dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion A suite of tools have been developed to support the publication of documents by the ATLAS collaboration. While the emphasis is on papers published in refereed journals, the new technology also supports internal documents and other public documents such as conference and public notes. The FENCE framework is used as the backbone of the whole setup and is also used to interface the web-based tracking of the status of an analysis with the documentation in GitLab. Extensive use is made of the Continuous Integration tools available in GitLab to ensure that documents can easily be submitted to the arXiv and journals as soon as they have been approved by the collaboration. The software solutions described in this document are now used to accompany the entirety of a physics analysis, from the expressions of interest by research groups to the final journal publication.
Acknowledgements The authors are indebted to the  ATLAS Collaboration  for the support provided to achieve the results described in this paper. We are grateful to ATLAS collaborators who provided invaluable comments and input to the paper and the framework it presents. Special acknowledgements go to  Marzio Nessi  for helping initiate the  Glance  project in ATLAS and for supporting its development, and to Kathy -20 -Pommes for supervising the Glance team at  CERN. Special thanks to  Giordon Stark  and  Zachary Marshall  for thoroughly reviewing this paper.
, 2004; Barker et al. , 2016). To obtain greyscale intensity values and profile quantification, we used the free software package ImageJ (Figure  4C ), allowing greyscale profile quantification. Images were rotated where needed so the crystal boundary was perpendicular to the diffusion direction. This also prevents profiles from becoming anomalously stretched  (Allan et al. , 2013; Kilgour et al. , 2014). To reduce the noise, the greyscale profiles were averaged over the columns of a portion of the image. The averaging areas were relatively consistent between all samples, but were adapted depending on the image and zone observed.
Optimization of Model Parameters Each greyscale profile was fitted to the diffusion profile (Eq. 3) in order to obtain model parameters. The model parameters are determined by either propagating the model forward in time until the solution (model) matches the profile (data), as achieved by  Girona and Costa (2013)  and their software in the analysis of olivine by DIPRA, or adjusting parameters until the model "fits" the data, typically done by eye (e. g. ,  Kilgour et al. , 2014; Fabbro et al. , 2017). However, manual adjustment is prone to bias as there is no metric for judging goodness-of-fit, i. e. , whether the current solution is globally optimal. Due to the strong nonlinearity of Eq. 3 and the interdependence of model parameters, particularly D, small changes to the model may require substantial changes in these parameters.
Such pitfalls are avoided by using well-tested, robust numerical optimization schemes such as non-linear least-squares regression (e. g. ,  Press et al. , 2007). The metric that determines goodness-of-fit is often Eq. 4 is a measure of how closely the model fits the available data points, which is how close the model comes to the data points. Hence, our goal is to minimize its value by adjusting the model parameters using the optimization schemes mentioned above. The optimal solution is obtained when Eq. 4 is minimised. The data were manually selected from the profiles extracted from SEM images and only these selected data were input into the fitting algorithm (Figure  4 ). The suitability and quality of this selection was checked as part of the fitting quality process (see below). We only selected profiles that had clear "plateau" to the left and right of the diffusion zone (Figure  5 , Supplementary Data S1).
Several (3-6) profiles were extracted per sample, which later allowed us to check the consistency of profiles from the same sample and check for quality. As an initial estimate for p, we chose C melt and C mineral as the minimum and maximum greyscale values from the selected data, µ as the midpoint of the data range, and τ 2 × 10 6 s which is considered as a typical timescale value, given a typical diffusion coefficient  (Dohmen et al. , 2016). For the diffusion coefficient, the most important factor to consider is temperature, which has a large effect on Fe-Mg diffusion and therefore timescales. Even a small temperature variation results in a large change in the diffusion timescale; in the case of La Soufrière de Guadeloupe, a temperature variation of ±20 °C results in a broad range. For example, a timescale of 50 days for the 1657 Cal.
We have developed a procedure for automatically and robustly fitting the model parameters using well-known and robust numerical routines contained within the Scipy module in Python. The non-linearity of Eq. 3 requires a good estimate of the solution to be entered as a starting condition for the iterative procedure, which proceeds until either convergence is reached, or the maximum number of iterations is achieved. In the latter case, no valid model solution would be output so we either tried to improve the data selection (e. g. , by extending the range), or rejected this profile from further analysis. In most cases, we added further profiles with longer plateaus on either side of the diffusion zone, from the same sample and iterated this process until the timescales converged.
