dataesr/scientific-paragraphs-categorization huggingface dataset:
Description of the synthetic dataset To create synthetic data, a climate model known as PLASIM  (Fraedrich et al. , 2005a)  was used, which is a general circulation model (GCM) of medium complexity based on a simplified general circulation model PUMA (Portable University Model of the Atmosphere)  (Fraedrich et al. , 2005b). This model based on primitive equations is a simplified analog for operational numerical weather prediction (NWP) models. This choice facilitates the generation of synthetic data thanks to its low resolution and reasonable computational cost. Different components can be added to the model in order to improve the circulation simulation such as the effect of ocean with sea ice, orography with the biosphere or annual cycle. A 100-year daily simulation was run at a T42 resolution (an approximate resolution of 2.
8 • ). We used orography and annual cycle parameterization; ocean and biosphere modelization were turned off in order to keep the dataset simple enough for our exploratory study. We removed the first 10 years in order to keep only the stationary part of the simulation. These resulting 90 years of simulation constitute the sampling of the climate distribution that we aim to reproduce. As preprocessing, each of the channels was normalized. Each database sample is an 82-channel (nfield) twodimensional matrix of size 64 (nlat) by 128 (nlon) pixels. The channels represent seven physical three-dimensional variables: the temperature (ta), the eastward (ua) and northward (va) wind, relative humidity (hus), vertical velocity (wap), the relative vorticity (ζ ), divergence (d) and geopotential height (zg) at 10 pressure levels from 1000 to 100 hPa, plus the surface pressure (ps).
Another channel was added to represent the latitude: it is an image going from -1 at the top of the image (North Pole) to 1 at the bottom (South Pole) in every column. It was found that hard coding the latitude in the data improved the learning of physical constraints, allowing the network to be sensitive to the fact that the data are represented by the equirectangular projection of the atmospheric physical fields, and, for example, the size of meteorological objects increases closer to the poles. Finally, the choice of having diagnostic variables in the dataset was to help the post-processing, and assessment of their necessity requires further research.
Comparison between climate dataset and generated climate Our study aims to have a generator able to reproduce the climate distribution present in the dataset made from the lowresolution GCM PLASIM. This section proposes a way to assess the quality of the distribution learned by the WGAN. The first required property for a weather generator is a low computational cost compared to the GCM that produced the data. Here the simulation with the GCM PLASIM took 50 min for a 100-year simulation in parallel on 16 processors, whereas the generator took 3 min to generate 36 500 samples equivalent to a 100-year simulation on an NVIDIA Tesla V-100. Each generated sample is compared with dataset samples. Figures  8  and 9  show a sample where only the pressure levels 1000, 500 and 100 hPa are represented for readability.
It should be noted that the generated fields seem to be spatially noisy compared to the dataset. The periodic boundary is respected knowing that in the dataset the borders are located at the longitude 0 • where no discontinuities can be observed. In the figures, the image is translated in order to have Europe at the center of the image and to see whether some discontinuities remain. In order to quantitatively assess the generator quality, Figs. 10 and 11 show the mean and standard deviation pixelwise differences over 10 800 samples (equivalent to 30 years of data) between normalized dataset and generations. It appears that fields where small-scale patterns are present are the most difficult to fit for the generator. In order to go further in the analysis of the generated climate states, a singular value decomposition (SVD) was performed over 30 years of the dataset (renormalized over the 30 years).
Then the same number of generated data was considered and projected onto the five first principal components https://doi. org/10. 5194/npg-28-347-2021 Nonlin. Processes Geophys. , 28, 347-370, 2021 Figure  8. Sample on three different pressure levels (1000, 500 and 100 hPa) taken from the dataset. The samples were horizontally transposed in order to have Europe at the center of the images. Coastlines were added a posteriori for readability. Units available in Table  2. of the SVD that represent 75 % of explained variance of the dataset. In Fig. 12  the dot product is represented between SVD components derived from the dataset (u i ) i∈{0,. ,4} and another one derived from the generated data (v i ) i∈{0,. ,4}. Figure  12  represents the cross-covariance matrix defined by s ij = u i • v j. Values close to 1 or -1 show that the eigenvectors for both datasets (original and generated) are similar.
