dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Formatting The data were exported as a dataset from EEGLAB under the. set and. fdt format. The COBIDAS BIDS formatting guidelines were followed  (Pernet et al. , 2020). Data were organized as follows: • One directory per subject; • Two sub-directories for each session; • Inside each session directory one sub-directory for the precise electrode locations (measured via the STRUCTURE app) and one for the EEG data; • 5 (. set) files per session. Each of the epoched and preprocessed task conditions, the resting state as well as the raw file for the resting state; • Each epoch was marked by events to show the condition (difficult, medium, easy, Resting State); • The electrode locations were provided in a. txt file with the xyz coordinates for each electrode.
The selection was made sequentially, by pruning channels, to find the combination that maximises the Riemannian distance between class-conditional covariance matrices. In Figure  3 , the discrepancy of the validation accuracy (evaluated by the competitors using the published datasetsessions 1 and 2) and the test accuracy (evaluated by the  organizers using session 3) is depicted. Validation accuracy from Sharma (2021) was missing in the report and is therefore omitted from the graph. Teams are ordered from left to right following the ranking of the competition. For the first five competitors, the overfitting seems limited as the validation accuracy are close to test accuracy. However, for the three deep learning methods at the end of the ranking, generalization seems to have been an issue as the methods perform very well from session 1 to 2 but at chance level on session 3.
CONCLUSION Overall, in this article, we described our efforts toward moving pBCI technologies beyond proof-of-concept studies in a single session, to more realistic pBCI use across multiple days/sessions, i. e. , in a neuroergonomic approach. In particular, we organized the first pBCI competition that aimed at estimating workload levels (with three levels of workloads) across sessions, with two sessions for training the BCI classifier, and one session for testing it. For that competition, we collected a dedicated EEG data set, that we publicly shared with the community-in order to stimulate research in that direction even beyond the competition. The results of that competition provided several interesting insights. First, it confirmed the effectiveness and superiority of Riemannian geometry classifiers for BCI, whether active or passive, as the winner used Riemannian classifiers, and 3 out of the 4 best scores were obtained using Riemannian geometry.
However, this claim should be tempered by the fact that the number of participating teams was only of 10 and that achieved accuracy is quite low. Besides, we could also notice that a classical approach here submitted by  Bolton et al. (2021) , with a traditional feature extraction and a random forest as classifier has achieved comparable results to those obtained with the other methods of the top-5. The overfitting of 3 out of 4 of the deep learning methods highlights that deep learning is not a silver bullet. It requires some careful design of an adapted architecture and training procedure to the dataset. Here, as in all BCI research so far, the dataset was small. Hence, overfitting due to too large networks was a clear pitfall. A notable exception, the 2nd best results, used a neural network method with a compact (shallow) network and strong prior knowledge about EEG generation.
Yet, it should be noted that the database provided for this competition could have been larger. Indeed, at the time of the competition opening only 15 participants had undergone the 3 acquisition sessions, with an unbalanced database in terms of gender. This was mainly due to participant recruitment and data acquisition constraints in times of covid-19 pandemic, and will need to be further addressed with the release of a more complete and richer database. Nevertheless, such results thereby open interesting perspectives for future research. First, they stress the need for more BCI studies across sessions, both to assess existing pBCIs (usually designed on a single session) but also to design new algorithms able to deal with cross-session variabilities. To do so, transfer learning algorithms (across sessions or users) seem to be promising approaches to explore, that the competitors of that competition have not employed yet.
15-Jun-2021: Official competition opening, publication of the (training) database (2 sessions with labels); • 01-Jul-2021: Release of dataset version 2 with the 3rd session included as a test set (i. e. , without labels); • 31-Jul-2021: Competition closing, deadline for predictions and abstract submission; • 31-Aug-2021: Evaluation by the competition organizing team; • 05-Sep-2021: Submission of presentation materials; • 13-Sep-2021: Announcement of results at the opening reception of the conference. Oral presentation of the winner and poster session presentations of all competing teams.
