dataesr/scientific-paragraphs-categorization huggingface dataset:
technical Validation Taxonomic and status validation. Each species name found in a given information source was confronted to the valid and synonym species names lists from FishBase and Eschmeyer's Catalog of Fishes to ensure the identifications validity provided by the information source. This taxonomic validation identified 1,332 synonyms, 781 typing errors and 294 unknown species names (names not listed in any of the two scientific catalogues). The original scientific names of the species are reported in the expanded table of the database ('CompleteDatabase'), where users can extract sub-species, synonyms or unknown species names. After having validated the taxonomic names, we further verified the presence certainty in the Amazon Basin of all the taxonomically valid species recorded in our database.
This careful review was an essential step in the elaboration of the database and resulted in assigning a status to each species. The species status is based on the information provided by the data source, expert opinion from the AmazonFish partners and information about the species general distribution available in FishBase or Eschmeyer's Catalog of Fishes catalogues. When the presence of a taxon was inconsistent with its actual known distribution, the species was classified as 'to be verified'. A recently published database on the global distribution of freshwater fish species  5  was also consulted to verify the overall distribution of each species, their exotic status and to identify species endemic to the Amazon Basin. As a result, the database provides not only information on the validity of each species, but also on species occurrences and names that need further attention ('to be verified' and 'unknown name at present').
This gives the opportunity for database users to refer to their own expertise and knowledge to validate or not the accuracy of the original source, species name and distribution (ideally, giving feedback to the AmazonFish project, https:// www. amazon-fish. com/). species distribution validation. The geographic coordinates of the sites were compared to the location name of the sub-basin given in the source. In case of mismatch, the coordinates were removed from the database and the information was kept only at the sub-basin grain and referenced as 'geographic error'. The geographic accuracy of the species distribution (for 'native' and 'valid' species) inside the Amazon Basin was checked using a basic geographic analysis. A convex hull envelop was delineated for each species based on its occurrence points, resulting in a list of sub-drainages potentially occupied by a given species.
This list was then compared to the list of sub-drainages where the species had at least one record. From this comparison, circa 200  species showed some inconsistent distributions (outlying occurrences). All these occurrences were consequently carefully checked and further validated or excluded (see 'ExcludedOccurrences' file  31  ). Major Tributary Area Number of sites Number of occurrences Number of Families Number of Genera Number of species Number of endemic species Amazonas 100 Updates and limitations. The database is obviously not complete and definitive, and we aim to keep the high-quality level of the database with regular updates, ideally with bi-annual steps, depending on human and financial resources. More than 100 new fish species were described between 2017 and 2019, which makes this update effort crucial in order to improve our knowledge about the distribution of freshwater fish within the Amazon Basin.
The technical and taxonomic validation procedures described above will be applied to any new information included in the database. Three main factors will be considered in future updates: (1) new or previously non-available data sources with species lists or records; (2) occurrences of newly described species; and (3) nomenclature changes in the taxonomic classification. If the main rivers of the Amazon Basin appear well surveyed, some gaps do exist, however in various parts of the basin (Fig. 2 ). These gaps are mainly located in zones either difficult to access due to the topography and/or located in protected areas (indigenous lands or protected areas). Identifying never-sampled (to our knowledge) or under-sampled sub-drainages is a first step to guide increasing sampling efforts in these areas.
The AmazonFish project has already initiated this process, by supporting the numeric digitalization of the national freshwater fish Fig. 2  (a) Number of records, (b) density of sites (number of sites divided by the sub-drainage area and areas without information using the HydroBASINS Level7 spatial grain unit  30  ), (c) total number of species and (d) number of endemic species (species present only in the Amazon Basin and only in the sub-drainage) for the 144 sub-drainage units. collections from Peru  25, 26  and by initiating sampling campaigns in detected gaps in Colombia, Peru and Brazil. All these spatial gaps in the database will also be prioritized in future updates through literature and web-based sources checking. Researchers holding fish distribution data from any of the current gaps or under-sampled areas (Fig.