This is another way of assessing whether the covariance structure of the original data is being preserved, and Fig. 12  shows that the five eigenvectors are similar. One should note that the SVD algorithm used from  Pedregosa et al. (2011)  suffers from sign indeterminacy, meaning that the signs of SVD components depend on the random state and the algorithm. For this reason, we consider the dot product close to both 1 and -1. One should note that an inversion remains between the components with indexes 3 and 4, which could be explained by a difference of eigenvalue order (sorted in decreasing order) in each dataset that determines the order of eigenvectors. The fourth principal direction (index 3 in the figure) of the generated data represents more variation of the generated dataset than the same direction explains variation in the original dataset.
The magnitude of the geostrophic wind scales with the strength of the horizontal gradient of geopotential  Vallis (2006, Sect. 2. 8. 2, p. 92). This asymptotic balance Eq. (  15 ) is verified to within 10 % of error at mid latitude, that is, u = u g + u ag , where the magnitude of the ageostrophic wind, u ag , is less than 0. 1 of the magnitude of the real wind u. Figure  19a  illustrates a particular boreal winter situation from the PLASIM dataset, focusing on the mid latitude and presenting a low area of geopotential in the southwest of Iceland. It appears that the wind is well approximated by the geostrophic wind, which is quantitatively verified in Fig. 20a  that shows the norm of the ageostrophic wind normalized by the norm of the wind (that is, the relative error when approximating the wind by the geostrophic wind): the order of magnitude of the error is around 20 %.
Despite the truncation, the geostrophic approximation seems to not be respected everywhere and could be a quantitative metric to monitor in order to improve our method. We find that weather situations generated from samples in the latent space reproduce the geostrophic balance at an order of approximation that is similar to the one of the real dataset. This means that the generator is able to produce the realistic multivariate link between the wind and the geopotential. This property is essential in operational weather forecasting, e. g. , in producing balanced fields in the ensemble Kalman filter.
Thermal wind balance The thermal wind balance arises by combining the geostrophic wind Eq. (  15 ) and the hydrostatic approximations, ∂ ∂p = -1 ρ , where ρ is the density  (Vallis, 2006, Sect. 2. 8. 4, p. 95) : taking the derivative of Eq. (  15 ) with respect to the pressure p makes the hydrostatic approximation appear, so that the vertical derivative of the geostrophic wind can be written as ∂ ∂p = - R pf k × ∇ p T , ( 16 ) where the ideal gas equation, p = ρRT , has been used. Equation (  16 ) is the thermal wind balance that relates the vertical shear of the horizontal wind to the horizontal gradient of temperature. In particular, when the temperature falls in the poleward direction, the thermal wind balance predicts an eastward wind that increases with height. Figure  21a  and b show the vertical cross section of the zonal average of temperature and of the zonal wind for a particular weather situation in the dataset, corresponding to a boreal winter situation of the same weather situation represented in Fig.
This results in the generator being able to render a weather situation that reproduces the thermal wind balance. Moreover, Fig. 23  shows the thermal wind balance averaged on 30 years for the dataset (Fig. 23a ) and generations (Fig. 23b ); both are very similar. This section has shown the ability of the generator to reproduce some important balances present in the atmosphere. In particular, the generator is able to produce midlatitude cyclones whose velocity field is in accordance with the geostrophic balance. The authors emphasize that it is necessary to conduct more analysis of the weather situations out-putted by the generator, which is beyond the scope of this study. For example, it would be interesting to assess whether other inter-variable balances are present, such as the ω equation or vertical structures.
More advanced strategies, such as training a separate inference network  (Chan and Elsheikh, 2019) , are also possible to apply Bayes' rule without using a particle filter. It is also possible to condition the generations to a specific date in the annual cycle with slight modifications in the network architecture. One could think to condition the output of the generator by a forcing field in input such as forcing fields like SST fields for data assimilation application, which should be possible but with more important modifications of the network architecture and a possible impact on the speed of the training procedure. A more sophisticated dataset could be used, such as a true climate reanalysis, to see the effect of the dataset complexity on the method's performance. The optimization of the network's architecture and a sensitivity study on the hyperparameters such as the dimension of the latent space, for example, would be useful.