Acknowledgements. This work was supported by  ORANO-Malvesi  and the  CNRS-INSU Tellus-Syster program. We thank  Sebastien Carretier  and  Odin Marc  for fruitful discussions and  Jens Turowski  for his comments on a preliminary version of this paper. We thank  Laure Guerit  and an anonymous reviewer for their constructive comments, which greatly improved the paper.
In this merger, the effect of the self-gravity of the DM halo could potentially play a much more important role because of the multiple wraps that the Sagittarius dwarf has already made around the MW halo. More generally, it would be important to investigate how and when self-gravity becomes important, depending on the kinematic structure of the host galaxy. We thank the anonymous referee for their helpful suggestions. This work has been supported by the Agence Nationale de la Recherche (ANR project GaDaMa ANR-18-CE31-0006). It has also received funding from the project ANR-19-CE31-0017 and from the European Research Council (ERC grant agreement No. 834148). We used the High Performance Computing facility of the Observatoire astronomique de Strasbourg for our computations, and we thank Mathieu Misslin for running it smoothly.
Methods A diagram summarizing the various methods is included in Supplementary Fig. 2. All R code is available in the Github Repository  113. Fossil and modern data. North American fossil mammal records are well resolved taxonomically and chronologically, making mammals ideal for understanding the effects of late Quaternary anthropogenic and climatic perturbation. Lists of species at individual sites are taken from a dataset containing 67 extant communities  114, 115  , as well as 298 Late Pleistocene and Holocene communities. Extant species occurrences are derived from a variety of literature and web sources and reflect occurrences during the 1970s to 2000s (operationally modern for the purposes of the present study)  114, 115. The Late Pleistocene and Holocene occurrences are derived from the FAUNMAP II database (2003), currently the most comprehensive compilation of North American Quaternary mammal occurrences that includes information about taxonomy, geochronology, geology, and taphonomy 116.
We required each assemblage of species to meet specific criteria for inclusion into the data set. First, assemblages were included only if they comprised a minimum of 20 species, based on sites with the lowest species richness included in ref. 114. Second, assemblages were required to include at least one occurrence of species belonging to each of Rodentia, Artiodactyla, and Carnivora, assuring representation from the clades comprising the majority of non-volant North American mammal diversity. Delineating and dating fossil sites. We defined sites from the FAUNMAP II database as spatially and/or temporally separated occurrences of mammal species meeting the richness and taxonomic criteria listed above. We included fossil sites that have been dated using both radiocarbon and biostratigraphy.
Radiocarbon dates were calibrated using the BchronCalibrate function in the Bchron R package (v. 4. 7. 5) 117 using the IntCal13 calibration curve. We considered two or more stratigraphic layers within the same deposit (sharing the same latitude and longitude), but with calibrated radiocarbon ages that differed by more than 500 years, as separate sites. Layers within that same deposit that were dated to be within 500 years of each other were combined for the purpose of increasing the number of usable sites. Combining dated layers increases time-averaging but is unlikely to influence temporal trends, given that sites were then apportioned to longer time bins (as below). Similarly, two layers with different latitudes and longitudes but similar or identical radiocarbon ages were considered separate sites.
We plotted the sites on maps for each time bin using the sp (v. 1. 4. 5) 118 , raster (v. 3. 4. 10) 119 , mapdata (v. 2. 3. 0) 120 , maps (v. 3. 4. 0) 121 , and ggplot2 (v. 3. 3. 5)  122  R packages (Supplementary Fig. 10 ). Inaccuracies in bulk radiocarbon dates (those dating samples that have been performed prior to the use of Accelerator Mass Spectrometry for radiocarbon dating) are commonly as large as 2,000 years 123. Furthermore, a number of sites in the dataset have no associated radiocarbon dates and are dated based on biostratigraphic methods. Thus, to be conservative and avoid false precision, we use 5,000-year time bins. Fossil sites were therefore divided into six 5,000 year time intervals (larger than the typical bulk radiocarbon dating error) based on their associated median radiocarbon or biostratigraphic dates: 30,000-25,000 cal.