Introduction Action recognition is still a challenging problem in the area of computer vision despite its numerous applications in sports analysis, video surveillance, and human-machine interaction  [2, 3]. A popular way to represent action data is through its description based on 3D skeleton sequences, which are the 3D positions of body joints through time. This representation medium can efficiently describe the underlying dynamics of the actions  [4]. Recently, the development of motion capture technologies such as the Microsoft Kinect  [5]  along with the existing robust pose estimation methods  [6]  has considerably facilitated the collection of skeleton-based datasets. These technological advancements have motivated considerable research efforts being put into this area  [7, 8]. In particular, skeleton-based action recognition has become an interesting problem for many deep learning algorithms such as convolutional neural networks (CNNs)  [9, 10, 11]  and recurrent neural networks (RNN)  [8, 12, 13].
RNN methods can learn the temporal dynamics of the sequential data; nevertheless, they have practical shortcomings in the training of their stacked structures  [14, 15]. Compared to RNN architectures, CNN-based methods provide more effective solutions by extracting local features from their input and finding discriminative patterns in the data  [16, 10]. However, regardless of the promising feature extraction capability of CNN, its specific convolutional structure is designed originally for image-based input data and primarily relies on spatial dependencies between the neighboring points. In contrast, such a direct relationship does not generally exist in skeleton-based action datasets. Although some works tried to solve this problem by using 1-dimensional filters (only for the temporal dimension), it is still not an efficient solution to this specific shortcoming of CNN-based frameworks  [17].
Fine-Prediction Module According to the structure of 1D-CNN, each pooling layer downsamples the feature maps by a factor of 2. Hence, for a network with q deep layers, the prediction map O has the resolution of 2 q time-Figure  6 : Fine-prediction module of DACNN. The skip-connections from score-maps of specific Abs-filters result in fine-grained segmentations compared to Figure  5. frames, which reduces the accuracy of the network, especially in the segment borders. Therefore, to have a fine-grained prediction map, we employ a specific skip-connection structure throughout the fine-prediction module in our DACNN framework (Fig. 1 ). As illustrated in Fig. 6 , we add the score maps obtained from the Abs-filters (from the alignment layer) to the upsampling process of 1D-CNN. More precisely, for the Abs-filters of size p = 2 qt, ∀q ≥ 2, we downsample their alignment map V p by a max-pooling of size p and feed it to a score-conv layer.
Datasets CMU  Mocap [33] : This human motion capture dataset is a an extensive collection of different human activities which are recorded by the Vicon system. The data is collected from 144 subjects each of which performs a sequence of different actions in separate sessions. The segmentation is performed on each session and is evaluated based on the Jaccard index measure. We collect the sessions which contain actions from 15 highly observed categories in the data including walk, punch, wave, run, jump, and raise, and consider the rest of actions as the blank spaces (gaps). The training set contains 75% of the sessions, while the rest is left for testing. [34]  This dataset is related to the "ChaLearn Looking at People" challenge which is recorded with Kinect technology as described by  [34].
Montalbano V2 dataset SYSU-3D Human-Object Interaction dataset (SYSU)  [35] : This dataset contains 12 different action classes recorded in 480 video sequences. SYSU dataset is captured from 40 human subjects, and in each time-frame, it represents the 3D coordinates of 20 body joints. We create the test and train batches by splitting the number of subjects in half over 10 randomized splits. [36] : This action recognition dataset consists of 60 classes of actions captured from 40 human subjects. It has 56,000 sequences with 4 million frames in total, and the recorded data of 25 main body joints are used for the action recognition task. There are two typical evaluation protocols for this benchmark: The Cross-Subject (CS) recognition task which uses data of 20 subjects for training and the rest for testing, and the Cross-View (CV) task in which the recorded samples from camera 2 and 3 constitute the training set and the rest is preserved for the test set.
Implementation Details In the implementation of DACNN, we choose the kernel size of t = 3 for Al-filters and 1 × 3 for the conv-filters. This choice of t for Al-filters provides them enough angular freedom to learn any local curvature in the data. We also apply the dropout  [37]  of rate 0. 65 to prevent DACNN from overfitting. We train DACNN using the Adam approach  [38] , and the skeleton data is normalized per dimension which allows the filters to be aligned to local parts of different joints. Regarding the implementation of the baseline algorithms, either we use their publicly available codes and tune their parameters with cross-validation on training set, or we refer to their reported results in the relevant publications. For each dataset, we use its typical evaluation setting reported in the literature.
