. A reference value of Q r ¼ 40:9ml=min for retinal blood flow can be found in  [25]. This value results from a review of the experimental measurements reported in  [71] [72] [73] [74] [75]. Similarly, a reference value of Q cl ¼ 133ml=min for the blood flow in the ciliary body can be found in  [76]. This value was estimated from measured levels of aqueous-to-plasma ascorbate ratio and aqueous production rate  [77, 78]  The choice of a reference value for the choroidal blood flow is less obvious due to the lack of experimental measurements. Here we choose a value of Q ch ¼ 368ml=min, which is within the same range as L cra;3 0.
In silico docking analysis Studies to understand the mode of binding of kaempferol to IRE1α was carried out using AutoDock suite 4. 0. Crystal structure of the human IRE1α bound with ADP (PDB ID: 3P23) was used as the target protein. Ligands were obtained from Pubchem as. sdf format and converted to. pdb using smiles translator. Polar hydrogen atoms were assigned for the macromolecule prior docking. Gasteiger charges for ligands and Kollman charges for the receptor molecule was added using AutoDock tools 1. 5. 6. Throughout the study, the macromolecule was kept rigid with rotatable bonds assigned for the ligands. The grid center made on the macromolecule covering the entire surface of the protein acted as search space. AutoGrid 4. 0 was used to produce the map files of the flexible atoms and docking parameter file (DPF) was generated using Lamarckian Genetic algorithm of AutoDock 4.
, 2012). The conformation of DFG (Aspartic acid-Phenylalanine-Glycine) motif of kinase domain in IRE1α with an activator for endoribonuclease activity should be in DFG-in conformation rather than DFG-out  (Wang et al. , 2012). The ADP bound crystal structure of IRE1α (PDB ID: 3P23), with DFG-in motif showing favorable binding affinity for docking with kaempferol suggests its ability to modulate the IRE1α endoribonuclease activity. Moreover, docked pose of kaempferol falls into the same pocket where ADP and APY29 are known to interact with the kinase domain of IRE1α (Fig. 4E ). Collectively, the docking analysis showed that kaempferol binds to the kinase domain of IRE1α and could activate the endoribonuclease activity. To confirm the docking results further, ADP-Glo kinase assay (Promega) was performed using recombinant kinase active human IRE1α.
Red circles represent the common amino acid residues making interactions with all three compounds. Dashed green lines represent the hydrogen bonding between the amino acid residue and the compound. E. Docked poses of ADP, kaempferol and APY29 at DFG-in confirmation (D711, F712 and G713) of human IRE1α (PDB ID: 3P23). Superimposed docked poses and interaction analysis was performed using UCSF chimera.
Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. * The first two authors contributed equally to this work. Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly.
Indeed, generative models can be leveraged to learn intermediate representations or for data augmentation  [52]. In transfer learning, the model parameters can also be transferred from one subject to another or from task A to task B. This might drastically widen or change the applicability of several EEG-based technologies. On the other hand, there are various reasons why DL might not be optimal for EEG processing and that may justify the skepticism of some of the EEG community. First and foremost, the datasets typically available in EEG research contain far fewer examples than what has led to the current state-of-the-art in DL-heavy domains such as computer vision (CV) and NLP. Data collection being relatively expensive and data accessibility often being hindered by privacy concernsespecially with clinical data -openly available datasets of similar sizes are not common.
Methods English journal and conference papers, as well as electronic preprints, published between January 2010 and July 2018, were chosen as the target of this review. PubMed, Google Scholar and arXiv were queried to collect an initial list of papers to be reviewed. 3 Additional papers were identified by scanning the reference sections of these papers. The databases were queried for the last time on July 2, 2018. The following title and abstract search terms were used to query the databases:   OR 18). The papers were then included or excluded based on the criteria listed in Table  2. To assess the eligibility of the selected papers, the titles were read first. If the title did not clearly indicate whether the inclusion and exclusion criteria were met, the abstract was read as well. Finally, when reading the full text during the data collection process, papers that were found to be misaligned with the criteria were rejected.
