dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation details for CAIMAN BATCH Each dataset was processed using the same set of parameters, excepting the expected size of neurons (estimated by inspecting the correlation image), the size of patches and expected number of neurons per patch (estimated by inspecting the correlation image). For the dataset N. 01. 01, where optical modulation was induced, the threshold for merging neurons was slightly higher (the stimulation caused clustered synchronous activity). For shorter datasets, rigid motion correction was su cient; for longer datasets K53,J115 we applied non-rigid motion correction. The parameters for the automatic selection of components were optimized using only the rst three datasets and xed for all the remaining les. For all datasets the background neuropil activity was modeled as a rank 2 matrix, and calcium dynamics were modeled as a rst order autoregressive process.
Implementation details for CAIMAN ONLINE Datasets were processed for two epochs with the exception of the longer datasets K53,J115,J123 where only one pass of the data was performed to limit computational cost. For each dataset the online CNN classi er was used to detect new neurons, and ve candidate components were considered for each frame. The online CNN classi er had the same threshold 0. 5 for all datasets, with the exception of the longest datasets J115,J123 where the threshold was set to 0. 75. Setting the threshold to 0. 5 for these datasets led to slightly poorer performance. Large datasets were spatially decimated by a factor of 2 to enhance processing speed, a step that did not lead to changes in detection performance. For all datasets the background neuropil activity was modeled as a rank 2 matrix, and calcium dynamics were modeled as a rst order autoregressive process.
For each dataset, CAIMAN ONLINE was initialized on the rst 200 frames, using the BAREINITIALIZATION on the entire FOV with only 2 neurons, so in practice all the neurons were detected during the online mode. To highlight the truly online processing mode, no post-processing of the results was used, a step that can further enhance the performance of the algorithm. Similarly to batch processing, the expected size of neurons was chosen separately for each dataset after inspecting the correlation image. For the analysis of the whole brain zebra sh dataset, CAIMAN ONLINE was run for 1 epoch with the same parameters as above, with only di erences appearing in the number of neurons during initialization (600 vs 2), and the value of the threshold for the online CNN classi er (0. 75 vs 0. 5). The former decision was motivated by the goal of retrieving with a single pass neurons from a preparation with a denser level of activity over a larger FOV in this short dataset (1885 frames).
Performance quanti cation as a function of SNR To quantify performance as a function of SNR we approximate the ground truth traces by running CAIMAN BATCH on the datasets seeded with the "consensus" binary masks obtained from the manual annotators. After that the average peak-SNR of a trace c with corresponding residual signal r (5) is obtained as SNR(z) = * *1 (p min ), (13) where *1 ( ) denotes the probit function (quantile function for the standard Gaussian distribution), z is the z-scored version of c + r (6) and p min is given by (8). After we compute the SNR for both ground truth and inferred traces the performance algorithm can be quanti ed in multiple ways as a function of a SNR thresholds ✓ SNR : Precision: Precision at level ✓ SNR , can be computed as the fraction of detected components with SNR cm > ✓ SNR that are matched with ground truth components.
(c) Quality assessment in space: The spatial footprint of each real component is correlated with the data averaged over time, after removal of all other activity. (d) Quality assessment in time: A high SNR is typically maintained over the course of a calcium transient. (e) CNN based assessment. Top: A 4-layer CNN based classi er is used to classify the spatial footprint of each component into neurons or not. Bottom: Positive and negative examples for the CNN classi er, during training (left) and evaluation (right) phase. The CNN classi er can accurately classify shapes and generalizes across datasets from di erent brain areas.
False negative/positive pairs are plotted in green along the x-and y-axes respectively, perturbed as a point cloud to illustrate the density. Most false positive/negative predictions occur at low SNR values. Shaded areas represent thresholds above which components are considered for matching (blue for CAIMAN BATCH selected components and red for GT selected components) (d) F 1 score and upper/lower bounds for all datasets as a function of various peak SNR thresholds. Performance increases signi cantly for neurons with high peak SNR traces (see text for de nition of metrics and the bounds).
