dataesr/scientific-paragraphs-categorization huggingface dataset:
Results This subsection presents the results obtained by GAwC using ELM for all simulations, compared with the baseline  [17]  and with the following competitive methods: W-DMEA-FS, F-DMEA-FS  [10] , W-QEISS, F-QEISS, and W-MOSS  [6]. Preliminary results for the baseline are presented in Table  3 , an example of the best individual over generations is depicted in Fig. 4  and the performances of GAwC and of all the competitive methods on all datasets are summarised in Table  4. Note that, Tables  3  and 4  use the same presentation and use the same criteria as  [10]  in order to ease comparison with the original work. Thus, the rows correspond to 5 UCI datasets plus the ASA-DI dataset and the columns to the experimented methods (i. e. , in Table  3 : Baseline  [17] ; in Table  4 : GAwC and the results of the five competitive feature selection algorithms  [6, 10] ).
The results are presented as: accuracy (as a percentage -%) followed by the number of selected features, presented between parentheses. 10 repeated tests are implemented for each methods for fair comparison with  [6, 10]  (See protocol description in Section 4. 1. 3), and both the best and worst results obtained in terms of accuracy and number of features along with their average over the 10 runs are reported. Each method is ranked according to its average results for each dataset. The rank of each method on both metrics (Accuracy_Rank:NumberOfFeatures_Rank) is reported in Table  4  for each dataset. Finally, the results obtained by GAwC are presented graphically in Fig. 5. They show the two part solutions obtained by GAwC for the six different datasets and the best solutions found by the competitive methods.
Baseline results Before experimenting the new GAwC method, the results obtained by the original method  [17]  without Compass are first measured (See Table  3 ). On the six datasets it underperforms the competitive methods' results published in  [6, 10]  (See Table  4 ), either with regard to both number of selected features and accuracy, or solely for the number of features criterion. With the Diabetes dataset, which is a low dimensional dataset, it obtains an equivalent average number of features to the best competitive method (4. 0 versus 4. 1 for W-DMEA-FS) but a lower accuracy (76. 42 versus 77. 44 for W-DMEA-FS). Nevertheless, when the baseline is applied to higher dimensional cases as the Breast Cancer, Cardiotocography, Heart, Musk1, or the ASA-DI datasets, it performs very poorly in terms of feature selection (21.
8 versus 5. 75 for W-DMEA-FS with the Breast Cancer dataset; 10. 6 versus 5. 69 for W-DMEA-FS with the Cardiotocography dataset; 8. 0 versus 6. 2 for W-DMEA-FS with the Heart dataset; 81. 2 versus 7. 5 for W-DMEA-FS with the Musk1 dataset; 13. 8 versus 5. 4 for W-DMEA-FS with the ASA-DI dataset). Thus, even though it obtains similar results on experiments with a low dimensional dataset (Diabetes) in comparison with the competitive methods, it does not suitably operate in mid or high dimensional cases. Hence, those previous considerations support the idea that the total number of features needs to be considered as part of the objective functions of the baseline method.
Generations Before comparing the results obtained by GAwC in terms of accuracy and number of features with those of the competitive methods and, in order to better understand the method, the results obtained by the Compass over generations are presented in Fig. 4. They illustrate the evolution of the best individual encountered after 3 generations (g 1 , g 30 , and g 88 ) on one specific run with the Musk1 dataset. Moreover, each subfigure shows the results from a different point of view: accuracy (Fig. 4b ), AUC (Fig. 4c ) and number of features (Fig. 4d ). Hence, in Fig. 4 , it can be observed that the projection for generation g 88 on the reference vector is higher than it is for other generations. This directly expresses the methods ability to best fit to the desired trade-off between accuracy, number of selected features, and AUC.
Results obtained by GAwCs and competitive methods: global analysis The results obtained by GAwC and the competitive methods are presented in Table  4  and Fig. 5. 4  shows that GAwC outperforms the competitive methods for all datasets in terms of best accuracy (i. e. , 98. 2% versus 98. 06% for W-DEMA-FS with the Breast Cancer dataset, 90. 87% versus 90. 51% for W-DEMA-FS with the Cardiotocography dataset, 80. 61% versus 78. 13% for W-DEMA-FS with the Diabetes dataset, 88. 15% versus 86. 67% for W-DEMA-FS with the Heart dataset, 82. 6% versus 76. 64% for F-DEMA-FS with the Musk1 dataset, 78. 34% versus 78. 10% for W-DEMA-FS with the ASA-DI dataset). Moreover, GAwC obtains better results in terms of number of selected features with the Heart dataset (i. e. , 4 features versus 6 for W-DMEA-FS, F-DMEA-FS, F-QEISS and W-MOSS) and with the Musk1 dataset (i.
e. , 6 features versus 7 for W-DMEA-FS). Finally, it obtains similar results to those of W-DMEA-FS in terms of number of selected features with the Breast Cancer (i. e. , 4 features), the Cardiotocography (i. e. , 5 features), the Diabetes (i. e. , 4 features), and the ASA-DI datasets (i. e. , 4 features).