These equations were solved to find α and ß. We note the latter equation predicts that E [y] y 1/n y i , the arithmetic mean of the data. For the parameter variance, we note the covariance matrix of the model parameters is given by, var(θ) (-Ε[Η(θ)]) -1 (9) where θ [α, β] is the vector of model parameters and E [H (θ)] is the expected value of the Hessian (matrix of 2nd derivatives) of θ. This expression is termed Ficher's information matrix  (Cowan, 1998). The square-roots of the diagonal terms of Eq. 9 are the standard errors on  [α, β]. For a sufficiently large sample size (typically several tens of samples), the standard errors become normally distributed under the central-limit theorem and we may write, for the population timescale standard error, σ τ (σ α /α) 2 + σ β /β 2 1 2 (10) A final step is to check how well the distribution fits the population of timescales, for which we used the Kolmogorov-Smirnov goodness-of-fit test  (Dodge, 2008) , as formulated in Python's Scipy module.
In the majority of crystals, this zoning is reverse (i. e. , Mg-rich rim and a relatively Fe-rich core). Type-2 crystals show one diffuse zone that is 10-80 μm wide, wider than the Type-1 crystals sharp single zones (Figure  7B ). The majority of crystals exhibit reverse zoning. Type-3 crystals have multiple, sharp 2-15 μm wide zones, with the outer zone in the majority of crystals being a reverse zone (Figure  7C ). The other two types identified are more complex crystals, with various resorption textures and similar features and histories. The resorbed areas of these complex crystals have higher enstatite contents than the simple crystals, though across the whole dataset overlap in enstatite contents are observed (Figure  7 ). Type-4 crystals are partially resorbed crystals with thick, magnesium-rich rims and iron-rich cores with undulating core-rim boundaries and patchy zoning present in the rim (Figure  7D ).
ACKNOWLEDGMENTS This work is part of AM's doctoral dissertation at IPGP. The authors thank the  OVSG-IPGP team  for logistical support and help with data collection. We are grateful for help and information from  Laurence and Eric Barret (Vert Intense ). We thank  G. Boudon  for assistance in the field and discussions over the last 3 decades on the geology of  La Soufrière de Guadeloupe  and the reconstruction of its eruptive past. We are grateful to the  Parc National de Guadeloupe  for allowing us to undertake research and obtain geological samples. We are grateful for assistance in preparing samples and for analysis from  Samia Hidalgo ,  Pierre Burckel ,  Stephan Borenborensztajn ,  Nicolas Rividi ,  Michel Fialin , and  Nicole Metrich. Radiocarbon  dates were obtained from  Beta Analytical.
FUNDING The PhD scholarship of AM was co-funded by the "Make our Planet Great Again" initiative of the French Government (Campus France) and the IPGP Ecole Doctorale. We thank IPGP for providing a 5-weeks visiting research position for GK in January 2020 as well as for general funding to the Observatoires Volcanologiques et Sismologiques (OVS), the INSU-CNRS for funding provided by Service National d'Observation en Volcanologie (SNOV), a Tellus-Aleas project to SM (2020, Insights into the dynamics of the active hydrothermal system of La Soufrière de Guadeloupe from past eruptions), and the Ministère pour la Transition Écologique et Solidaire (MTES) for financial support. This work has been supported by the Clervolc (UCA-LMV), the AO-IPGP 2018 project "Depth to surface propagation of fluid-related anomalies at La Soufrière de Guadeloupe volcano (FWI): timing and implications for volcanic unrest" (coord: R.
Moretti), the project "Vers la Plateforme Régionale de Surveillance Tellurique du futur" (PREST) co-funded by INTERREG Caraïbes V for the European Regional Development Fund, and the European Union's Horizon 2020 research and innovation program, under grant agreement No 731070 (EUROVOLC project). This work was supported by the Institut National des Sciences de l'Univers (INSU-CNRS) for radiocarbon dating (AO artemis, LMC14-CEA Saclay), and the CASAVA Project (PI: J-CK) funded by Agence National de la Recherche, 2009-2015 (ANR-09-RISK-02).
