dataesr/scientific-paragraphs-categorization huggingface dataset:
This mirrors the split into training-validation-test data of the LHCO data. In both panels of Fig. 9  we can now compare the ONLINEFLOW results to the different prescalings and find that it performs similarly to 10% of the training data. In a setting where one has to work with a trigger fraction of less then 10%, one could benefit from the ONLINEFLOW setup. While the CWoLa results show that the ONLINEFLOW does not only encode features represented in the input variables, but also describes correlations directly, it remains to be shown that its performance is stable when we decouple the main features more and more from the input variables. This happens when we train the generative networks on low-level event representation, challenging the network both in expressivity and reliability. In line with the conclusions from Fig.
Conclusions Data rates of modern particle colliders are a serious challenge for analysis pipelines. In terms of data compression, triggered offline analyses use lossless data recording per event, but at the price of a huge loss in deciding which event should be recorded. Trigger-level analyses accept losses in the individual event information, to be able to analyze significantly more events. Our strategy is inspired by the statistical nature of LHC measurements and aims at analyzing as many events as possible, but accepting a potential loss of information on the event sample level. For this purpose, we propose to train a generative neural network, specifically a normalizing flow (ONLINEFLOW), to learn and represent LHC events for offline analyses. First, we have studied the performance of this ONLINEFLOW compression for a 1-dimensional parametric example.
We mimic a narrow bump hunt on an exponentially dropping flat background and compare the significance achieved by classic methods with different prescale factors with the significance based on generated events from the trained ONLINEFLOW. We find that ONLINEFLOW outperforms a classical, offline analysis for prescale factors larger than 3. 5, while fake signal significances remain at the level of the classical analysis once the flow is trained properly. Second, we looked at a more realistic example, specifically a simulated W -signal available as the LHCO R&D dataset. We use the CWoLa method to extract the signal from correlated phase space observables and to define the benchmark based on the training events with a variable prescale factor. Again, we find that ONLINEFLOW outperforms the offline CWoLa method for prescale factors above a certain threshold (in this case ∼ 10).
Acknowledgements The research of AB and TP is supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under grant  396021762 -TRR 257  Particle Physics Phenomenology after the Higgs Discovery. AB would like to thank the LPNHE for their hospitality. This work was supported by the  Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy   EXC 2181/1 -390900948  (the  Heidelberg STRUCTURES Excellence Cluster ). SD is funded by the  Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy -EXC 2121 Quantum Universe -390833306. BN is supported by the  U. S. Department of Energy, Office of Science  under contract  DE-AC02-05CH11231. The work of DS was supported by  DOE  grant  DOE-SC0010008. RW is supported by  FRS-FNRS (Belgian National Scientific Research Fund ) IISN projects 4.
Initial Populations A last component of the model is the initial population state in the first year. Most naturalized plant species were introduced many years before the first naturalized population was established. This complicates the modeling of the invasion dynamics back to the very first introduction. We assumed that, in the first model year (1980), Plectranthus barbatus was still restricted to the cells where at least one record was reported before 1980, thanks to the BODATSA database. The species was initially reported in N ini = 24 cells noted i ini 1 ,. , i ini N ini , and we assumed it to be absent from all other cells in this first year. The fact that these 24 cells were in large urban areas supports the hypothesis that the species was still mostly growing as cultivated specimens by then and, hence, our assumption that it was absent from wilder areas.
Sampling Process Model For each dataset d, the number of records observed in cell i during year t is modeled with a Poisson distribution: y i,t,d ∼ P(n i,t,. o i,t,d ) (4) where o i,t,d := p detec d N TG i,t,d is the sampling effort on the focal species located in cell i and at year t from dataset/monitoring scheme d and where n i,t,. = ∑ K k=1 n i,t,k is the total plant population of cell i at year t. N TG i,t,d is the total number of records for the target group (TG) of the species  [41]  specifically chosen for dataset d as a proxy of its sampling effort intensity across space and time, as explained further. The parameter p detec d is meant to account for the global variability of the reporting interest across the datasets. This model implicitly assumes that all plants of a cell for a given year are independently sampled with the same small probability (o i,t,d 1).