(b) Break down of processing time for CAIMAN BATCH (left) and CAIMAN ONLINE (right) (excluding motion correction). (Left) Processing with CNMF in patches and re nement takes most of the time for CAIMAN BATCH. Right: Tracking neural activity and new neuron detection can be done in real-time for CAIMAN ONLINE. (c) (Left) Cost of neural activity online tracking for the whole brain zebra sh dataset (maximum time over all planes per frame). Tracking can be done in real-time. (Right) The most expensive part during online processing occurs while updating the spatial footprints, a step that can be distributed or parallelized. Each color corresponds to the update cost for the various di erent planes. (d) Cost analysis of CNMF-E implementation for processing a 6000 frames long 1p dataset. Processing in patches in parallel induces a time/memory tradeo and can lead to speed gains (patch size in legend).
i denote the spatial footprint and temporal trace of component i, and the let A \i , C \i denote the matrices A, C when the component i has been removed. Similarly, let Y i = Y * A \i C \i * B denote the entire dataset when the background and the contribution of all components except i have been removed. If component i is real then Y i and a i c Ò i will look similar during the time intervals when the component i is active. As a rst test CAIMAN nds the rst N p local peaks of c i (e. g. , N p = 5),.
CAIMAN BATCH classi er for post processing classi cationThe purpose of the batch classi er is to classify the components detected by CAIMAN BATCH into neuron somas or other shapes, by examining their spatial footprints. Only three annotated datasets (. 03. 00. t, NF. 04. 00. t, NF. 02. 00) were used to train the batch classi er. The set of estimated footprints from running CAIMAN BATCH initialized with the consensus ground truth was matched to the set of ground truth footprints. Footprints matched to ground truth components were considered positive examples, whereas the remaining components were labeled as negatives. The two sets were enriched using data augmentation (rotations, re ections, contrast manipulation etc. ) through the Keras library (keras. io) and the CNN was trained on 60% of the data, leaving 20% for validation and 20% for testing.
The CNN classi er reached an accuracy of 97% on test data; that also generalized to the rest of the datasets (Fig. 2e) without any parameter change. Online classi er for new component detectionThe purpose of the CAIMAN ONLINE classi er is to detect new components based on their spatial footprints by looking at the mean across time of the residual bu er. To construct the ground truth data for the online classi er, CAIMAN BATCH was run on the rst ve annotated datasets seeded with the masks obtained through the manual annotations. Subsequently the activity of random subsets of found components and the background was removed from contiguous frames of the raw datasets to construct residual bu ers, which were averaged across time. From the resulting images patches were extracted corresponding to positive examples (patches around a neuron that was active during the bu er) and negative examples (patches around other positions within the FOV).
A neuron was considered active if its trace attained an average peak-SNR value of 4 or higher during the bu er interval. Similarly to the batch classi er, the two sets were augmented and split into training, validation and testing sets. The resulting classi er reached a 98% accuracy on the testing set, and also generalized well when applied to di erent datasets. Di erences between the two classi ersAlthough both classi ers examine the spatial footprints of candidate components, their required performance characteristics are di erent which led us to train them separately. The batch classi er examines each component as a post-processing step to determine whether its shape corresponds to a neural cell body. As such, false positive and false negative examples are treated equally and possible mis-classi cations do not directly a ect the traces of the other components.
Acknowledgments We thank  B. Cohen ,  L. Myers ,  N. Roumelioti , and  S. Villani  for providing us with manual annotations. We thank  V. Staneva  and  B. Deverett  for contributing to the early stages of CAIMAN, and  L. Paninski  for numerous useful discussions. We thank  N. Carriero ,  I. Fisk , and  D. Simon  from the  Flatiron Institute (Simons Foundation ) for useful discussions and suggestions to optimize High Performance Computing code. We thank  T. Kawashima  and  M. Ahrens  for sharing the whole brain zebra sh dataset. Last but not least, we thank the active community of CAIMAN users for their great help in terms of code/method contributions, bug reporting, code testing and suggestions that have led to the growth of CAIMAN into a widely used open source package. A partial list of contributors (in the form of GitHub usernames) can be found in https://github.
com/atironinstitute/CaImAn/ graphs/contributors (Python) and https://github. com/atironinstitute/CaImAn-MATLAB/graphs/ contributors (MATLAB ® ). The authors acknowledge support from following funding sources: A. G. , E. A. P. , J. F. , P. G. ( Simons Foundation ). J. G. , S. A. K. , D. W. T. ( NIH NRSA   F32NS077840-01 , 5U01NS090541 ,  1U19NS104648  and  Simons Foundation  SCGB), P. Z. ( NIH NIBIB   R01EB022913 ,  NSF  NeuroNex  DBI-1707398 , Gatsby Foundation), J. T. ( NIH   R01-MH101198 ), F. N. ( MURI, Simons Collaboration on the Global Brain and Pew Foundation ).