Following a first inspection of the papers with the data extraction sheet, data items were added, removed and refined. Each paper was initially reviewed by a single author, and then reviewed by a second if needed. For each article selected, around 70 data items were extracted covering five categories: origin of the article, rationale, data used, EEG processing methodology, DL methodology and reported results. Table  3  lists and defines the different items included in each of these categories. We make this data extraction table openly available for interested readers to reproduce our results and dive deeper into the data collected. We also invite authors of published work in the field of DL and EEG to contribute to the table by verifying its content or by adding their articles to it.
Data The availability of large datasets containing unprecedented numbers of examples is often mentioned as one of the main enablers of deep learning research in the early 2010s  [53]. It is thus crucial to understand what the equivalent is in EEG research, given the relatively high cost of collecting EEG data. Given the high dimensionality of EEG signals  [105] , one would assume that a considerable amount of data is required. Although our analysis cannot answer that question fully, we seek to cover as many dimensions of the answer as possible to give the reader a complete view of what has been done so far. To simplify the figure, some of the categories defined in Fig. 4  have been grouped together.
Subjects Often correlated with the amount of data, the number of subjects also varies significantly across studies (see Fig. 7 ). Half of the datasets used in the selected studies contained fewer than 13 subjects. Six studies, in particular, used 10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 Recording ti e ( in) Affecti Nu ber of e)a ples 10 -2 10 -1 10 0 10 1 10 2 10 3 10 4 Ratio (e)a ples/ in) datasets with a much greater number of subjects:  [132, 160, 188, 149]  all used datasets with at least 250 subjects, while  [22]  and  [49]  used datasets with 10,000 and 16,000 subjects, respectively. As explained in Section 3. 7. 4, the untapped potential of DL-EEG might reside in combining data coming from many different subjects and/or datasets to train a model that captures common underlying features and generalizes better.
In  [202] , for example, the authors trained their model using an existing public dataset and also recorded their own EEG data to test the generalization on new subjects. In  [191] , an increase in performance was observed when using more subjects during training before testing on new subjects. The authors tested using from 1 to 30 subjects with a leave-one-subject-out cross-validation scheme, and reported an increase in performance with noticeable diminishing returns above 15 subjects.
Recording parameters As shown later in Section 3. 8, 42% of reported results came from private recordings. We look at the type of EEG device that was used by the selected studies to collect their data, and additionally highlight low-cost, often called "consumer" EEG devices, as compared to traditional "research" or "medical" EEG devices (see Fig. 8a ). We loosely defined lowcost EEG devices as devices under the USD 1,000 threshold (excluding software, licenses and accessories). Among these devices, the Emotiv EPOC was used the most, followed by the OpenBCI, Muse and Neurosky devices. As for the research grade EEG devices, the BioSemi ActiveTwo was used the most, followed by BrainVision products. The EEG data used in the selected studies was recorded with 1 to 256 electrodes, with half of the studies using between 8 and 62 electrodes (see Fig.
They concluded, however, that the position of the referential channels is very important as well, making it difficult to compare across datasets coming from different neurologists and recording sites using different locations for the reference(s) channel(s). Similarly, in  [26] , the impact of different electrode configurations was assessed on a sleep staging task. The authors found that increasing the number of electrodes from two to six produced the highest increase in performance, while adding additional sensors, up to 22 in total, also improved the performance but not as much. The placement of the electrodes in a 2-channel montage also impacted the performance, with central and frontal montages leading to better performance than posterior ones on the sleep staging task. Furthermore, EEG sampling rates varied mostly between 100 and 1000 Hz in the selected studies (the sampling rate reported here is the one used to record the EEG data and not after downsampling, as described in Section 3.