Indeed, under the assumption that o i,t,d 1 and if n i,t,. is big  ( 30) , the binomial distribution of y i,t,d of the parameters o i,t,d and n i,t,. is well approximated by the Poisson distribution given above. To select a TG for each dataset, we needed to account for the spatial distribution of its constituent species, as it could induce a strong bias in our estimation of the species distribution parameters. Specifically, we would ideally need the underlying sum of the TG species abundances to be as homogeneous in space as possible  [42]  As a simple heuristic to approach this situation and minimize bias, we included the species in the TG one at a time, beginning with the focal species, such that, at each step, the added species maximized the spatio-temporal volume occupied by the TG globally.
Occurrence Data We used the presence-only records from the three largest plant occurrence datasets in South Africa with distinct aims and complementary temporal coverage: the BODATSA database (GBIF extraction on the 01/07/21: https://doi. org/10. 15468/dl. 9q36sf, accessed on 20 August 2022) gathers the records from the professional botanists of South African herbariums since the early 20th century and was crucial to assessing the pioneer locations of Plectranthus barbatus in 1980; SAPIA is a professional monitoring scheme that collates records of alien plant species and was especially active between 1988 and the early 2000s; and iNaturalist is a naturalist community platform which has collected a large amount of validated (research-grade) crowdsourcing records in South Africa since 2017. However, the records from BODATSA and significant parts of those from SAPIA are rasterized over spatial grids.
We dealt with this spatial uncertainty by aggregating the records as counts per dataset, year, and cell of a common grid of 5 min square cells (approx. 10 km size), being the one used by many SAPIA records. We thus build the three-dimensional array of focal species records noted y and the one of TG records noted N TG. The BODATSA records are rasterized over a quarter-degree grid, which is too rough for our analysis, and each cell of this grid exactly overlaps 9 cells of our 5 min grid. Indeed, our grid is nested inside the quarter-degree grid. Thus, we split each BODATSA record into 9 records, each one being associated to one 5 min cell overlapped by the quarter-degree cell and being given the same weight of 0. 1111, overall summing to 1. In mathematical terms, this weight corresponds to the probability that the record belongs to a particular 5 min cell, assuming that its unknown location is randomly distributed across the original quarter-degree cell area, and aims at accounting for the spatial uncertainty associated with this record.
For iNaturalist, we only used research-grade records, i. e. , those that have had at least one external review by a community member and whose species final identification has at least a two-thirds agreement among reviewers; these were extracted on 12/02/21. Our data comprised 190 total records for Plectranthus barbatus. The automatic selection of TG plant species per dataset led to the selecting of 354, 169, and 348 species, respectively, for BODATSA, SAPIA, and iNaturalist, representing a total of 106,287 TG records collected during 1980 and 2021.
Environmental Variables We used spatio-temporal environmental variables in two components of the proposed model: the germination rate p β (x i,t ) that we formulated as a function of vector x i,t , in- cluding land cover and bioclimatic variables, and the long-distance dispersal rate, which depends on the urban percent cover a i of a cell i. Land cover variables. We focused on percent-cover variables for summarizing the land cover for each cell, i. e. , the percentages of the land covered by forests, crops, and settlements, respectively, in each cell. The latter is used for modeling p β and the (longdistance) dispersal kernel disp(. ,. ). The land cover has changed substantially in recent decades, affecting the spatial invasion dynamics. To account for these changes in our model, we reconstructed them by linearly interpolating our percent-cover variables between the 4 sampling years where global coverage datasets were available, namely 1992, 2001, 2010, and 2019.
This dataset provides a worldwide coverage at a 2. 5-arc-minute resolution of monthly maximum/minimum temperature and precipitation. We derived from it (i) the annual mean diurnal range (mean of monthly max-min temperature); (ii) the maximum temperature of the hottest month (max. of monthly mean of daily max. temperature); (iii) the minimum temperature of the coldest month (min. of monthly mean of daily min. temperature); (iv) the precipitation (v) the precipitation of the wettest month; and (vi) the precipitation of the driest month for each year from 1980 to 2018. Indeed, these six bioclimatic variables have been used to model the invasive species' distribution response to climate change  [46]. We extrapolated the values in each 2. 5-arc-minute cell for the years from 2019 to 2021 based on the prediction of a simple linear regression fitted over the years from 2000 to 2018.