Analysis on worst results. As it can be observed in Table  4 , GAwC outperforms the competitive methods for all datasets in terms of worst accuracy (i. e. , 96. 67% versus 96. 63% for W-DEMA-FS with the Breast Cancer dataset, 90. 31% versus 89. 89% for W-DEMA-FS with the Cardiotocography dataset, 76. 83% versus 76. 24% for W-DEMA-FS with the Diabetes dataset, 85. 86% versus 85. 49% for W-DEMA-FS with the Heart dataset, 81. 9% versus 75. 32% for F-DEMA-FS with the Musk1 dataset, 75. 66% versus 75. 62% for W-DEMA-FS with the ASA-DI Fig. 5. The two objectives of part solutions on six datasets by GAwC (ELM) method against the competitive methods. dataset). Moreover, GAwC obtains better results in terms of worst number of features with the Diabetes dataset (i. e. , 4 versus 5 for W-DMEA-FS and F-DMEA-FS) and Musk1 dataset (i.
e. , 8 versus 9 for W-DMEA-FS and F-DMEA-FS). Finally, it obtains similar results in terms of worst number of features than W-DMEA-FS with the Breast Cancer dataset (i. e. , 6 features), with the Cardiotocography dataset (i. e. , 7 features), with the ASA-DI dataset (i. e. , 6 features), and then all competitive methods (except for W-QEISS which is less performant) with the Heart dataset (i. e. , 7 features). 4. 2. 3. 3. Analysis on average results. GAwC outperforms the competitive methods for all datasets in terms of accuracy average as shown in Table  4  (i. e. , 97. 48% versus 97. 3% for W-DEMA-FS with the Breast Cancer dataset, 90. 7% versus 90. 23% for W-DEMA-FS with the Cardiotocography dataset, 79. 75% versus 77. 44% for W-DMEA-FS with the Diabetes dataset, 86. 52% versus 86. 32% for W-DMEA-FS with the Heart dataset, 82.
2% versus 75. 91% for F-DMEA-FS with the Musk1 dataset, 77. 45% versus 77. 44% for W-DMEA-FS with the ASA-DI dataset). Moreover, except with the Musk1 dataset (i. e. , 7. 6 features for GAwC versus 7. 5 features for W-DMEA-FS) GAwC outperforms the competitive methods for all other datasets in terms of average number of features (i. e. , 5. 6 versus 5. 75 for W-DMEA-FS with the Breast Cancer dataset, 5. 6 versus 5. 69 for W-DMEA-FS with the Cardiotocography dataset, 4. 0 features versus 4. 1 features for W-DMEA-FS with the Diabetes dataset, 5. 8 features versus 6. 2 for W-DMEA-FS with the Heart dataset, 5. 0 versus 5. 4 for W-DMEA-FS with the ASA-DI dataset).
4. 2. 3. 4. Observation of the two objectives in solutions. Fig. 5  represents the number of features selected by GAwC (black points) along with each classification accuracy. Those are compared to the competitive methods' best results (colored crosses). Fig. 5  shows that for all datasets and for all selected number of features, the GAwC algorithm obtains a higher accuracy than that obtained by the competitive methods. This figure is an interesting complement to Table  4  since it shows the values of both objectives in the solutions computed by GAwC in the search space defined by the reference vector (See Figs. 3 or 4  for illustration of the Compass).
Studied population: collected features According to  [60] , the ASA-DI clinical data collected includes age, gender, height, weight, body mass index (BMI), smoking status, and currently prescribed medications. Categories of drugs prescribed to less than 15 participants were not considered of interest in the database (i. e. : antiarrhythmic agents, calcium blockers, diuretics, proton-pump inhibitors, nonsteroidal anti-inflammatory drugs). The categories studied were: antihypertensives, cholesterol-lowering drugs, antidiabetic drugs, opioids or benzodiazepines or antiserotonin or other neuroleptics, platelet ADP inhibitors, anticoagulants, acetaminophen, corticosteroids, alpha blockers, anti-TNFa agents or other immunosuppressors, antihistamines, allopurinol, betablockers, betamimetics, and ASA.