CMU Mocap: The segmentation performance of DACNN on CMU Mocap dataset is evaluated by its comparison to SSSM  [18]  and HACA  [19]  as unsupervised temporal segmentation approaches, and to ConvS2S  [16] , End2End  [39] , LSTM-CRF  [40] , and RNN-CRF  [41]  as the deep sequence labeling frameworks. In the latter group, we replace the word-embedding parts with the quaternion values of the time-frames. As reported in Table  1 , the performances of deep learning algorithms have substantial distances from the unsupervised methods. Compared to them, DACNN outperforms the best method (End2End) with a notable margin of 4. 1%. This result supports the effectiveness of the DACNN architecture regarding the supervised segmentation of skeleton-based action data. In Fig. 7 , the segmentation results of some baselines are visually evaluated on one of the challenging sequences from the CMU dataset (Subject 86, trial 03).
In comparison to the unsupervised method SSSM, the supervised algorithms better identify the gaps (insignificant actions) because they are optimized by the relevant label information in the training phase. Additionally, SSSM finds subclusters in some of the segments (e. g. , walk and kick) which is not the desired outcome in supervised temporal segmentation. The algorithms DACNN and DACNN-nf (without fine-prediction unit) have fewer mistakes in the results, especially in the gap areas. Besides, DACNN obtains less overlapping between the segments and better predictions in the gap areas compared to DACNN-nf, which emphasizes the positive effect of the fine-prediction module in DACNN. In comparison, End2End has lower performance than DACNN and DACNN-nf regarding the gap areas and segments. Montalbano V2 dataset: For this dataset, we compare DACNN to the following baselines regarding the temporal segmentation accuracy: YNL  [34] , Terrier  [34] , Quads  [34] , Ismar  [34]   Labeling  [42] , Moddrop  [44] , and CNN+LSTM2  [43].
The majority of these algorithms need to perform a separate segmentation on the data prior to the training phase, but the DACNN's training is independent of that preprocessing phase. According to Table  1 , our proposed method obtained higher performance than the baselines (2. 9% margin in the Jaccard index), while the best alternative approach (Moddrop) has a relevantly complex network architecture and uses 3 different modalities (video, Mocap, and audio).
Action Recognition Results SYSU dataset: For the empirical evaluations, we compare our DACNN algorithm to other baselines including CNN+DPRL  [28] , ST-LSTM+Trust Gate  [13] , Dynamic Skeletons  [35] , LAFF(SKL)  [45] , SR-TSL  [46] , VA-LSTM  [47] , and GCA-LSTM  [48] , which includes the most recent deep learning applications (CNN, LSTM, etc. ) on this dataset. In Table  2 , our proposed DACNN framework outperforms the state-of-the-art with a 2. 3% margin. NTU dataset: For the NTU dataset, we evaluate DACNN in comparison to the state-of-theart methods from the literature: HBRNN-L  [8] , Dynamic Skeletons  [35] , LieNet-3Blocks  [49] , Part-aware LSTM  [36] , ST-LSTM+Trust Gate  [13] , CNN+LSTM2  [43] , Two-Stream RNN  [15] , STA-LSTM  [12] , GCA-LSTM (stepwise)  [48] , Clips+CNN+MTLN  [10] , View invariant  [14] , CNN+DPRL  [28] , VA-LSTM  [47] , ST-GCN  [11] , CNN+LSTM  [50] , Two-Stream CNN  [9] , and SR-TSL  [46].
As we can see in Table  3 , DACNN did not beat SR-TSL and CNN+LSTM in recognition accuracy. Nevertheless, It still achieves a competitive result compared to other recent state-of-the-art algorithms, such as ST-CGN, CNN+DRPL, and VA-LSTM, and even outperforms CNN+LSTM in the CS settings. It is important to notify that, the NTU dataset is recorded in a very constrained experimental setting, which is an advantage for the methods which considerably rely on the spatial processing of human poses (such as SR-TSL and CNN+LSTM).