This proof of concept study accomplishes both at once, population-scale diagnostics and variants' tracking. This strategy relies on (1) the use of the portable MinION DNA sequencer; (2) a DNA barcoding and a SPIKE gene-centered variant's tracking, increasing the number of candidates per assay; and (3) a real-time diagnostics and variant's tracking monitoring thanks to our software RETIVAD. This strategy represents an optimal solution for addressing the current needs on SARS-CoV-2 progression surveillance, notably due to its affordable implementation, allowing its implantation even in remote places over the world.
1. 618 also known as "triple mutant, ", as suspected to have evolved from B. 1. 617)  14. In all cases, new mutations on the gene SPIKE were observed, further supporting our strategy for targeting this genomic region for variants surveillance. For all these reasons, we are convinced that our proposed strategy, accompanied by the dedicated computational solution RETIVAD, can represent a game-changer approach for tracing variants' progression in the following months. Barcode primer design. Primers used for reverse transcription are composed by (1) a fragment of the Gibson sequenced (redGibs: GAA GAA CCT GTA GAT AAC TCG CTG T), (2) a barcode sequence issued from the ONT PCR Barcoding Expansion 1-96 kit (EXP-PBC096) and (3) the target sequence. For targeting the E-Sarbeco region within the SARS-CoV-2 genome, the reverse primer sequence provided by Corman et al.
5x. Pooled & cleaned material is recovered on 50ul and used for Nanopore sequencing library preparation (Nanopore Ligation Sequencing Kit: SQK-LSK109). DNA libraries were sequenced on Nanopore FLO-MIN106D flowcells (R9) with the MinION Mk1C instrument (72 h sequencing; high-accuracy base calling). 5 ) collect the sequences between the cDNA and PCR-associated barcodes for their alignment towards the SARS-CoV-2 reference genome (Wuhan-Hu-1 genome: NC_045512; BWA aligner following nanopore parameters). Considering that steps (3-5) are performed within the retrieved monomers in step (2), RETIVAD matches cDNA/PCR barcode combinations to aligned outcomes, thus providing a diagnostic outcome for the associated pseudocandidate. Due to the low number of sequences retrieved at intervals of 10 min, and the relative short size of the SARS-CoV-2 genome (~ 29 kb), RETIVAD is able to provide such diagnostic outcome within such 10 min interval.
Hence, at the next round of data collection, RETIVAD reiterates on the aforementioned steps over the newly collected fastq files and appends the outcome to the previous results. The continuous gain on aligned readcounts per region of interest (e. g. E-Sarbeco amplicon sequence, or SPIKE gene; defined as entry parameters) is visualized within scatterplot view (png file) updated during the processing (RETIVAD produces a png file per detected PCR barcode monomers; thus allowing to generate scatterplots displaying as many lines as detected cDNA barcode monomers; Figure  S4 ).
Real-time SARS-CoV-2 diagnostics and variants tracking. In addition to the diagnostic outcome, RETIVAD has been designed for performing real-time variants detection. For it, the racon package (version 1. 4. 20) is used for raw de novo DNA assembly, followed by the alignment with Minimap2 (version 2. 17). Medaka (version 1. 2. 5) is used to create a consensus sequence and variant calls from the aligned data (> 20 aligned reads required as default for variants calling). RETIVAD generates BAM files per cDNA/PCR barcode combination harboring base conversion information and their associated coverage (for visualization with Genome browsers like IGV). Furthermore, summary files per cDNA/PCR barcode combination are also generated, providing the genomic position in which the base conversion has been detected, the identity of the converted bases as well as their associated coverage.
CoV-2 synthetic RNA. The following Synthetic SARS-CoV-2 RNA control samples, delivered at a concentration of 1 million copies per microliter has been used in this study: -Wuhan-Hu-1 (GeneBankID: MN908947. 3); Twist Biosc. ID: Ctrl2. -France/HF2393/2020 (EPI_ISL_418227); Twist Biosc. ID: Ctrl7. -England/205,041,766/2020 (EPI_ISL_710528); Twist Biosc. ID: Ctrl14 (B. 1. 1. 7).