10 ), (3) all sites excluding extinct megafauna, (4) all sites including species >1 kg, and (5) all sites including species > 5 kg, because the preservation potential, fieldwork collection practices, and dispersal abilities of small and large mammals differ  63, 98. Measures of taxonomic similarity. We use the term biotic homogenization to refer specifically to the increasing average compositional similarity of samples (i. e. , a decrease in β diversity) through time  124. All analyses were performed in R version 4. 03 125 using the R packages betapart (v. 1. 5. 4) 126 , vegan (v. 2. 5. 7) 127 , fossil (v. 0. 4. 0) 128 , and glm2 (v. 1. 2. 1) 129. The Forbes similarity index was calculated using code by J. Alroy (http://bio. mq. edu. au/~jalroy/Forbes. R)  60. To test for biotic homogenization, we used several metrics for compositional similarity and turnover, including the average of each of two measures of pairwise site similarity, the Jaccard and corrected Forbes similarity indices  60.
Distance decay of similarity is a measure of the steepness of the slope of a regression of similarity calculated using a pairwise dissimilarity metric, in this case the Jaccard Index, as a function of distance  132. Multivariate dispersion measures differences in compositional dissimilarity among areas, using mean distances from the centroid of a principal coordinates analysis of dissimilarity matrices calculated using metrics such as Jaccard. Multivariate dispersion facilitates comparison among areas or groups 131. It has not, however, been widely applied to study biotic homogenization so is not easily comparable to existing studies. Range size and occupancy. Range sizes and occupancy were calculated from the fossil occurrence data. To delineate the proximate drivers of biotic homogenization, we calculated geographic range size (km 2 ) for each mammal species occurring at five or more sites in each time bin with multiple 95% convex hulls using a resampling approach implemented in the adehabitatHR R package (v.
0. 4. 19) 135. The method involves estimating the geographic range using single-linkage cluster analysis (clusthr function) followed by size estimation at the 95% level (MCHu2hrsize function). We then calculated the mean geographic range size for each time bin. To calculate mean proportional occupancy, we divided North America into 100 km by 100 km grid cells under a Behrmann equal area projection (9196 cells). We then assigned sites to a grid cell based on their proximity to its center using the R function spDistsN1in the sp R package (v. 1. 4. 5) 118 , thus creating a species by grid cell occurrence matrix. We used this method because sites clustered in space are not treated individually and, thus, occupancy is not inflated. For each species occurring in each time bin, we calculated the proportion of grid cells occupied as a proportion of the total number of cells.
Such changes in sampling (e. g. , the number of sites and the distances among them) can affect measures of taxonomic similarity and quantification of other macroecological phenomena. To assess the effects of sampling intensity and area on our analyses of taxonomic similarity, we first used a resampling approach in which we iteratively selected 15 sites per time bin (the number of sites in the poorest sampled time bin) and re-calculated taxonomic similarity 1,000 times for each dataset. We then determined the minimum longitudinal extent (i. e. , the maximum longitudinal distance between sites for the time period with the smallest longitudinal spatial coverage) for the entire dataset (all sites in all time bins) and used the same approach while simultaneously limiting the longitudinal extent of the data for each of the 1,000 iterations.
For the sampling intensity analyses, we calculated mean taxonomic similarity using the Jaccard index. This approach therefore accounts for changes in the number of sites as well as their density (mean distance among sites) during the sampling period. Space-for-time comparison. The mean pairwise taxonomic similarity of mammal assemblages is extensively studied in modern contexts on a variety of spatial scales, particularly in the Western Hemisphere  66, 142. To contextualize the patterns observed in the fossil record and as a means of assessing whether observed changes may be biologically significant, we make a space-for-time comparison  73. We downloaded spatially referenced geographic range data for modern non-volant Western Hemisphere mammals  143  , a dataset that uses the taxonomy of ref.
