dataesr/scientific-paragraphs-categorization huggingface dataset:
Visualization and reconstruction of complex MYCN and RTK amplicons JaBbA  90  was used to generate cancer genome graphs using SvABA SV, GATK CNV and absolute purity and ploidy as inputs. The cancer genome graphs combined with ChIP-seq tracks were visualized in gGnome (https://github. com/mskilab/gGnome) / gTrack (https://github. com/mskilab/gTrack), which were also used to calculate distances between loci in the cancer genome. To infer the structure of possible extrachromosomal amplicons the genome graphs were subsetted to only segments with CN > 20 and all circular paths in the genome graphs were reconstructed with the gGnome walks() function. To determine which genomic loci were recurrently incorporated into the amplicons, the distribution of amplicons around the oncogene was evaluated using methods analogous to previously published approaches  20, 21.
First, the TADs adjacent to the amplified oncogene were divided into 10kbp windows. The average copy number per 10kbp window was calculated in all tumors with an amplicon of CN >5 anywhere in the TAD of the oncogene (using the germinal zone TAD boundaries from GSE77565  86  ). Likewise, among tumors with an amplification of CN>5 anywhere within the TAD of the oncogene, the fraction of tumors with an amplification in each 10kpb window was determined. If the fitness advantage provided by the amplicons resulted only from their inclusion of the oncogene, one would expect a symmetric normal distribution with the peak at the location of the coding sequence of the oncogene for both mean amplification level and fraction of tumors with amplifications in the 10kbp windows around the oncogene. The location of likely enhancer elements, necessary to drive expression of the amplified oncogene, was inferred from the direction of the skew of the observed distribution compared to the expected symmetric normal distribution.
Signature integration and definition of signature clusters with similar variant generating processes To better understand the information contained in each of the five SV and 14 SNV signatures, consensus clustering was applied to the tumor x signature proportion matrix, comprising the 19 values representing the proportion of each of the SV and SNV signatures out of all SV and SNV signatures in each tumor. The proportions for each signature were median-centered across all tumors before consensus clustering with the ConsensusClusterPlus R package  104  using the parameters: reps=1000, pItem=0. 9, pFeature=0. 9, clusterAlg="hc",distance="spearman". The resulting three most stable and informative clusters were named "Complex-SV", "Intermediate" and "Simple-SV", after the signature with the highest enrichment in the cluster.
Variant timing analysis Previously published approaches  61, 108  were leveraged to evaluate if a potential driver variant occurs early or late in tumor development. The palimpsest 109 R package was applied to determine single patient timing of SNVs, as previously described  108. In brief, SNVs were classified into clonal vs. subclonal based on their cancer cell fraction (CCF, calculated from the variant allele fraction adjusted by local copy number and purity/ploidy for the respective sample). SNVs overlapping with SCNA could further be timed into early or late depending on if they happened before or after the SCNA  61. MutationTimeR 61 was used to determine the timing of SCNAs in individual patients, including clone clusters as input. Mobster 110 was used to define the clone clusters based on the distribution of the absolute  83  CCFs.
Extended Data Figure  5  Associations between SV-defined groups. (A) Jaccard distances between tumor pairs (vertical axis), calculated from the combination of variants in each tumor, across subgroups of H3 K27M DMGs. Tumor groups were determined by whether mutations were present in H3. 1 or H3. 3 and whether the combined Complex-SV signatures contributed more or less than 20% to all SV signature activity. Tumors were paired within or between these groups, as indicated on the horizontal axis. All differences were significant with q < 0. 005 (FDR-corrected Wilcoxon test) unless indicated otherwise. (B) Volcano plot indicating the significance (vertical axis; FDR-corrected Fisher's exact tests) of associations in H3. 3 K27M Simple-SV relative to H3. 3 K27M Complex-SV DMGs, for all genetic variants observed in at least 10% of tumors in one subgroup.