Interpreting the Abs-filters Apart from the action recognition and segmentation performance, it is interesting to visualize the learned Abs-filters of DACNN in order to investigate any semantic pattern among them. and interpretation of its trained filters (Abs-filters). Specifically, we associate filter f p i to class c if c = arg max c X∈class c v p (i, :)| X 2 2 , (7) in which v p (i, :)| X denotes the i-th row of the alignment map V p after applying f p i on the dimensions of X. In Fig. 8 , we visualize some of the Abs-filters learned by DACNN after being trained on the NTU dataset. These filters are mostly related to the action classes walking, waving, sitting, jumping, and throwing. It is clear that each filter has learned a semantic subsequence from one joint of the whole action. For instance, one Abs-filter is aligned with the movement of the left foot in the jumping action before the subject leaves the ground, or another filter has learned half of a walking cycle on the right foot.
Ablation Study To study individual roles of the different modules in DACNN (Fig. 1 ), we perform an ablation study by repeating the recognition experiments (Sec. 5. 4) for the variants of DACNN framework (Table  4 ). In DACNN-nf, we remove the fine-prediction module, and we do not employ any Abs-filter in DACNN-Al. The DACNN-1D network is similar to DACNN-Al, but it uses 1D-conv filters instead of the Al-filters. According to the results in Table  4 , the lower performance of DACNN-nf compared to DACNN shows the positive effect of the Abs-filter skip-connections (Fig. 6 ) in improving the accuracy of the final prediction in 1D-CNN. Nevertheless, the accuracy of DACNN-nf is still close to DACNN and is even higher than the stateof-the-art for SYS-3D and Montalbano. Removing the Abs-filters from DACNN causes a notable decrease in the performance of DACNN-Al, which justifies the significant role of these filters in extracting the key patterns in the data.
Conclusions In this work, we proposed a deep-aligned convolutional neural network for skeleton-based action recognition and temporal segmentation. This network is constructed upon introducing the novel concept of temporal alignment filters for CNNs. These filters are efficient choices for classification of skeleton-based motion data compared to regular convolution filters. They extract crucial local patterns in the temporal dimensions of the data to better discriminate the action classes. Besides the competitive performance of our DACNN framework compared to the state-of-the-art, its extracted features (learned Abs-filters) are easily interpretable regarding their semantic contents. Our empirical evaluation of DCANN on different SBARS benchmarks support our claims regarding the performance and benefits of our network.
However, do these "markers" only signal sequence position, or are they also informative about the identity of the upcoming and/or missing stimuli? Neural activity patterns for anticipated events are informative about stimulus identity. To determine whether the brain activity patterns that we observed above for anticipated stimuli are merely generic signals marking the progress of the sequence, or whether they actually carry information about the identity of one or more upcoming stimuli, we performed a cross-temporal generalization analysis. In this analysis, a multivariate "stimclassifier" was trained on EEG data to predict stimulus identity. This stim-classifier was then tested on 1) the ISI periods before trial onset, and 2) the post-trial onset periods of catch trials (i. e. , in the absence of stimulus input).
In order to familiarize participants with the task and experiment layout, they were shown 10 trials (stim and catch trials), of another sequence than the one they had to learn, prior to the start of the experiment. EEG acquisition and preprocessing 64-channel EEG was recorded using a BioSemi Active-Two system at a sampling rate of 1024 Hz. A three-channel EOG was also recorded to monitor horizontal and vertical eye movements and blinks. Data were downsampled offline to 200Hz and notch-filtered at 50Hz to remove electrical line noise. We defined trial start as the onset of the presentation of a stimulus (on stim trials), the gray square (on catch trials), or the two choice stimuli (test trials). We extracted epochs from 500ms before trial onset to 1000ms post trial onset, yielding epochs of 1500ms, in which 0ms represents the trial onset.
Classification analysis: cross-temporal generalization from ISI periods to catch trials (Fig. 2C) In the cross-temporal generalization we performed the same procedure as in temporal generalization but the classifier was trained and tested on independent sets of data. Two cross-temporal generalization analyses were performed. In the first analysis (Figure  2C ), a 6way classifier was trained to discriminate between the ISI periods, and tested on catch trials. The ISI intervals were split into 6 groups, depending on which event they preceded. More precisely, the classifier was trained on ISI data at time point t and tested at time points t' of the catch trials. This cross-decoding method allowed us to test whether selective patterns of neural activity generalize across conditions  (Reddy et al.