144  and includes 1,366 species. The Western Hemisphere mammal dataset has been used in other recent studies of community structure 145. We sampled the ranges of extant Western Hemisphere mammals using a Behrmann equal area projection and 100 km by 100 km grid cells using the R packages raster (v. 3. 4. 10) 119 and maptools (v. 1. 1. 1) 146 because smaller grid cell sizes are more subject to false positives (more likely to record a species as present when it is not) 147. We considered grid cells to be occupied by a species if the center of the cell intersected with its geographic range 145. The result was a species by grid cell occurrence matrix, which we used for further analyses. We used the R function spDistsN1 in the sp R package 148 to calculate the great circle distances amongst grid cells.
Using a spatial window of 1000 km, we subsampled grid cells surrounding each focal grid cell using the inverse of the great circle distance as the probability of selection  66. For each subsampled group of grid cells, we calculated taxonomic similarity using the Jaccard Index. We then plotted the values back onto projected maps of the Western Hemisphere under a Behrmann equal-area projection. Paleoclimate data. To test for an association between climate heterogeneity and mean mammal taxonomic similarity, we downloaded North American paleoclimate data from de-biased and downscaled earth system model (ESM) climate simulations based on recent paleoclimate models at 0. 5 degree resolution  68. We downloaded four climate variables from the Dryad digital database (http:// datadryad. org/resource/doi:10.
5061/dryad. 1597g): annual average minimum temperature, annual average maximum temperature, annual actual evapotranspiration (AET), and total annual precipitation. These climate variables are known to correlate with mammal diversity  67, 149. Climate variables of 500-year intervals were a) sampled evenly across the continental USA and southern Canada, where the majority of mammal fossil sites are located and b) averaged into 5,000-year time intervals for comparison to fossil mammal data. Climate and mammal data were projected into a WGS-84 global coordinate system for analysis. We summarized the paleoclimate data using principal components analysis (PCA). We subjected the paleoclimate data for all sites in each time slice to a single PCA and extracted values for the first and second principal components.
Acknowledgements D. F. works on the traditional and unceded territories of the Algonquin-Anishnaabe Nation. This work was supported by the  Smithsonian Institution's National Museum of Natural History Program  grant to the  Evolution of Terrestrial Ecosystems Program  (ETE) and by a  NSF   Research Coordination Network Grant   DEB 1257625  to S. K. L. , A. K. B. , and N. J. G. This is ETE publication #352. DF was supported by a  Discovery Grant  from the  Natural Sciences and Engineering Research Council of Canada ,  Research Activity Grants  from the  Canadian Museum of Nature , and a  Peter Buck Postdoctoral Fellowship  endowed by  Dr. Peter Buck  and awarded by the  Smithsonian's National Museum of Natural History.
Data availability Pleistocene and Holocene mammal data were collected from the published Faunmap 2. 0 database (https://ucmp. berkeley. edu/faunmap/). Data from individual fossil and archeological sites were not collected by the authors of the present study. Faunmap was assembled from published occurrence data (described here: https://ucmp. berkeley. edu/ faunmap/). Modern mammal occurrence data were collected from the following sources,  Brown & Nicoletto (1991)  and  Lyons & Smith (2013). Brown & Nicoletto (1991)  and Lyons & Smith (2013) compiled mammal occurrence data from various sources, including within national parks, as described therein. Software used in data collection included an internet browser (Google Chrome) and Microsoft excel. The data used in this study have been deposited in the Github repository https://doi.