Acknowledgements We thank and acknowledge the children and their families who made this research possible through their participation in pHGG clinical trials and tissue banking protocols. We also thank the many clinicians and researchers who led the DIPG BATS trial. We would also like to thank the members of the  Ligon, Beroukhim and Bandopadhayay Laboratories , in addition to  Ruolin Liu ,  Justin Rhoades ,  Matthew Leventhal ,  Justin Hwang ,  Jonathan So ,  David Mayhew ,  Jonathan Rennhack ,  Mehdi Touat ,  Andrew Cherniack ,  Beatrice Rondelli  and  Sophie Polo  for their support and helpful discussions. The authors would like to thank and acknowledge the following funding sources: The  German Research Foundation (DFG)  (research fellowship supporting FD),  Fund for Innovative Cancer Informatics (RB) , the  Gray Matters Brain Cancer Foundation  (RB), The  Bridge  Project of  MIT and Dana-Farber/Harvard Cancer Center (RB, KLL) ,  The Sontag Foundation (RB) ,  The V Foundation for Cancer Research (PB) ,  Michael Mosier Defeat DIPG Foundation (PB) , The  ChadTough Foundation  (PB), The  St.
Improved chain of tasks The system became centralized in 2019 to improve efficiency. The execution of DOWNFLOW and FLOWGO was combined in DOWNFLOWGO as part of the upgrade  [Harris et al. 2019; Peltier et al. 2021] , thereby merging Actions 1 and 2. This was possible through PyFLOWGO, an integrated python code that allows the actions to be executed by a single operator at a single location on any operating system  [Chevrel et al. 2018]. Action 3 was also streamlined because the original process required a new simulation to generate the flow runout projection with each change in the TADR, which was time-consuming and inefficient. Simulations are now run for a range of likely effusion rates (typically between 5 and 50 m 3 s -1 at increments of 5 m 3 s -1 ). In essence, once the vent coordinates are entered in the code line, a raster of the most probable area to be covered, the path of steepest descent, and the runout locations (coordinates and elevation) along this path are computed.
ACKNOWLEDGEMENTS We warmly acknowledge:  J-L. Froger  and  A. Hrysiewicz  from  OI 2  (now at ISDeform) platform that produced lava flow outlines from InSAR data,  F. Massimetti  for providing the Sentinel 2 images for the December 2020 eruption of  Piton de la Fournaise  and  M. Ramsey  for providing, in most eruptions at  Piton de la Fournaise ,  ASTER IR  and visible images. HOTVOLC data were supported by  French CNRS-INSU  through the  National Observation Service in Volcanolgy (SNOV) , and  French Space Agency the Centre National d'Études Spatiales (CNES). We acknowledge  H. Dietterich  and two other anonymous reviewers as well as the editor,  L. Gallant , for their constructive comments and suggestions that improved the manuscript. This research was fully funded by the  Agence National de la Recherche  through the project  LAVA  (Program:  DS0902 2016 ; Project:  ANR-16 CE39-0009 ).
DATA AVAILABILITY All maps presented here are available upon request to the corresponding author or to the OVPF. PyFLOWGO is available at https://github. com/pyflowgo/pyflowgo, archived via Zenodo (doi: 10. 5281/zenodo. 7182331). OVPF reports (ISSN 2610-5101) are available at http://www. ipgp. fr/fr/.
This molecular diffusion coefficient is calculated as function of temperature and salinity using compiled relation of  Boudreau (1997) , implemented in the R package Marelac  (Soetaert and Petzoldt, 2020). As a simplifying assumption, material accumulation has no effect on porosity. We further assumed the porosity profile decreased with depth but invariant with time. Although, this assumption is a restrictive as the site of flood deposition can undergo variation in grain size which might affect their porosity  (Cathalot et al. , 2010) , we proceed noting that the fixed parameters which define the porosity curve can be changed when necessary. Thus, using optimized parameters fitted with data in the proximal sites of Rhône prodelta  (Ait Ballagh et al. , 2021) , porosity (φ (z)) in Eqs. (  10 )-(  8 ) is prescribed as an exponential decay, φ (z) = φ ∞ + (φ 0 -φ ∞ ) e -(z-z swi ) δ , (12) where φ 0 and φ ∞ is the porosity surface and at deeper layer, respectively, while Z swi is the depth of the SWI (sedimentwater interface), and δ (cm) is the porosity exponential decay coefficient with depth.
Model vertical grid The model is vertically resolved with grid divided into 100 layers (N), of thickness ( z) increasing geometrically from 0. 01 cm at the sediment-water interface to 6 cm at the lower boundary. The result is a 100 cm model domain comprising of a full grid with non-uniform spacing and maximum resolution near the SWI. Depth units are in centimetres. This choice of modelled depth allows for complete carbon degradation. This modelled grid is generated by the grid generation routine of the ReacTran R package  (Soetaert and Meysman, 2012) , which implements many grid types used in early diagenesis modelling. During the time instance of the event specification, the added grid of new layers (N pert ≈ Z pert ) and the current grid (N cur ≈ N) is rescaled to the model's common grid of N layer by linear interpolation (see Sect.