We upscaled each raster to our 5 min grid scale by averaging over the 2. 5-arc-minute cells whose center fell inside each 5 min cell. Finally, to restrict the number of parameters, we centered and scaled each of all of the six variables and synthesized them by taking the values along the two first axes of a singular value decomposition (SVD) of the bioclimatic variable values across the combinations of cells and years. This pipeline is provided in our GitHub script repository: https://github. com/ChrisBotella/plectranthus_barbatus (accessed on 20 August 2022).
While Θ 0 was randomly drawn based on prior distributions independently for each chain for the first session, it was set as the parameters maximizing the posterior likelihood across all samples from the past session for the second and third sessions. Box 1 summarizes the model fitting procedure. To assess the convergence of the last MCMC session, we analyzed visually the parameter trace plots, including all chains (Figure  A2  of Appendix A. 3), and computed their univariate and multivariate Gelman and Rubin criteria  (Brooks & Gelman, 1998) , implemented in the R package coda (see Appendix A. 3). We used 1678 posterior samples in the main analysis, obtained from a thinning of samples from the last MCMC session after applying a burnin of 15,000 iterations. We set a thinning interval of 450 to reduce the auto-correlation between MCMC samples due to a very low acceptance rate.
g. , iNaturalist) for invasion monitoring, it also supports the broader call for better access to biodiversity data in urban areas in order to better manage the regional landscape  [53]. For instance, improving the knowledge of urban plant ecology is tied to the availability of better data on urban biodiversity habitats  [54]. In addition, large biodiversity occurrence datasets explicitly tagging cultivated (or captive) versus wild alien individuals, such as those hosted by iNaturalist, are rare. Such information is highly relevant for quantifying the propagule pressure during the naturalization phase, which is crucial for determining later invasion success and could be better accounted for in the kind of model proposed here. Another way to address this challenge of implicit tagging would be to apply automated procedures to classify a posteriori biodiversity records as cultivated/captive or wild.
Some iconic invasive species have a better documented physiology, ecology, and history of introduction and spread, providing the opportunity to use such greater knowledge to specifically reduce uncertainty on the population size and demographic rates. For instance, reproductive traits could be used to constrain more precisely the features of the age-structured fecundity curve, such as the maximum number of seeds per plant and the age before maturity, and account for vegetative reproduction. Estimates of detection probabilities obtained at finer spatial scales than the dense presence-only datasets and site-occupancy models  [60]  could inform prior distribution on the detection rates. Finally, the approximate age of reported individuals could be determined from images of crowdsourcing records (e.
Funding: This work was funded by the  DSI-NRF Centre of Excellence for Invasion Biology. We also acknowledge financial support from the  Millennium Trust , the  National Research Foundation of South Africa (NRF  grant  89967 ),  Mobility 2020  project no. CZ. 02. 2. 69/0. 0/0. 0/18_053/0017850 ( Ministry of Education, Youth and Sports of the Czech Republic ), and the long-term research development project  RVO 67985939  ( Czech Academy of Sciences ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Data Availability Statement: Data used in this study were collected from various sources mentioned in the Materials and Methods sections. The pre-formatted data openly provided for reproducibility are available from Zenodo (V3 https://doi. org/10. 5281/zenodo. 6921965, accessed on 20 August 2022). The R scripts (and pre-fitted parameter samples) to reproduce the last MCMC session, the convergence assessment, and all Figures are openly provided in the article's GitHub repository: https://github. com/ChrisBotella/plectranthus_barbatus (accessed on 20 August 2022).