Also, all three are from 2018, perhaps showing an emerging interest in data augmentation. First, in  [192] , Gaussian noise was added to the training data to obtain new examples. This approach was tested on two different public datasets for emotion classification (SEED  [227]  and MAHNOB-HCI  [159] ). In  [152] , the authors explicitly targeted the class imbalance problem of under-represented sleep stages by generating Fourier transform (FT) surrogates of raw EEG data on the CAPSLPDB dataset. They improved their accuracy up to 24% on some classes. An additional 30 papers explicitly used data augmentation in one form or another but only a handful investigated the impact it hae on performance. In  [82, 15] , noise was added to 2D feature images, although it did not improve results in  [15]. In  [76] , artifacts such as eye blinks and muscle activity, as well as Gaussian white noise, were used to augment the data and improve robustness.
Preprocessing Preprocessing EEG data usually comprises a few general steps, such as downsampling, band-pass filtering, and windowing. Throughout the process of reviewing papers, we found that a different number of preprocessing steps were employed in the studies. In  [71] , it is mentioned that "a substantial amount of preprocessing was required" for assessing cognitive workload using DL. More specifically, it was necessary to trim the EEG trials, downsample the data to 512 Hz and 64 electrodes, identify and interpolate bad channels, calculate the average reference, remove line noise, and high-pass filter the data starting at 1 Hz. On the other hand, Stober et al. [164]  applied a single preprocessing step by removing the bad channels for each subject. In studies focusing on emotion recognition using the DEAP dataset  [81] , the same preprocessing methodology proposed by the researchers that collected the dataset was typically used, i.
Reporting of results The performance of DL methods on EEG is of great interest as it is still not clear whether DL can outperform traditional EEG processing pipelines  [105]. Thus, a major question we thus aim to answer in this review is: "Does DL lead to better performance than traditional methods on EEG?" However, answering this question is not straightforward, as benchmark datasets, baseline models, performance metrics and reporting methodology all vary considerably between the studies. In contrast, other application domains of DL, such as computer vision and NLP, benefit from standardized datasets and reporting methodology  [53]. Therefore, to provide as satisfying an answer as possible, we adopt a two-pronged approach. First, we review how the studies reported their results by focusing on directly quantifiable items: 1) the type of baseline used as a comparison in each study, 2) the performance metrics, 3) the validation procedure, and 4) the use of statistical testing.
In the second case, authors will instead cite previous literature that reported results on the same task and/or dataset. This second option is not always possible, especially when working on private datasets or tasks that have not been explored much in the past. In the case of typical EEG classification tasks, state-of-the-art approaches usually involve traditional processing pipelines that include feature extraction and shallow/classical machine learning models. With that in mind, 67. 9% of the studies selected included at least one traditional processing pipeline as a baseline model (see Fig. 15 ). Some studies instead (or also) compared their performance to DL-based approaches, to highlight incremental improvements obtained by using different architectures or training methodology: 34. 0% of the studies therefore included at least one DL-based model as a baseline model.
In  [34] , the authors used a large dataset and tested the performance of their model both on new (unseen) subjects and on known (seen) subjects. They obtained 38% accuracy on unseen subjects and 75% on seen subjects, showing that classifying EEG data from unseen subjects can be significantly more challenging than from seen ones. In  [184] , the authors compared their model on both intra-and inter-subject tasks. Despite the former case providing the model with less less training data than the latter, it led to better results. In  [62] , the authors compared different DL models and showed that cross-subject (37 subjects) models always performed worse than within-subject models. In  [127] , a hybrid system trained on multiple subjects and then fine-tuned on subject-specific data led to the best performance.
Comparison of results Although, as explained above, many factors make this kind of comparison imprecise, we show in this section how the proposed approaches and traditional baseline models compared, as reported by the selected studies. We focus on a specific subset of the studies to make the comparison more meaningful. First, we focus on studies that report accuracy as a direct measure of task performance. As shown in Fig. 12a , this includes the vast majority of the studies. Second, we only report studies which compared their models to a traditional baseline, as we are interested in whether DL leads to better results than non-DL approaches. This means studies which only compared their results to other DL approaches are not included in this comparison. Third, some studies evaluated their approach on more than one task or dataset.