In our case, we used only the deposit mode. The event algorithm is used at the stated time point to estimate the model porewater and solid properties driven by the instantaneous change in the boundary condition. The concentrations are successively updated by their diagenetic contributions during this time step. Afterward, this modified profile is integrated forward in time. The model is written in Fortran for speed and integrated using the R programming language (R Core Team, 2021) via the "method of lines" approach  (Boudreau, 1996). In addition, the model made use of the event-handling capabilities' specific numerical solvers written in the R de-Solve package  (Soetaert et al. , 2010b). The R programming language is used in the preprocessing routine for model grid generation  (Soetaert and Meysman, 2012) , porewater chemistry parameter  (Soetaert et al.
Code availability. As a whole, the model is bundled as an R package for easy accessibility and can be downloaded from R-forge (https://r-forge. r-project. org/R/?group_id=2422, last access: 2 August 2022). The most recent version of the model, and its evolution, can be found on the project development page (https://r-forge. r-project. org/projects/diagenesis/, last access: 2 August 2022) with subsequent expected release in CRAN. Full R vignette illustrating the capabilities of the model can be found on the model doc folder. The version used to produce the results used in this paper is archived on Zenodo (https://doi. org/10. 5281/zenodo. 6369288; Nmor and Soetaert, 2022) , along with the input data and scripts to recreate the simulation presented in this paper. FESDIA users should cite both this publication and the relevant Zenodo reference.
Study Site The Caboolture River Catchment is a sub-tropical catchment in southeast Queensland, Australia, approximately 40 km north of Brisbane (Figure  1 ). It has a catchment area of 383 km 2 with elevations ranging from 0 to 550 m above sea level. The complex stream network is an amalgamation of Wararba Creek, Lagoon Creek, King Johns Creek, Gregor's Creek, Sheepstation Creek, and Gympie Creek, which discharge to the main Caboolture River and downstream Caboolture Estuary Mouth, with final discharge to Deception Bay, a small embayment within the larger Moreton Bay (Figure  1 ). Rainfall data measured at 10 Bureau of Meteorology (BoM) stations and temperature data at the BoM Beerburrum Meteorological Station, 20 km north-west of Caboolture (Figure  1 ) was utilized. Gridded rainfall was calculated using an inverse distance weighting method that captured the spatial variation of rainfall within the area, which was higher in the mountain areas compared to the lowland areas.
This resulted in the isotope-enabled export model producing values in better agreement (closer to 1:1 line) with the measured values than the no-isotope model, as shown in the top panel of Figure  8. A sensitivity analysis was conducted to explore the effect of the concentration data distribution on model results. This was done by removing the outliers and extending the data set from 4 to 6 sampling events (Figure  S8  in Supporting Information S1). The original data of CR5, C11, and CR13 were selected and bound to the 20th and 80th percentiles and, for the next three events, datasets were modified by increasing the concentrations by 3% and reducing the flows by 3%. Isotope abundances were unaltered. The results shown in the lower panels of Figure  8  indicate an improvement in the metrics of determination (r 2 ), NSE, and RSR for NO 3 -N, suggesting the data distribution does influence the model results.
Data Observational data used to train and test the CNN are taken from the ECMWF Reanalysis version 5 (ERA5) data set between 1979 and 2019  (Hersbach, et al. , 2020). We compare the CNN to models from the S2S database (F. Vitart et al. , 2017). We select reforecast data from four representative models, chosen to span the range of performances of models in the S2S database. In particular, we include the European Centre for Medium-Range Weather Forecasts (ECMWF) model, which is known to produce the most skilful MJO forecasts  (Lim et al. , 2018). The remaining models chosen had the largest reforecast ensemble size, enabling probabilistic forecast skill to be assessed. Details are presented in Table  S1  and Text S1 of Supporting Information S1.