These precision and recall metrics are computed on di erent sets of neurons, and therefore strictly speaking one cannot combine them to form an F 1 score. However, they can be bound from above by being evaluated on the set of matched and non-matched components where at least one trace is above the threshold (union of blue and pink zones in Fig. 4c) or below by considering only matched and non-matched components where both ground truth and inferred traces have SNR above the threshold (intersection of blue and pink zones in Fig. 4c). In practice these bounds were very tight for all but one dataset (Fig. 4d). More details can be found in Methods and Materials (Performance quanti cation as a function of SNR).
The Northern and Southern Patagonian Icefields are the largest ice masses in the Southern Hemisphere outside Antarctica, but their ice volume and bed topography are poorly known. Here, we combine airborne gravity data collected in 2012 and 2016, with radar data from the Warm Ice Experiment Sounder and Centro de Estudios Científicos's to map bed elevation and ice thickness in great detail. We perform a 3-D inversion of the gravity data constrained by radar-derived thickness and fjord bathymetry to infer bed elevation at 500-m spacing, with a precision of about 60 m. We detect deep glacial valleys with ice thickness exceeding 1,400 m and sectors below sea level on the western branch of Glaciar Pio XI, Occidental, between San Rafael and Colonia, and near Fitz Roy. We calculate an ice volume of 4,756 ± 923 km 3 for Northern Patagonia Icefield and Southern Patagonia Icefield, or 40 times the volume of glaciers in the European Alps.
We used a Cessna-206 aircraft and split the survey into seven flights between 22 July to 1 August 2016 from Villa O'Higgins, Chile, at the foothills of SPI (Figure  S1 ). AIRGrav has draping capabilities, which enables data collection with changes in altitude, direction, or shallow-bank turns. We optimized the survey by avoiding high mountain ranges with steep slopes, where ice thickness is low, to minimize changes in altitude, maintain the safety of the survey, and maximize data quality. The traverse line spacing was 2 km, with a target speed of 80 knots and a ground clearance of 100 m. Control line were placed so that intersections area could be maximize and terrain clearance could be maintained. The gravity anomalies were recorded at 128 Hz with a nominal noise level of 0. 2 mGal. The data were corrected for the Eotvos, normal, free-air, and static effects.
5-MHz center frequency, with 1-μs duration pulses (Figure  S2 ). A conventional GPS receiver was operated at 10 Hz with a vertical precision of 10 m. We recorded radar data between 150 and 300 m above the ice surface. Low-altitude flying increases spatial resolution and clips radar returns from the surface, which reduces data clutter  (Mouginot & Rignot, 2015). We deduced ice thickness from the difference between a reference surface elevation from a TanDEM-X digital elevation model (DEM) from year 2015 and bed elevation measured from the WISE radar echograms. We employed a constant dielectric of 3. 15 for ice and apply no firn correction. Even with the low-altitude flight, the separation of off-nadir and bed returns was ambiguous. To assist in the identification of bed versus surface returns, we used a radar simulation where the surface is decomposed into facets (larger than the radar wavelength) and each facet is treated as a quasi-specular reflector  (Nouvel et al.