Acknowledgements. This research paper was written during a thesis in partnership with Total. We would like to thank  Philippe Berthet ,  Anahita Abadpour ,  Daniel Busby  and  Tatiana Chugunova  for their support in the application of our method in different fields of the geosciences. We would like to thank  Rabeb Selmi  for her help and for sharing her expertise. Review statement. This paper was edited by  Takemasa Miyoshi  and reviewed by two anonymous referees.
Code availability. The code and the weights of the trained neural network are available at the following GitHub repository in v0. 1: https://github. com/Cam-B04/ Producing-realistic-climate-data-with-GANs. git (last access: January 2021). The repository is associated with the following DOI: https://doi. org/10. 5281/zenodo. 4442450 (Besombes, 2021). Data availability. The dataset used is available on demand. The GitHub repository explains how to recreate it from a PLASIM simulation.
Namely, large maps create many small clusters. In contrast, a map of small size produces fewer but more composite clusters. Once each data holds an imaginary point into the input space, a search for the closest points (users) is performed  [16]. The pseudo-code for the classic SOM clustering tailored to the scenario of multicast subgrouping is presented in Algorithm 1. We recall that, at this stage, the SOM algorithm operates by considering multicast users, whereas blockage is accounted for in the next stage (see section III-B). The following notation is used throughout the paper: • t, the current iteration (epoch); • λ, the time constant that is used to decay the radius and learning rate; • E, the iteration limit, i. e. , the total number of iterations the network can undergo; • i, the row coordinate of the neuron grid; β i,j (t) = exp -d 2 2σ 2 (t) , σ(t) = σ 0 exp -t λ ; 13 Update the weight vectors of the neurons in the neighborhood of the BMU (including the BMU itself) by pulling them closer to the input vector: w i,j (t + 1) = w i,j (t) + α i (t)β i,j (t) [x(t) -w ij (t)], α(t) = α 0 exp -t λ ; 14 t ← t + 1; 15 end • j, the column coordinate of the neuron grid; • d, the distance between a neuron and the best matching unit (BMU); • w, the weight vector; • w ij (t), the weight of the connection between the neurons i, j in the grid and the input vector's instance at iteration t; • x is the input vector; • α(t), the learning rate, decreasing with time in the interval [0, 1] to ensure the network convergence; • β ij (t), the neighborhood function, monotonically decreasing and representing a neuron i, j distance from the BMU, and the influence it has on the learning at step t; • σ(t), the radius of the neighborhood function, which determines how far neighbor neurons are examined in the 2D grid when updating vectors.
IV. PERFORMANCE EVALUATION In this section, we evaluate the effects of the proposed D2D-assisted multicast scheduling via learning and assess the performance of our mmWave system. By default, we assume linear antenna array with 32×4, 16×4, 8×4, 4×4, 2×4, and 1×4 antenna elements. The transmit power is fixed at 33 dBm. The other main simulation parameters, including the inputs for the SOM algorithm implemented in Matlab using the Neural Network Clustering App with map dimension 2x2 (N = 4), are listed in Table  I. The analyzed performance parameters are: • Network throughput (also known as aggregate throughput) is the sum of data rates that are delivered to all terminals in the network. • Energy consumption is the amount of energy used during a given period of time. • Energy efficiency (or efficient energy use) is defined as the achieved network throughput divided by the consumed energy in bit/s/J.
However, no apparent restart of growth was recorded at 24 h with these mutants, likely due to the higher bactericidal activity of primary macrophages. In contrast, multiplication of the ΔtalA mutant remained identical to that of WT F. novicida at all time points tested (S6 Fig). Overall, these data show that the ability to consume available carbon sources in infected macrophages, which depends on the enzymes TktA, Rpe and RpiA, is essential for intracellular Francisella multiplication.