Reproducibility Reproducibility is a cornerstone of science  [111] : having reproducible results is fundamental to moving a field forward, especially in a field like machine learning where new ideas spread very quickly. Here, we evaluate ease with which the results of the selected papers can be reproduced by the community using two key criteria: the availability of their data and the availability of their code. From the 156 studies reviewed, 54% used public data, 42% used private data  6  , and 4% used both public and private data. In particular, studies focusing on BCI, epilepsy, sleep and affective monitoring made use of openly available datasets the most (see Table  6 ). Interestingly, in cognitive monitoring, no publicly available datasets were used, and papers in that field all relied on internal recordings.
Data A critical question concerning the use of DL with EEG data remains "How much data is enough data?". In Section 3. 3, we explored this question by looking at various descriptive dimensions: the number of subjects, the amount of EEG recorded, the number of training/test/validation examples, the sampling rate and data augmentation schemes used. Although a definitive answer cannot be reached, the results of our meta-analysis show that the amount of data necessary to at least match the performance of traditional approaches is already available. Out of the 156 papers reviewed, only six reported lower performance for DL methods over traditional benchmarks. To achieve these results with limited amounts of data, shallower architectures were often preferred. Data augmentation techniques were also used successfully to improve performance when only limited data was available.
However, more work is required to clearly assess their advantages and disadvantages. Indeed, although many studies used overlapping sliding windows, there seems to be no consensus on the best overlapping percentage to use, e. g. , the impact of using a sliding window with 1% overlap versus 95% overlap is still not clear. BCI studies had the highest variability for this hyperparameter, while clinical applications such as sleep staging already appeared more standardized with most studies using 30 s non-overlapping windows. Many authors concluded their paper suggesting that having access to more data would most likely improve the performance of their models. With large datasets becoming public, such as the TUH Dataset  [65]  and the National Sleep Research Resource  [217] , deeper architectures similar to the ones used in computer vision might become increasingly usable.
However, it is important to note that the availability of data is quite different across domains. In clinical fields such as sleep and epilepsy, data usually comes from hospital databases containing years of recordings from several patients, while other fields usually rely on data coming from lab experiments with a limited number of subjects. The potential of DL in EEG also lies in its ability (at least in theory) to generalize across subjects and to enable transfer learning across tasks and domains. When only limited data is available, intra-subject models still work best given the inherent subject variability of EEG data. However, transfer learning might be the key to moving past this limitation. Indeed, Page and colleagues  [127]  showed that with hybrid models, one can train a neural network on a pool of subjects and then fine-tune it on a specific subject, achieving good performances without needing as much data from a specific subject.
Reported results Our meta-analysis focused on how studies compared classification accuracy between their models and traditional EEG processing pipelines on the same data. Although a great majority of studies reported improvements over traditional pipelines, this result has to be taken with a grain of salt. First, the difference in accuracy does not tell the whole story, as an improvement of 10%, for example, is typically more difficult to achieve from 80 to 90% than from 40 to 50%. More importantly though, very few articles reported negative improvements, which could be explained by a publication bias towards positive results. The reported baseline comparisons were highly variable: some used simple models (e. g. , combining straightforward spectral features and linear classifiers), others used more sophisticated pipelines (including multiple features and non-linear approaches), while a few reimplemented or cited state-of-the-art models that were published on the same dataset and/or task.
Since the observed improvement will likely be higher when comparing to simple baselines than to state-of-the-art results, the values that we report might be biased positively. For instance, only two studies used Riemannian geometry-based processing pipelines as baseline models  [11, 87] , although these methods have set a new state-of-the-art in multiple EEG classification tasks  [105]. Moreover, many different tasks and thus datasets were used. These datasets are often private, meaning there is very limited or no previous literature reporting results on them. On top of this, the lack of reproducibility standards can lead to low accountability: since study results are not expected to be replicated and results can be inflated by non-standard practices such as omitting cross-validation. Different approaches have been taken to solve the problem of heterogeneity of result reporting and benchmarking in the field of machine learning.