Network Performance Figure  1  compares the network's performance to models from the S2S database (see Text S5 in Supporting Information S1 for definitions of all metrics). Figures  1a-1c  show the deterministic skill of the CNN mean forecasts in terms of the root mean square error (RMSE), Amplitude Error, and Phase Error respectively. In terms of RMSE, the CNN is competitive with models from the S2S database, though has larger errors than ECMWF. Similarly to the dynamical models, the CNN forecasts suffer from an increasing amplitude error with time, indicating a decay in MJO strength over the duration of the forecast. It is known that dynamical models simulate slower MJO propagation speeds than observed, resulting in a negative phase error  (Lim et al. , 2018). Here the CNN outperforms the dynamical models, accurately capturing the MJO propagation speed.
A fourth metric, the bivariate correlation, is shown in Figure  S4  of Supporting Information S1: the CNN performance is poorer than ECMWF, but similar to CNRM and BOM. 2016 )) compares forecast and observed cumulative distribution functions. The CNN is competitive with forecasts from the S2S database, outperforming three of the four dynamical models considered. Despite being widely used, the CRPS can give unintuitive rankings (e. g. ,  Bolin & Wallin, 2019) , as it penalizes errors in the forecast mean more than poor calibration of spread  (Christensen et al. , 2015). An alternative score is the "Ignorance" or log-score  (Roulston & Smith, 2002)  (Panel e). This score is local, derived from information theory, and easily generalizes to multivariate predictions  (Bjerregård et al. , 2021; Roulston & Smith, 2002).
Discussion and Conclusions We presented a CNN which produces probabilistic forecasts of the MJO in terms of means and variances of the bivariate RMM index. The skill of the CNN is competitive with models from the S2S database. Moreover, the CNN outperforms all S2S models for one key forecast property: it can rank start dates according to the forecast uncertainty associated with the initial conditions. In other words, the CNN forecast spread is a dynamic indicator of the uncertainty in the MJO forecast on a given day. Since the CNN exhibits state-dependent reliability, we identify "certain" CNN forecasts with predictable states of the MJO and use the CNN forecasts to probe associated sources of predictability. We do this by considering composites of initial conditions which the CNN indicated led to "certain" and "uncertain" 10-day forecasts.
Figures 1d-1f assess the probabilistic skill of the CNN. The continuous ranked probability score (CRPS: Marshall et al. (2016)) compares forecast and observed cumulative distribution functions. The CNN is competitive with forecasts from the S2S database, outperforming three of the four dynamical models considered. Despite being widely used, the CRPS can give unintuitive rankings (e. g. , Bolin & Wallin, 2019) , as it penalizes errors in the forecast mean more than poor calibration of spread (Christensen et al. , 2015). An alternative score is the "Ignorance" or log-score (Roulston & Smith, 2002)  (Panel e). This score is local, derived from information theory, and easily generalizes to multivariate predictions (Bjerregård et al. , 2021; Roulston & Smith, 2002). It is also consistent with the loss function used to train the network.
Data Availability Statement Data related to this paper can be downloaded from the ERA5 Copernicus database (https://cds. climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels, https://cds. climate. copernicus. eu/cdsapp#!/dataset/ reanalysis-era5-single-levels) and the S2S project archive (http://s2sprediction. net) via the European Centre for Medium-Range Weather Forecasts portal (https://apps. ecmwf. int/datasets/data/s2s-reforecasts-instantaneous-accum-ecmf/; https://apps. ecmwf. int/datasets/data/s2s-reforecasts-instantaneous-accum-rums/; https://apps. ecmwf. int/datasets/data/s2s-reforecasts-instantaneous-accum-lfpw/; https://apps. ecmwf. int/datasets/data/s2s-reforecasts-instantaneous-accum-ammc/). The convolutional neural network (CNN) forecasts produced for this paper can be downloaded from https://doi.
org/10. 5281/zenodo. 5175837. The RMM indices were computed using the CLIVAR diagnostics package (https://www. ncl. ucar. edu/Applications/mjoclivar. shtml). PyTorch (https:// pytorch. org) and DropBlock (https://github. com/miguelvr/dropblock) libraries were implemented to build and train the CNN model. PatternNet code was adapted from https://github. com/TNTLFreiburg/pytorch_patternnet. The codes used in the current analysis are available at https://github. com/antoine-delaunay/DeepLearningMJO/.