Despite potential for data refinement, there are only a few studies on UWB systems and related technologies that scrutinize data-processing methods, particularly within the unique settings of housings of cage-free laying hens. In the current study, we aimed to contribute to the collective effort of evaluating tracking systems for laying-hen farming, with a focus on the interior of a commercial aviary system. To achieve this aim, two analysis steps were involved. First, two data-processing approaches were applied to filter false registrations, including a simple deterministic approach that filters stays of short durations (SD method) and the interior of a commercial aviary system. To achieve this aim, two analysis steps were involved. First, two data-processing approaches were applied to filter false registrations, including a simple deterministic approach that filters stays of short durations (SD method) and a machine learning approach (ML method) based on a tree-based classifier.
The two processed datasets and the unprocessed dataset were compared against video-observation results (our gold standard). This evaluation was conducted in terms of the number of transitions per individual within the predefined zones and individuals' presence/absence in each zone every second. Secondly, to better evaluate the tracking system, we studied the effect of filtering false registrations based on the ML method over a two-month period on 144 tracked animals under three potential influencing factors: different areas of the aviary, external temperature, and external humidity. We selected these factors because they have already shown to be associated, to some extent, with tracking-system performance and could introduce biases in our own work and that of others using comparable technology if associated with false-registrations.
It is important that during that time, the tag only receives the signal of one marker; otherwise, signals would overlap and might not be valid. Therefore, the markers send at different transmission intervals (varying from 1. 6 to 2. 1 s depending on the zone) a fixed low carrier frequency signal of 0. 125 MHz (LFsignal) that is modulated to allow markers to be differentiated. Within a 10-s interval, a tag could theoretically receive between five and six signals per marker, but this number will often be lower, as every marker has a maximum range of only two to three metres. Every time a tag receives an LF-signal, an algorithm (tag-algorithm) is applied to the registered LF signals received within the past 10 s to evaluate whether the tagged hen has transitioned to a new zone. The tag algorithm reports a new transition when a tag receives the absolute strongest signal value from the same marker twice within 10 s and if the associated zone differs from the last registered zone (pseudo-code in the Supplementary Text S1).
Among CRs, we distinguish two types of registrations:  (1)  registrations that are not associated with a true transition (corrected registrations) and (2) registrations associated with a true transition (transitional registrations). Our goal was to obtain only transitional registrations, and data processing was used towards this objective. The receiving strength of the LF signal (RSS) is used to determine theoretical distance to the antenna loop. At almost every position in any zone, the tag can receive the signal of multiple markers. This signal last for 50 ms. It is important that during that time, the tag only receives the signal of one marker; otherwise, signals would overlap and might not be valid. Therefore, the markers send at different transmission intervals (varying from 1. 6 to 2.
1 s depending on the zone) a fixed low carrier frequency signal of 0. 125 MHz (LF-signal) that is modulated to allow markers to be differentiated. Within a 10-s interval, a tag could theoretically receive between five and six signals per marker, but this number will often be lower, as every marker has a maximum range of only two to three metres. Every time a tag receives an LF-signal, an algorithm (tag-algorithm) is applied to the registered LF signals received within the past 10 s to evaluate whether the tagged hen has transitioned to a new zone. The tag algorithm reports a new transition when a tag receives the absolute strongest signal value from the same marker twice within 10 s and if the associated zone differs from the last registered zone (pseudo-code in the Supplementary Text S1).
Video Observations to Detect False Registrations Two cameras per pen were placed within the indoor portion of each pen in such a manner that each location where an animal could transition between any of the three indoor zones was visible. The view did not cover the interior of the pop hole nor the winter garden and thus did not allow transitions to the winter garden to be filmed. For the generation of the video-based tracking data as a gold standard, video data were collected over the third and fourth weeks for an 11-day period simultaneously with the collection of the tracking data. Single animals Sensors 2022,  22, 659  were visually tracked by two trained observers independent of one another in order to classify each registration as FR or CR. An inter-rater reliability test between the two observers for 137 registrations, including four random hens and four different days, resulted in an inter-rater reliability of perfect agreement, with all recordings classified correctly by both observers.