Real-time processing of the fastq files generated by the Mk1C instrument during sequencing is performed with our in-house developed RETIVAD (Real-Time Variants Detector) software. RETIVAD collects each ten minutes the fastq files generated during sequencing from the Mk1C instrument and (1) search for Gibson sequences within the sequenced reads (regex query (https:// pypi. org/ proje ct/ regex) with maximum 4 mismatches accepted by default); (2) split sequenced reads on monomers-delimited by the presence of the identified Gibson sequences; (3) search for barcode sequences introduced during the reverse transcription (cDNA barcodes: BC33-BC48; regex query); (4) search for barcodes introduced during the PCR amplification (PCR barcodes: BC1-BC8; regex query); (.
MethodsSARS- CoV-2 synthetic RNA. The following Synthetic SARS-CoV-2 RNA control samples, delivered at a concentration of 1 million copies per microliter has been used in this study: -Wuhan-Hu-1 (GeneBankID: MN908947. 3); Twist Biosc. ID: Ctrl2. -France/HF2393/2020 (EPI_ISL_418227); Twist Biosc. ID: Ctrl7. -England/205,041,766/2020 (EPI_ISL_710528); Twist Biosc. ID: Ctrl14 (B. 1. 1. 7).
A key goal in neuroscience is to understand brain mechanisms of cognitive functions. An emerging approach is "brain decoding ", which consists of inferring a set of experimental conditions performed by a participant, using pattern classification of brain activity. Few works so far have attempted to train a brain decoding model that would generalize across many different cognitive tasks drawn from multiple cognitive domains. To tackle this problem, we proposed a multidomain brain decoder that automatically learns the spatiotemporal dynamics of brain response within a short time window using a deep learning approach. We evaluated the decoding model on a large population of 1200 participants, under 21 different experimental conditions spanning six different cognitive domains, acquired from the Human Connectome Project task-fMRI database.
The proposed approach leverages our prior knowledge on brain network organization using graphs, and automatically learns the spatiotemporal dynamics of cognitive processes during model training. Our decoding pipeline (as shown in Fig. 1  ) takes a short series of fMRI volumes as input, maps the fMRI signals onto a predefined brain graph, propagates information of brain dynamics among inter-connected brain regions and networks, generates task-specific representations of recorded brain activities, and then predicts the corresponding task states. We tested the decoding pipeline on the Human Connectome Project (HCP) database by evaluating the performance across 1200 participants and 21 different cognitive tasks at the same time. The performance was compared with a classical brain decoding model, which applies multi-class linear support vector machines on trial-averaged brain activity.
fMRI datasets and preprocessing In this project, we are using the block-design task-fMRI dataset from the Human Connectome Project S1200 release ( https://db. humanconnectome. org/data/projects/HCP_1200 ). The minimal preprocessed fMRI data of the CIFTI format were used, which maps individual fMRI time-series onto the standard surface template with 32k vertices per hemisphere. The preprocessing pipelines includes two steps  ( Glasser et al. , 2013 ) : 1) fMRIVolume pipeline generates "minimally preprocessed " 4D time-series that includes gradient unwarping, motion correction, fieldmap-based EPI distortion correction, brain-boundary-based registration of EPI to structural T1-weighted scan, non-linear (FNIRT) registration into MNI152 space, and grand-mean intensity normalization. 2) fMRISurface pipeline projects fMRI data from the cortical gray matter ribbon onto the individual brain surface and then onto template surface meshes, followed by surface-based smoothing using a geodesic Gaussian algorithm.
Further details on fMRI data acquisition, task design and preprocessing can be found in  ( Barch et al. , 2013 ; Glasser et al. , 2013 ). The task fMRI data includes seven cognitive tasks, which are emotion, gambling, language, motor, relational, social, and working memory. In total, there are 23 different experimental conditions. Considering the short event design nature of the gambling trials (1. 5s for button press, 1s for feedback and 1s for ITI), we evaluated the decoding models (see the pipeline section below) with and without the two gambling conditions and found a much lower precision and recall scores for gambling task (average f1-score = 61%) than other cognitive domains (average f1-score > 91%). In the following experiments, we excluded the two gambling conditions and only reported results on the remaining 21 cognitive states.