, 2009). Therefore, for one cross-validation fold we used all ISI periods preceding stim trials, all ISI periods preceding test trials, but only 4/5 th of the ISI periods preceding catch trials. The remaining 1/5 th of ISI periods preceding catch trials was left-out in this cross-validation fold. The catch trials, i. e. the post-trial onset period following the left-out ISI periods) were used as the test dataset in this cross-validation fold. This selection procedure was repeated five times in order to use all ISI periods preceding catch trials in the training dataset and all catch trials in the testing dataset. Therefore, for each event (e. g. ISI-face) and each cross-validation fold the training dataset was composed of 112 (all ISI preceding stim trials) + 90 (ISI preceding catch trials, i.
e. 4/5 th of the total 112 ISI periods preceding catch trials) + 23 (all ISI preceding test trials) = 225 single trials (on average across participants). For the testing dataset, for each event (e. g. catchface) and each cross-validation fold, 1/5 th of all catch trials were used, which amounts to 22 trials. As for other classification analyses, we created 5 pseudo-trials per event, separately for the training and testing datasets. For the training dataset we therefore averaged 45 trials per ISI event to create one pseudo-trial. For the testing dataset we therefore averaged 4 trials per catch event to create one pseudo-trial. The classifier was then trained on all 5 pseudo-trials per ISI event x 6 ISI events = 30 pseudo-trials for training, and tested on the 5 pseudo-trial per catch event x 6 catch events = 30 pseudo-trials for testing.
Classification analysis: cross-temporal generalization from stim to ISI and catch trials (Fig. 3 ) In the second cross-temporal generalization analysis (Fig. 3 ), a 6-way classifier was trained on only the stim trials (stim-classifier), to discriminate between the six images, at each time point t. It was then tested in two separate analyses on 1) time points t' of the ISI periods and 2) t'' of the catch trials. This analysis allowed us to determine whether sensory stimulus information recorded when participants were viewing the stimuli corresponded to brain patterns induced by purely top-down expectations of these stimuli (in the absence of sensory input). The section "Preceding trial type control" above explains which time points of stim trials were used for training the stim classifier. This procedure was repeated for each training time point of the training dataset and it yielded six 2-dimensional maps of classifier predictions (training vs.
SIMULATION PROCEDURE We simulated DD and TD phylogenetic trees using the Gillespie algorithm, as implemented in functions dd_sim and td_sim, respectively, from the R package DDD 4. 3  (Etienne et al. 2012). We set λ 0 = 0. 8 and K = 40 following  Etienne et al. (2012) , so that trees reach carrying capacity after around 10 myr in the absence of extinction. We then simulated trees for different crown ages, to capture different phases of the radiation relative to equilibrium diversity: exponential growth with little diversity dependence (5 myr), equilibrium diversity reached recently (10 myr), or sometime in the recent (15 myr) or ancient (60 myr) past. Varying the crown age with fixed λ 0 and K thus allowed us to consider trees with an increasing (DD or TD) slowdown signal. For each age, we considered four scenarios with increasing levels of extinction ( μ 0 = 0.
However, many metabolic mechanisms like breathing (which are usually ignored in whole-brain models) alter the BOLD signals  (Power et al. , 2018) , and the adequate procedure to minimize the corruption of data and obtain a satisfactory signal-to-noise ratio is still under debate  (Parkes, Fulcher, Yücel, & Fornito, 2018). A distinct line of research  (Cabral et al. , 2011; Deco et al. , 2013; Proix et al. , 2016; Sanz-Leon, Knock, Spiegler, & Jirsa, 2015)  focuses on the development of whole-brain models in a more bottom-up fashion (model-driven), combining various datasets and biologically inspired mechanisms, such as oscillators to produce rhythms observed in the brain. Such approaches allow for the study of the influence of specific parameters (as ingredients in the model) in collectively shaping the global network activity.