We next wished to follow and quantify the dynamics of intracellular multiplication of the PPP mutants by time-lapse video microscopy, using a fully automated microscope (Incucyte 531 S3, Essen BioScience). J774-1 macrophages with red nuclei (here designated J774. 1 red ) were infected with GFP-expressing bacteria at an MOI of 100 and infection was followed over a 48 h-period, in 96-well plates (Figs 5 and S7 and S1-S5 Movies). We first quantified the total number of J774. 1 red cells over time to evaluate the impact of bacterial infection on cell viability (see Materials and methods). This assay showed that cells were able to continuously multiply at least during the 40 first hours and were not affected by bacterial infection (Fig  5A ). Intracellular bacterial multiplication was next followed by monitoring the total green area intensity (reflecting multiplication of the GFP-positive bacteria) over time translated on graph line by using the Incucyte S3 software  (Fig 5B).
Multiplication of the ΔtktA, Δrpe and ΔrpiA mutants was affected to variable extents. The ΔtktA mutant was again the most severely affected whereas the ΔtalA mutant showed wild-type multiplication. One hour after infection, cells were washed several times with gentamicin-containing medium (10 μg mL -1 ) to remove extracellular bacteria. A gentamicin concentration of 10 μg mL -1 was then maintained throughout the experiment. Images were acquired every 20 min using the IncuCyte S3 live cell imaging system (Essen BioScience) over a 48-h period. The kinetics of intracellular multiplication of PPP mutants were monitored by Incucyte S3 software (Incucyte Live-Cell Analysis System, Sartorius). F. novicida WT is shown in blue; the negative control strain Δfpi, in cyan; ΔrpiA, in red; Δrpe in yellow; ΔtktA, in orange; and ΔtalA, in green.
(A) Total number of J774. 1 red cells was determined by counting the number of red nuclei per image every 20 minutes. An image taken 1h post-infection generally contains between 50 and 100 cells. Results are presented as the mean and standard deviation of sixteen images. (B) Bacterial multiplication (GFP-expressing bacteria) was monitored by checking the total area of green particles (i. e. , GFP-expressing bacteria) in each image (containing between 50 and 250 cells) every 20 min. Multiplication was translated into graphical lines using the Incucyte S3 software. Results are presented as the mean and standard deviation of sixteen images. (C) The total number of GFP-positive cells (i. e. , infected with GFP-expressing bacteria) was monitored by quantifying the percentage of cells with red nuclei associated with at least one detectable green intensity signal in one image (containing between 50 and 250 cells).
Results are presented as the mean and interpolation curve of eight images. (D) At selected times (10 h, 24 h, and 48 h), the number of GFP-positive cells was decomposed into 5 categories based on the area of the detected GFP signal (in pixels; px): i) 0-200; px ii) 200-400; px, iii) 400-600; px; iv) 600-800; and >800; px; and corresponding to increasingly infected cells. The data presented correspond to the total number of cells in 8 images. https://doi. org/10. 1371/journal. ppat. 1009326. g005 We then used a machine learning approach (see Data analyses, Materials and methods; S8 Fig) to quantify from the video microscopy data: i) the total number of cells, at each time point, and ii) for each cell, at each time point, the presence of a GFP signal (bacteria) and the GFP area (bacterial multiplication).
Indeed, a ca. 3-fold increased expression of most of the genes of the fad operon was recorded in exponentiallygrown bacteria in K3 medium (OD 600 0. 5), in the mutant compared to WT. In late exponential phase (OD 600 1), this increase reached up to 6-fold that of WT (S10 Fig). Altogether these data strongly suggested a yet unanticipated link between PPP and fatty acid metabolism.
Data processing following nanoLC-MS/MS acquisition: The MS files were processed with the MaxQuant software version 1. 5. 8. 30 and searched with Andromeda search engine against the Uniprot F. novicida database (release 2016, 1 722 entries). To search parent mass and fragment ions, we set a mass deviation of 3 and 20 ppm respectively. The minimum peptide length was set to 7 amino acids and strict specificity for trypsin cleavage was required, allowing up to two missed cleavage sites. Carbamidomethylation (Cys) was set as fixed modification, whereas oxidation (Met) and N-term acetylation were set as variable modifications. The false discovery rates at the protein and peptide levels were set to 1%. Scores were calculated in MaxQuant as described previously. The reverse and common contaminants hits were removed from MaxQuant output.