Variables validation The experiment with GAwC using ELM with the ASA-DI database, conduced to retain the following features: • Age; • Cholesterol lowering drug; • Anticoagulants; • Non-ASA Antiplatelet AGents (AAG); • TIssue Viability Imaging (TiVi) which is also directly correlated to CIV and ASA intake  [60]. From a cardiologist's point of view: Current-induced vasodilation (CIV) has been proposed as a way of detecting aspirin intake without the need of blood sampling  [60]. Predicting aspirin intake by a bedside test could be of particular interest when it comes to define adherence to treatment. Nevertheless, which of the co-variate are associated to aspirin intake among which clinical factors could be important to determine whether adjustment should be performed when analyzing CIV response.
Evaluation metrics The five features subset GAwC has selected as optimal solution (See Table  4  and detailed results in Table  5 ) obtains an accuracy of 78. 34% which outperforms all competitive methods. Nevertheless, in this paragraph, we deepen our evaluation on several other metrics identified as more relevant in the field of medicine i. e. , Specificity, Sensitivity, MCC and AUC. Thus, we observe that the optimal solution found by GAwC obtains a higher AUC with the ASA-DI Dataset than the original methods used in  [60]  i. e. , GAwc obtains an AUC of 0. 74 (i. e. , Excellent discrimination, see Table  1 ) versus an AUC of 0. 679 (i. e. , Acceptable discrimination, see Table  1 ) in  [60]  with linear models (the authors did not compute accuracy that was not relevant considering the objective of their paper).
Conclusion and perspectives This study has taken a decisive step in the direction of feature selection methods. In the area of binary classification problems, we have developed a novel hybrid approach for multi-objective feature selection called Genetic Algorithm with multi-objective Compass (GAwC). Namely, this method is a GA hybridized with a 3-Dimensional Compass method. According to a user defined trade-off, the method aims at: 1. minimizing the number of features to select, 2. maximizing the accuracy the classifier obtains, 3. maximizing the classifier's AUC. The performances of this approach overtake recent competitive methods on 5 well-known datasets (Breast Cancer, Cardiotocography, Diabetes, Heart, and Musk1 from UCI ML Repository). Moreover, these performances are very interesting from a practitioner's point of view on a new medical dataset (ASA (acetylsalicylic acid) Drug Intake).
Furthermore, these performances could reduce cognitive load upon the practitioners while decreasing the economical cost of variables collection. Further research in this field could include three directions depicted as follows. The first perspective would be the validation of this method on other new datasets. Secondly, the extension of compass to 4D or 5D could be very interesting. For example, the addition of AUK (Area under the Kappa curve)  [63]  could be promising: this score is particularly suitable for measuring classifiers' performance on skewed datasets; note that the mathematical relationship between AUC and AUK is nonlinear. The addition of MCC (Matthew's Coefficient Correlation) would conduce to a 5D compass that may have an interesting potential in the future. Finally, the last perspective deals with the replacement of the evolutionary part of the method by other methods such as Particle Swarm Optimization  [30] , Ant Colony Optimization  [36] , or Artificial Bee Colony algorithm  [31] ,.
kaggle. com/mathchi/diabetes-data-set. 5Available at https://archive. ics. uci. edu/ml/datasets/statlog+(heart). 6Available at https://archive. ics. uci. edu/ml/datasets/Musk+(Version+1). 7Python library http://scikit-learn. org/. •. For each dataset, the value of θ selected by the coarse exploration is used as a starting Compass angle for a finer exploration computing 10 runs with three angles: θ, θ + 1 • and θ + 2 •.
Molecular and energetic analysis of soluble organic matter The total energy available in the soluble organic matter was measured as the heat of combustion with bomb calorimetry  (Harvey et al. , 2016). The instrument was a Parr Oxygen Bomb Calorimeter 6300 M20609 (Parr instruments Moline, Illinois, USA). Calibration samples were always measured first with Benzoic Acid standardized for bomb calorimetry (Parr no. 3415, CAS. reg 65-85-0). Measurements were not replicated because the maximum variation that has been previously observed when duplicates were analysed was found to be 1. 5% (data not shown). Values for heat of combustion (ΔE) were converted into J mmol -1 C. There was insufficient sample to reliably measure the ΔE for the soluble organic matter of rural woodland soil. The molecular composition and diversity of the soluble organic matter was measured by ultra high resolution mass spectrometry prior to the heat dissipation experiment.