In addition, we used radar data from other sources  (Raymond et al. , 2005; Rivera & Cassassa, 2002; Zamora & Uribe, 2017 ; Figure  S3 ). Error were estimated by analyzing crossing profiles and evaluated to be 6% of the thickness. Bathymetry. The inversion of gravity data over open water is constrained by lake and fjord bathymetry data where available (Figures  1  and S3 ). We use fjord bathymetry for Glaciar Jorge Montt by  Rivera et al. (2012)  collected with a sonar system and a Datasonic Bubble Pulser subbottom profiler. For Lake O'Higgins,  Casassa et al. (2007)  collected bathymetry data using a parametrical echo sounder (Innomar) in 2004 and 2005. We integrate bathymetry from  Koppes et al. (2010)  for Laguna San Rafael collected on a zodiac using a Datasonics Bubble Pulser combined with a Lowrance 18-C depth sounder  (Koppes et al.
, 2010). Finally, we use data from Glaciar Tyndall  (Raymond et al. , 2005) , Upsala (A. Rivera, personal communication, 2018) , HPS-12, Bernardo, Tempano, and Pio XI  (Dowdeswell & Vasquez, 2013). All bathymetry data have a vertical resolution that is better than 10 m  (Casassa et al. , 2007; Dowdeswell & Vasquez, 2013; Koppes et al. , 2010). Gravity inversion. We form a 3-D model of NPI and SPI using the Geosoft GMSYS-3D software package, which implements Parker's (1973) standard method. The 3-D model includes three layers with densities of 0. 917 g/cm 3 for ice, 1. 028 g/cm 3 for water, and 2. 67 g/cm 3 for bedrock. For surface reference, we use the 2012 laser altimeter data along the 2012 surveys and a combination of TanDEM-X from year 2015 (10-m spacing) and year 2000 SRTM (30-m spacing) for the 2016/2017 surveys.
In total, 267 km 2 of glacier area, or 2. 2% of NPI and SPI, is grounded below sea level. All of these basins, however, are protected by higher ground along the periphery, so we find no direct pathway for the rapid retreat of glacier fronts along marine-based channels toward the interior of the icefield. At present, the most vulnerable glacier is Jorge Montt, grounded below sea level for 16 km upstream of the current ice front, which favors retreat due to warm-water-induced melt  (Millan et al. , 2018; Wood et al. , 2018). More data are needed in the lower reach of Occidental and Pio XI to draw conclusions on their vulnerability to warm water intrusions. Areas exposed to high surface melt production with overdeepenings could host meltwater, which would influence ice dynamics, or create natural hazards.
In the mean time, the current bed topography and ice thickness data sets, that includes bathymetry in lakes and fjords, should make it possible, for the first time, to conduct detailed quantitative studies of the evolution of the major glaciers of Patagonia in a warming climate (e. g. ,  Collao-Barrios et al. , 2018)  Propulsion Laboratory, California, Institute of Technology under grant NNX17AI02G with NASA Cryosphere Science, and at CECs under a grant from the Chilean National Science Fundation FONDECYT 1171832 and basal fund programs. We thank the helicopter pilots of the 2012 gravity survey conducted from Cochrane and Laguna San Rafael, the pilots of the 2014 radar and 2016 gravity survey conducted from Villa O'Higgins, the inhabitants of Villa O'Higgins for their hospitality, and the technicians and personal from Sanders Geophysics, Ltd.
In the spring of 2016, France saw a major social movement, with strikes and demonstrations, and a new form of protest, the Nuit Debout. Following the Occupy Wall Street and Los Indignados models, open air assemblies started in Paris on March 31 and then spread throughout France and abroad. The dataset presented here provides the exhaustive list of Nuit Debout gatherings that took place in France in April 2016 and an estimation of their audience. The data was gathered by a small multidisciplinary team (geographers, historian and sociologist) who consulted three main sources: a wiki created by the Nuit Debout movement, Facebook pages and groups created by local assemblies, and the regional press. Combining these sources made it possible to identify 1300 assemblies that took place in 215 different locations.
This paper, and the dataset it presents, deals with the geographical diffusion of the Nuit Debout movement in France. The first gathering occurred in Paris, on Thursday, March 31, 2016, but it did not take long for others to appear in a few major cities and then throughout France. Some even took place abroad, but their number remained limited and we restrict ourselves henceforth to the French ones. Most media and academics focused on the Parisian Nuit Debout protests ("Nuit debout", 2016;  Smaoui, 2018; Gérard & Bertina, 2018) , skirting the question of whether or not the movement was able to extend beyond Paris. Obviously, this issue is crucial, given that the French capital city is unique in terms of the sociological features of its population in comparison with the rest of France: over-representation of managers, of the cultural sector, rising gentrification in formerly lower-income areas, etc.