MRI Acquisition and Reconstruction The multidimensional diffusion-relaxation data set was acquired on a Bruker Avance-III HD 11. 7 T spectrometer equipped with a MIC-5 probe giving 3 T/m maximum gradient amplitude on-axis. The images have an in-plane spatial resolution of 80 × 80 μm 2 and 250 µm slice thickness with a 119 × 119 × 1 matrix size localized approximately at -3. 3 mm from bregma. The data set was obtained using Bruker's multi-slice multi-echo (MSME) sequence customized for diffusion encoding with general gradient waveforms. The diffusion gradients are derived from the variable-angle modification of magic-angle spinning of the q-vector [73] using two symmetric self-refocusing gradient waveforms with 8 ms duration on each side of the refocusing radiofrequency pulse. Images were acquired at varying b-value (0.
All metrics described below were calculated for each of the 100 solutions per voxel, and the values displayed in maps, projections, and ODFs were obtained as medians over the individual solutions. With inspiration from the recent concept of "dynamics detectors" to extract model-independent information about rotational diffusion from NMR relaxation dispersion data  [78] , the diffusion dimensions of the D(ω)-R 1 -R 2 distributions in the primary analysis space [D A ,D R ,θ,ϕ,D 0 ,Γ A ,Γ R ,R 1 ,R 2 ] were evaluated with Eq. 4 at selected values of ω within the narrow 50-150 Hz window actually probed by the gradient waveforms, giving a set of ω-dependent distribution in the [D A (ω),D R (ω),θ,ϕ,R 1 ,R 2 ] space. For each value of ω, these results can be visualized as previous ω-independent distributions  [44, 50, [52] [53] [54]  by projecting D A (ω) and D R (ω) to the dimensions of isotropic diffusivity D iso (ω) and squared normalized anisotropy D Δ 2 (ω), via Eqs 5 and 6, as well as the labframe diagonal elements D xx (ω), D yy (ω), and D zz (ω), and subsequently converting to means E[x], variances V[x], and covariances C[x,y] over relevant dimensions and sub-divisions ("bins") of the distribution space.
Goniometry: Sessile drop method The contact angle of water on the cork surface was measured by goniometry using the sessile drop method. Experiments were conducted under controlled temperature (25 °C) and relative humidity (50%) conditions. A liquid drop of around 3 mL was deposited on the cork surface, paying particular attention to select a non-lenticel area. The contact angle was measured using a goniometer (DSA30, Krüss, Germany) equipped with an image analysis software (Advance, Drop Shape, version 1. 9, Krüss, Germany). The duration of the experiment for each measurement was 60 s. As the value of the contact angle with water did not change with time, the average value was reported. Sixty repetitions were performed. For the measurement of the advancing contact angle, a water drop of 3 mL was firstly deposited on the cork surface.
The average value of the capillary constant obtained was 3. 1 Â 10 À5 ± 0. 5 Â 10 À5 cm 5. Secondly, this value of c was used in Equation (  3 ) to obtain the contact angle h of cork with each liquid from Table  1 , calculated from the initial linear part of the slope of the m 2 ¼ f t ð Þ plot. It is noteworthy that a contact angle greater than 90°cannot be measured using this method, as no wetting of the powder takes place. A specific program was developed (and detailed in section 2. 6) to take into account the variability brought by each step of the measurement on the final calculation of the surface tension and including its polar and dispersive components.
Statistical analysis A specific program has been developed on Matlab (MathWorks, R2018a) to take into account the variability induced both by experimental analysis and data treatment. This includes experimental variability on the determination of the capillary constant c with n-hexane (Eq. (  3 ), with cos h ¼ 1), experimental variability on the determination of the contact angle value h with each liquid (Eq. (  3 ), with known constant c) and variability due to fitting the Owens and Wendt model to the experimental data h (linear regression from Eq. (  4 ), with previously determined h values for each liquid). p replicates were obtained experimentally for the constant c determined with n-hexane. q i replicates were performed experimentally with each liquid i. n-hexane was obviously not included as a liquid, as it was already used for the determination of the constant c.