, 2016 ) , which delineates 180 areas per hemisphere, bounded by sharp changes in cortical architecture, function, connectivity, and topography. The edges between brain areas were estimated by calculating the group averaged resting-state functional connectivity (RSFC) based on minimal preprocessed resting-state fMRI data from  = 1080 HCP subjects  ( Glasser et al. , 2013 ). Additional preprocessing steps were applied before the calculation of RSFC, including regressing out the signals from white matter and csf, and bandpass temporal filtering on frequencies between 0. 01 to 0. 1 HZ. Functional connectivity was first calculated on individual brains using Pearson correlation and then normalized using Fisher z-transform before averaging among the entire group of subjects. After that, a k-nearest-neighbour (k-NN) graph was built by only connecting each brain region to its 8 neighbours with highest connectivity (see Fig.
7  -Supplement 2. The model takes a short series of fMRI data as input, propagates information among inter-connected brain regions and networks, generates a high-order graph representation and finally predicts the corresponding cognitive labels as a multi-class classification problem. An overview of the fMRI decoding model was illustrated in Fig. 1. The entire dataset was split into training (70%), validation (10%), test (20%) sets using a subject-specific split scheme, which ensures that all fMRI data from the same subject was assigned to one of the three sets. Specifically, for each subject and each cognitive domain, individual fMRI time-series on the 64k surface template (including both hemispheres) was first mapped onto the 360 areas of Glasser atlas  ( Glasser et al. , 2016 ) , by averaging the BOLD signals within each parcel.
The time-series of each task trial was extracted and saved into a 2D matrix, by first realigning fMRI signals with experimental designs of event tasks using task onsets and durations and then cutting the time-series into bins of selected time window (see Time window section below). Next, the time-series matrices from all training subjects were collected into a pool of data samples. At each step of model training, a set of data samples (e. g. 128 time-series matrices) was input to the decoding model and the parameter matrix Θ  of each layer were optimized through gradient descent. After all data samples have been trained (i. e. finishing one epoch), the model was then evaluated on the samples from the validation set before the next epoch started. The best model with the highest prediction score on the validation set was saved and then evaluated separately on the test set.
Size of the dataset The Human Connectome Project recruits 1200 healthy participants. It also provides us an opportunity to evaluate the sample size effect, i. e. how many independent subjects were sufficient to reach the stable performance of GCN. To test that, we scanned over the entire task-fMRI dataset and selected the first N complete subjects, who had completed the 7 cognitive tasks with 2 runs. The tested sample size ranges from 14 to 1060 subjects. The time window was fixed as 10s for this test.
Cognitive states can be decoded with high accuracy from 10s of fMRI activity The GCN state annotation model ( Fig. 1  ) was evaluated using the cognitive battery of HCP task-fMRI dataset acquired from 1200 healthy subjects. The entire dataset was split into training (70%), validation (10%) and test (20%) sets at the subject level, such that the model was evaluated on the validation set at the end of each training epoch and the best model over 100 epochs, which achieved the highest accuracy on the validation set, was saved and further evaluated on the independent test set. During model training and evaluation, fMRI response to different cognitive tasks acquired in HCP was collected and input to the decoding model at the same time. In our study we focused on 21 task conditions spanning six cognitive domains, namely: emotion, language, motor, relational, social, and working memory.
This observation is consistent with the well-known segregation of the neural substrates for encoding faces vs places, in the fusiform face area and parahippocampal place area respectively  ( Golarai et al. , 2007 ). We also found some overlap in brain regions between our saliency maps and meta-analysis activation maps from neuroquery ( Fig. 5  -Supplement 1), as well as contrast maps from HCP dataset ( Fig. 5  -Supplement 2). For instance, the inferior frontal gyrus and superior temporal gyrus were identified for the story condition in all three maps, while the inferior frontal sulcus and the adjacent middle frontal gyrus were identified for the mathematics condition, probably counting for the requirement of working memory for a sequence of mathematical operations. Although some consistent patterns of activations were observed for the motor tasks ( Fig.