Proteins were quantified according to the MaxQuant label-free algorithm using LFQ intensities; protein quantification was obtained using at least 1 peptide per protein. Statistical and bioinformatic analysis, including heatmaps, profile plots, and clustering, were performed with Perseus software (version 1. 5. 5. 31) freely available at www. perseusframework. org. For statistical comparison, we set two groups, WT and ΔtktA, each containing four biological replicates. Each sample was run in technical triplicates as well. We then filtered the data to keep only proteins with at least 3 valid values out 4 in at least one group. Next, the data were imputed to fill missing data points by creating a Gaussian distribution of random numbers with a SD of 33% relative to the SD of the measured values and 2.
The metabolites were separated over a linear gradient from 80% organic to 80% aqueous phase for 15 min. The column temperature was 48˚C and the flow rate was 200 μl/min. The metabolites were detected across a mass range of 75-1 000 m/z using the Q-Exactive Plus mass spectrometer (Thermo) at a resolution of 35,000 (at 200 m/z) with electrospray ionization and polarity switching mode. Lock masses were used to ensure mass accuracy below 5 ppm. The peak areas of different metabolites were determined using TraceFinder software (Thermo) using the exact mass of the singly charged ion and known retention time on the HPLC column. Statistical analyses were performed using MetaboAnalyst 4. 0 software. The algorithm for heatmap clustering was based on the Pearson distance measure for similarity and the Ward linkage method for biotype clustering.
Network analysis Aiming the metabolomics and proteomics data integration, we used the regularized canonical correlation analysis (rCCA) to build the correlation networks. The rCCA is a modification of the classical canonical correlation analysis (CCA), which is a multivariate statistical method used to assess correlations between two multivariate datasets  [61]. Three biological replicates of each dataset were used (proteomics and metabolomics) and, in order to reduce the discrepancy of the scales, we combined some transformations and scaling methods, like generalized logarithm transformation  [62] , pareto scaling  [63]  or z-score. CCA aims to maximize the correlation between linear combinations of variables (canonical variates) in two datasets  [64]. The rCCA analysis used is part of the mixOmics package V5.
2  [65]  for R (http://www. R-project. org). Regularization parameters (λ 1 and λ 2 ) were estimated using the tune. rcc() function to evaluate the cross-validation score (cv-score) for each point in the network, achieving the values for λ 1 and λ 2 that offered the highest cv-score. To create the canonical correlations and the canonical variates between the datasets we use the rcc() function and the network() to produce the networks. We set the correlation threshold to � 0. 95, this value was chosen to obtain biologically interpretable networks that were neither too sparse nor too dense  [66]. After, the networks were exported to Cytoscape network visualization software V3. 8  [67] , using the organic layout with diameter of the node relative to the number of undirected edges. The intensity of edge colors represents the correlation values ranging from dark green (negative correlation) to dark red (positive correlation).
In silico analyses For synteny prediction, we used the Kyoto Encyclopedia of Genes and Genomes (KEGG) Sequence Similarity Database (SSDB) to search conserved gene clusters containing homologs of FTN_1333 using default parameters (gap size 0, threshold 100). The SSDB database (Release 94. 0, last accessed on 14th April 2020) contains SSEARCH computation results (based on the Smith-Waterman similarity score) for all pairwise genome comparisons of the KEGG database (10. 1093/nar/gkw1092). The Microbial Genomic context Viewer MGcV allowing the coloring of the genes by Clusters of orthologous gene (COG) category retrieved from NCBI RefSeq was used for visualization (10. 1186/1471-2164-14-209).
These studies were supported by  INSERM ,  CNRS  and  Universitede Paris. Jason Ziveri  and Heloise Rytter were supported by a fellowship from the "Delegation Generale à understand the key role played by the PPP in the survival of Francisella in its intracellular niche. l'Armement". Elodie Ramond  was supported by the  Agence Nationale de la Recherche  ( ANR-15-CE15-0017 StopBugEntry ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.