Figures Figures 1d-1f assess the probabilistic skill of the CNN. The continuous ranked probability score (CRPS: Marshall et al. (2016)) compares forecast and observed cumulative distribution functions. The CNN is competitive with forecasts from the S2S database, outperforming three of the four dynamical models considered. Despite being widely used, the CRPS can give unintuitive rankings (e. g. , Bolin & Wallin, 2019) , as it penalizes errors in the forecast mean more than poor calibration of spread (Christensen et al. , 2015). An alternative score is the "Ignorance" or log-score (Roulston & Smith, 2002)  (Panel e). This score is local, derived from information theory, and easily generalizes to multivariate predictions (Bjerregård et al. , 2021; Roulston & Smith, 2002). It is also consistent with the loss function used to train the network.
Graph kernels are powerful tools to bridge the gap between machine learning and data encoded as graphs. Most graph kernels are based on the decomposition of graphs into a set of patterns. The similarity between two graphs is then deduced from the similarity between corresponding patterns. Kernels based on linear patterns constitute a good trade-off between accuracy performance and computational complexity. In this work, we propose a thorough investigation and comparison of graph kernels based on different linear patterns, namely walks and paths. First, all these kernels are explored in detail, including their mathematical foundations, structures of patterns and computational complexity. Then, experiments are performed on various benchmark datasets exhibiting different types of graphs, including labeled and unlabeled graphs, graphs with different numbers of vertices, graphs with different average vertex degrees, cyclic and acyclic graphs.
Introduction In recent years, machine learning has becoming a considerably effective and efficient tool in multiple real-world tasks, such as regression and classification problems. Machine learning algorithms have been defined on vector spaces, allowing to take advantage of the easiness in linear algebra operations. While many data can be represented by vectors, such as images by unfolding them into vectors, it turns out that many data types are too complex to be vectorized. Graphs are able to model a wide range of real-world data, by encoding elements as well as the relationship between them. Considering for example a molecule represented by a graph: its vertices stand for the molecule atoms, while its edges model the relationship between them, such as bond types between two atoms. Due to these properties, graph representation has broad applications in wide domains, such as 2D and 3D image analysis, document processing, biometric identification, image database, video analysis, biological and biomedical applications, bioinformatics, chemoinformatics, web data mining, etc.
Among them, the generalized random walk kernel is split into four different kernels due to the computing methods they use. Considering the theoretical aspects, we examine their mathematical expressions with connections between them, and their computational complexities, as well as the strengths and weaknesses of each kernel. In the exhaustive experimental analysis conducted in this paper, each kernel is applied on various datasets exhibiting different types of graphs, and a thorough performance analysis is made considering both accuracy and computational time. This rigorous examination allows to provide suggestions to choose kernels according to the type of graph data at hand. Finally, all the implementations are publicly available as an opensource Python library on GitHub. In this library, every kernel is able to tackle different types of graphs, and several computation methods are provided for kernels.
In other words, for all (x i , x j ) ∈ X 2 , we have k(x i , x j ) = Φ H (x i ), Φ H (x j ) H , (4) where Φ H : X → H is an embedding function. The positive semi-definiteness of the kernel is a sufficient condition to the existence of this function. For the sake of conciseness, positive semi-definite kernels are simply denoted as kernels in this paper. Kernel based methods in machine learning take advantage of Mercer's theorem, in order to transform conventional linear models into non-linear ones, by replacing classical inner products between data with a non-linear kernel. Let X = {x 1 , x 2 ,. , x N } be the finite dataset of N samples available for training the machine learning method, with associated data labels {y 1 , y 2 ,. , y N } where y i ∈ {-1, +1} for binary classification and y i ∈ R for regression (extensions to multiclass classification and vector output is straightforward  (Honeine et al.
Step 2 is to compare walks one by one in the two graphs under study, which has a computational complexity of O(n 2 d 2h ). For a dataset of N graphs, the computation of the Gram matrix requires O(N 2 n 2 d 2h ) operations. Therefore, the computational complexity is exponential in the length of walks, which is impractical for large-scale graphs. To overcome this difficulty, other computational methods have been proposed as given next. In  (Gärtner et al. , 2003) , the fully-labeled direct product graph is employed to reduce the computational complexity, within two types of kernels: kernels based on label pairs and kernels based on contiguous label sequences; the latter one can deal with labels on vertices and/or edges. In this case, the direct product is defined as following: 1  2  3   1'   2'  3  ' 4' 11' 22' 33'  34' = = (V 1 , E 1 ) and G 2 = (V 2 , E 2 ).