These data were then used to derive the NOSC of the molecular species contained in the different soluble organic matter  (LaRowe and Van Cappellen, 2011). Ultra high resolution electrospray ionization Fourier-transform ion cyclotron resonance (ESI FT-ICR) mass spectra were acquired on a Bruker SolariX XR hybrid quadrupole-ICR mass spectrometer (Bruker Daltonics, Bremen, Germany). ESI FT-ICR is equipped with Paracell™ dynamic harmonization, an actively shielded 7 T superconducting magnet and an electrospray ionization (ESI) source (Bruker). Freezedried soluble organic matter was first solubilised in 25% MeOH, and 75% high quality grade and ultrapure water in order to prevent reaction of compounds in solution  (McIntyre and McRae, 2005). The samples were diluted 30 fold in water/methanol (50/50 v:v) and infused continuously at a flow rate of 2 μl min -1 in positive ionization mode at 4 kV.
Data processing was done using Compass Data Analysis 4. 1 (Bruker). The assignment of molecular formulae from the detected mass-tocharge ratio (m/z) was performed using the TRFu algorithms  (Fu et al. , 2020) (version: TRFuFTMSopen07122020). The following formula assigning parameters were employed: the maximum mass error (Δmc = 1010 ppb), 0. 3 = H/C ≤ 2. 5, 0 < O/C ≤ 1. 25, 4 = C ≤ 50, 0 ≤ 13C ≤ 1, N ≤ 5, P ≤ 1, S ≤ 3, singly charged ions in positive mode (max_charge = 1), -0. 5 < double bond equivalent (min_DBE), the maximum intensity derivation of 13 C isotopic peak compared with the theoretical value is 30% (tol_br = 30), no execution of the DBE-O rule (AquaDOM = 0). The resulting neutral molecular formulae were classified into biochemical categories using a multidimentional stoichiometric compound classification approach  (Rivas-Ubach et al.
The organic matter solutions (0. 1 ml; 0. 3 mg C org g -1 soil dry weight) or H 2 OmQ (control condition) were then added drop-wise. The reaction vessels were sealed with a lid (acid proof stainless steel with O-ring seal) and set carefully inside a TAM Air isothermal calorimeter (TA Instruments Sollentuna, Sweden) with a thermostat set to 25 • C. Heat dissipation (μW g -1 soil dry weight) was measured continuously for 24 h. Heat dissipation data was chosen as a measurement of the microbial metabolic response because it gives a more complete and robust measurement of microbial activity than do CO 2 emissions  (Herrmann et al. , 2014). Heat dissipation measurements during the first hour were discarded as the signal was affected by the disturbance of the experimental setup. The heat dissipation due to microbial metabolism of the added organic matter was determined by subtracting the heat dissipation in the H 2 OmQ treatment.
The raw sequence data were processed using MR DNA analysis pipeline. In summary, reads were merged and barcodes were removed after trimming. Sequences <150bp and sequences with ambiguous base calls were then removed. The sequence data were then processed using the DADA2 package  (Callahan et al. , 2016)  (version 1. 16. 0). Sequences were filtered using the function filter-AndTrim() with default parameters and the maximum number of expected errors allowed in a read (maxEE) equal to 2. Amplicon sequence variants (ASVs) were generated with default settings and chimeras were removed. Final ASVs were taxonomically classified using the function assignTaxonomy() against the reference dataset Silva version 138. 1 (htt ps://www. arb-silva. de/documentation/release-1381/). Nucleotide sequences of ASVs were aligned with MAFFT  (Katoh and Standley, 2013 ) (version 7.
48) and a phylogenetic tree was inferred using FastTree  (Price et al. , 2010 ) (version 2. 1. 3) with the GTR + CAT model and the gamma option. ASVs that matched the kingdom of Archaea and Eukaryotes (Chloroplast and Mitochondria) were removed. A total of 783,187 reads were thus obtained. To account for variable sequencing depths, the abundance table was rarefied to the minimum sequencing depth (27,929 reads) among all samples using the rarefy_even_depth() function in phyloseq package (McMurdie and Holmes, 2013) (version 1. 32. 0). The differences in the bacterial community composition among soils were determined using weighted UniFrac distance matrices with the UniFrac() function. The weighted UniFrac distance takes into account both the phylogenetic relationship of ASVs and their respective number of reads.