In our opinion, this geographical restriction does not make it possible to correctly assess the order of magnitude and gain a full picture of this movement. However, it is hard to blame these journalists, French intellectuals and "experts" for their Paris-centric perspective, since until now, there has not been any systematic or rigorous collection of data to document what was happening in other locations. Such fluid events are typically difficult to document and to qualify, in terms of order of magnitude, regarding the number of participants, their characteristics, its channels of diffusion, etc. By carefully documenting the Nuit Debout movement beyond the Parisian Place de la République, the idea of our dataset is to give further insights on what the Nuit Debout movement really was, as this form of protest  (Tilly, 1986 (Tilly, , 2003) )  has remained rare in the previous decades in France.
org/) proposed a solution to record crowd size events in real-time with a smartphone application. None of these methods was implementable here, because it was impossible to attend all meetings and there is no systematic video or internet account for each of them. We have documented all Nuit Debout gatherings that occurred in France in April 2016. This month-long time delimitation is due to various reasons. First, most instances of the Nuit Debout emerged in this month. Some appeared later, in May or June, but their number remains very limited (15 out of 253). Second, they were most active during this month and most of them declined afterwards. Third, we were interested more in the diffusion of the movement, rather than in its duration. Our data does not allow us to identify any straightforward diffusion path, but does show that the Nuit Debout movement did indeed extend widely beyond Paris.
It was very idiosyncratic and very diverse across time and space. The first thing that we shall do is to present how we collected information on all Nuit Debout gatherings, by filling the dataset with the dates of the gatherings and writing a harmonized note for each of them. 1 We relied on information found on the internet, drawn from three main sources. First, the wiki 4 established by the movement itself (https://wiki. nuitdebout. fr/wiki/Villes/),2 which counts 248 municipalities where it is supposed that at least one Nuit Debout protest took place. We consider it as a sort of census of the movement but we were also able to find some Nuit Debout occurrences that were not present on this wiki. Secondly, we looked at websites and Facebook pages created by activists. Lastly, we used local newspapers, as this movement created a large amount of interest from the media at both regional and national levels.
In some cases, we used our fieldnotes, as some of us made direct and participant observations of this movement. Obviously, none of these sources is 100% reliable, but when considered together they provide a good level of accuracy on the Nuit Debout movement and the media attention that it created. The main difficulty is that the relevant aspects to understand such a social movement are not known a priori. These have to be explored along the way, and it takes time to gain a better understanding of this movement. All this documentation led to various results regarding the diffusion of the movement through space and time, which we present briefly in a second part. The dataset provides an exhaustive list of Nuit Debout assemblies that took place in France with the following information: city name, alliance and source used.
Methods of Data Gathering and Analysis -Nuit Debout deposited at Zenodo -doi:10. 5281/zenodo. 3056000 -Nuit Debout -The script to reproduce this analysis -url: https://www. nakala. fr/ data/11280/292348ab -Temporal coverage: 31 March -30 April 2016 Collaborative work was required in order to gather information, as three digital sources were explored. Two engineers, one geographer and one sociologist worked together for several months; all of them participated in the Nuit Debout movement, three in Paris (Place de la République and several other Nuit Debout locations in Paris and its suburbs), and one in Melun (Île-de-France).
2. 2. Second Source: Facebook, Twitter and Websites We systematically explored major social networks (Facebook, Twitter) and websites or blogs (often indicated in the wiki). It is worth noting that, by the time our data collection process started, one year after the beginning of the Nuit Debout movement in March 2016 (summer 2017), websites and blogs have disappeared (8 out of 17) more often than Facebook page and Twitter accounts. Scrolling down Facebook pages is tedious because of the repetition of slogans and talkative exchanges, but it is also very useful to give 1) an idea of the beginning of the assembly thanks to the date of the first post 2) a quantitative estimation of the amount of (online) support, and 3) a collection of meaningful comments on the events that took place. It also helped to identify media coverage posted on Facebook walls.