Conclusions The surface tension of the cork and its wettability with water were assessed. First, the sessile drop method was performed with water on the cork surface, leading to an average value of around 100°, with a broad distribution. Although this may correspond to a rather hydrophobic material, this method is not suitable for a surface as textured and heterogeneous as cork. Another approach, the capillary rise method, was then used on cork particles, allowing for a considerable increase in the contact surface between the liquid and the cork particles. Thanks to specific data processing, the variability resulting from both the heterogeneity of the chemical composition and the physical texture of the material was thus taken into account. This led to a contact angle with water close to 90°and a surface tension of 22.
Acknowledgments We would like to thank  Diam Bouchage  as well as the  French National Association of Research and Technology (ANRT)  for their financial support and  Julie Chanut 's PhD grant. Two-photon images were performed at Dimacell platform with the help of  Pascale Winckler. The authors would also like to thank  Frederic Herbst  ( Laboratoire Interdisciplinaire Carnot de Bourgogne ) for MEB imaging and the DIVVA platform for the access to the goniometry / tensiometry equipments. The authors also thank  Theeraphorn Panrong  for her help during the experiments.
At the Brownfields scale (1-10 km dimension, ∼ 100 m resolution), modelling aims to estimate deep structures beyond the depths reached by drilling, such as the depth of a particular interface or the delineation of potential mineral system components (e. g. fluid alteration pathways and ore deposition environments). At the Mine scale (< 1 km dimension, ∼ 10 m resolution), the objectives are to assist with near mine exploration, resource estimation, ore body localisation, drill targeting, operation scheduling and efficient mining, by characterising local structures and geometries of stratigraphy and mineralisation. In addition to fulfilling these various objectives, 3D models are useful to improve hydrogeological characterisation, to identify critical zones where more knowledge has to be gained, to allow for comparison between datasets and test internal consistency, scenarios and last but not least, for visualisation and communication.
, 1997) , 2-point (geo)statistics (semi-variogram,  Matheron, 1963) , multiple-point statistics (multiple-point histogram,  Boisvert et al. , 2010) , connectivity (Renard and Allard, 2013), wavelet decomposition (e. g. Scheidt et al. , 2015)  and topology (e. g. Thiele et al. , 2016). Some indicators are similar to exploratory data analysis techniques but here they are applied to model ensemble analysis. To illustrate the different indicators, inspired by a Precambrian geological setting, that is a simplified dataset from the Hamersley region, Western Australia, we generated synthetic ensembles of 10 model sets for 3 different scenarios. Each model set is composed of a lithocode voxet describing the lithological units (categorical variable), and of its underlying scalar field voxet (continuous variable).
, 2011, scikit-learn implementation) , we classify all patterns into N c = 10 clusters. Each cluster centroid or barycentre defines its representative. In addition, voxets can be easily upscaled, which allows MPH analysis of potentially large scale features with a small search window at a high level of upscaling. Note that at a given level l of upscaling, the size of the dataset is divided by 2 l along each dimension. Here, to avoid property values smoothing, we perform a stochastic upscaling, i. e. in a 2D case, the upscaled value of a 2×2 subset of pixels is achieved https://doi. org/10. 5194/gmd-15-4689-2022 Geosci. Model Dev. , 15, 4689-4708, 2022  by a uniform random draw among the values of the 4 pixels. Cluster pattern identification is performed at the initial resolution level (l = 0) and at all possible upscaled levels.
For a given upscaling level, once k-means clustering of patterns has been performed on two voxets or datasets, distances between cluster representatives of two images can be computed d(C i 1 , C j 2 ) = N w w=1 C i 1 (w) -C j 2 (w) 2 1 2 , where C i 1 is the ith cluster representative for voxet 1, C j 2 is the j th cluster representative for voxet 2 and w denotes the index of the window search elements. The cluster representatives between two datasets are paired by similarity (smallest distance), and reordered such that ∀i, 1 ≤ i ≤ N c , and C i 1 is paired with C i 2. To account for cluster size differences, the distance between paired cluster representative are weighted by proportion dissimilarities. It results in an MPH cluster-based distance between voxets or datasets 1 & 2 defined as in Eq.