Bacterial communities were compared by hierarchical clustering of weighted UniFrac distances using the Unweighted Pair Group Method with Arithmetic mean (UPGMA) with the hclust() function. The web-based server MicFunPred (http://micfunpred. microdm. net. in/) was used to estimate the functional profiles of the bacterial communities. MicFunPred minimizes false-positive results in comparison to other approaches  (Mongad et al. , 2021). As a result 5994 KEGG Orthologues (KO) were predicted based on the bacterial ASV sequences and the non-rarefied ASV abundance table. A KO abundance table was then used as input to the web-server MicrobiomeAnalyst in the section shotgun data profiling  (Chong et al. , 2020). The default options for low count and low variance filters were used: KO identifiers with at least 4 counts in 20% of samples were kept.
Statistical analyses Rstudio (Version 1. 3. 1073 -© 2009-2020 Rstudio, Inc) (RStudio Team, 2015. RStudio: Integrated Development for R. RStudio, Inc. , Boston, MA, USA) was used for all statistical analysis and plots. Data were transformed to ensure normality and homogeneity of variances where necessary. Non-parametric tests were carried out with the ARTool package  (Wobbrock et al. , 2011 ) (version 0. 10. 7) when transformations did not result in normality. Differences between groups were determined when relevant by pairwise comparisons of the least-square means using adjusted P-values (Tukey -implementated in the "emmeans" library  (Lenth, 2016)  version 1. 5. 0). The relationships between heat dissipation profiles and the molecular formulae composition of organic matter or the microbial community composition were assessed using Mantel tests on the respective.
Acknowledgments This study was supported by the  Agence Nationale de la Recherche  ( ANR-14-CE22-0021 ) and by the  Swedish Research Council Formas  ( 22836000  and  OPTUS 22551000 ). Financial support from the  French FT-ICR network  ( FR3624CNRS ) for conducting the research was also gratefully acknowledged. The authors would like to thank  D. Billoux ,  V. Pouteau ,  J. Kikuchi ,  J. Fiedler ,  M. Deschamps  and  A. -G. Haglund  for assistance with laboratory work. We also thank  H. Wang  and  B. Kammerer  for their suggestions,  Q. -L. Fu  for updating the TRFu algorithms,  N. Delhomme  for helping with bioinformatics and two anonymous reviewers for their pertinent and constructive comments that helped improve the manuscript.
Data availability All data needed to evaluate the conclusions in the paper are present in the paper and the Supplementary Materials. The datasets generated during the current study and the custom R scripts used for data analysis are available from the corresponding author on reasonable request and from a public repository entitled zenodo (https://zenodo. org/record/5547311) (Creative Commons Attribution 4. 0 International). The nucleotide reads have been deposited to NCBI under the BioProject PRJNA724026; sample accession numbers are: SAMN18839354 to SAMN18839371.
Based on a network of 1874 gauges from Ireland, the United Kingdom, France, Spain, and Portugal, Vicente-Serrano et al. (2019)  found that the physical drivers of streamflow trends (specifically on annual average flows) vary considerably between northern and southwestern Europe-Increases in the north are strongly climate-driven while in the south irrigation and land cover influences play a role as well as climate. However, while these studies have provided some large-scale context for potential changes in hydrological drought, the transferability of conclusions is limited by the relative sparseness and geographical biases of the datasets, particularly given the predominance of data from northern and western Europe and relative lack of coverage in the east and south. At the same time, strong spatial variability of hydrological droughts in various parts of Europe has been linked to different local/regional characteristics  (Barker et al.
Features of the Monthly Streamflow of Europe Dataset (MSED) and Map Viewer The reconstructed monthly streamflow of 3,224 gauging stations for Europe in the 1962-2017 period are included in the MSED map viewer. The MSED map viewer includes the location and the graphic representation of the temporal serie (hm3/month) of 3,224 gauging stations. All information available in the MSED map viewer can be downloaded in txt format, freely available on the website http://msed. csic. es/, maintained by the Spanish National Research Council (CSIC). The folder download has geographic information (coordinates, altitude, country and source of data) and information on the reconstructed monthly streamflow (hm3) of each station.
Dataset Creation This study analyzed spatiotemporal changes in monthly streamflow and hydrological drought over Europe between 1962 and 2017, using a spatially dense data set with unprecedented geographical coverage compared to past drought studies. Previous studies have employed a much sparser network of gauging stations or focused on particular regions (e. g. ,  Hisdal et al. , 2001; Parry et al. , 2012; Van Loon & Laaha, 2015). Most of the previously employed datasets (e. g. ,  Hannaford et al. , 2011; Hisdal et al. , 2001; Stahl et al. , 2010)  depended heavily on gauging stations from central Europe, mainly Germany and northern France, and Great Britain, with less representation of regions like southern Spain, Ireland, and large portions of northern and central Europe. As compared to several available streamflow databases (e.