dataesr/scientific-paragraphs-categorization huggingface dataset:
Let us describe how (  13 ) is implemented. The connectivity kernels were computed with the three-dimensional Fast Fourier Transform (3D FFT) on Graphics Processing Units (GPU) based on the package CUDA. jl (see  [7, 8] ). In order to compute the bifurcating branches, the reduced equations (see  [30]  or proof of proposition 17) at the bifurcation point were computed thereby yielding a system of polynomial equations of degree 3 in a number of variables equal to the dimension of the kernel of the Jacobian. The roots of these polynomial equations are then computed and used as guesses for points on the bifurcated branches which are corrected using a Deflated-Krylov-Newton (see  [26] ) to prevent converging to the trivial solution. This provides an entirely automatic procedure to find the bifurcated branches at a bifurcation point of any dimension.
Our model equations are equivariant w. r. t to this spatio-chromatic group and, by identifying some of its axial subgroups we have been able to prove (in part numerically) using the equivariant branching lemma that the simplest stationary solution (which turns out to be 0) bifurcates at pitchforks from which arise branches of stationary solutions enjoying the symmetries of the corresponding axial subgroup. Going into the numerics, the Julia package developed by the third author has allowed us to explore a much larger range of values of the bifurcation parameter than for example the authors of  [10]  whose approach, based on the use of normal forms, is not well-suited to the analysis of subcritical bifurcations. It has also allowed us to discover cases of snaking and hence to observe stable localised solutions.
Data related to sepsis, such as severity of sepsis and bacteriological documentation were also recorded. Organ failure was assessed as follows: Glasgow coma scale for neurological failure; doses of vasopressors (µg/kg/min) and lactatemia (mmol/L) for hemodynamic failure; invasive mechanical ventilation, PaO 2 and PaCO 2 (mmHg) for respiratory failure; albuminemia (g/L), prothombine time (PT), V factor, Alanine-Amino-Transferase (AST), Aspartate-Amino-Transferase (ALT), Gamma-Glutamyl-Transferase (GGT) and ALP (IU/L), bilirubinemia (µmol/L) for liver failure; pH, arterial lactate for metabolic failure and 24-h urine output following the injection (mL), uremia (mmol/L) and serum creatinine (µmol/L), and Kidney Disease Improving Global Outcomes (KDIGO) for renal failure. The KDIGO stages acute kidney injury (AKI) into three stages based on serum creatinine and urine output  [15, 16].
Population Pharmacokinetics Amikacin data were analyzed using non-linear mixed-effect modeling software (MONOLIX ® , version 2020R1, Lixoft, Antony, France), together with the stochastic approximation expectation-maximization (SAEM) algorithms. Monolix (Non-linear mixed-effects models or "MOdèles NOn LInéaires à effets miXtes" in French) is a platform of reference for model based drug development, used by academia, the pharmaceutical industry as well as the US regulatory agencies. Models were coded with differential equations in a MLXTRAN script file. Various structural models were tested, including one-or two-compartment distribution with first-order absorption and elimination rate constants. Categorial covariates were tested as follows: θ i = θ pop × θ COV (1) where θ i is the individual parameter for the patient, θ pop is the typical value of the parameter, θ COV is the covariate parameter, and COV is the category 0 or 1 for the covariate under study.
Continuous covariates were associated with pharmacokinetics parameters by a power function: θ i = θ pop × Cov i (Median(Cov)) PWR (2) Other covariates were also tested: demographic characteristics (age, sex, total body weight), hepatic function (albumin, PT, total bilirubin), renal function (urea, serum creatinine, KDIGO stage), severity scores (SAPS II and SOFA score), and hemodilution parameters. An effect of a covariate on a structural parameter was retained if it caused a decrease of Bayesian information criterion (BIC) and/or reduced the corresponding between subject variability (BSV) with p < 0. 05. The objective function value reduction was tested for significance via a likelihood ratio test. Diagnostic graphics were used for evaluating the goodness-of-fit. Concentration profiles were simulated and compared with the observed data using a prediction corrected visual predictive check (PC-VPC) to validate the model.
Population Pharmacokinetic Model A total of 215 plasma concentrations were recorded and used for the modeling process. The mean number of measurements per patient was 5. 5. Individual amikacin curve profiles are presented on Figure  1. Data were best described by a two-compartment model with first-order elimination. The pharmacokinetic parameters of this model were clearance (CL), central volume of distribution (V 1 ), intercompartmental clearance (Q), peripheral volume of distribution (V 2 ). The selected residual variability was a proportional error model. Inclusion of KDIGO on CL decreased the BIC by 107 units. The other significant covariates were TBW on V 1 , which reduced the BIC by 35 units and being supported with ECMO or not on V 2 , which reduced the BIC by 24 units. Implementing SOFA score, biological covariates, hemodilution parameters as covariate on CL, V 1 and V 2 did not improve the model.
Methods Data and functional trees. We focused on 28 terrestrial mammal species (14% of the 202 European species), and 37 terrestrial breeding bird species (10% of the 378 European species), which have been reintroduced at least once in Europe. Reintroduced species were identified through a comprehensive search of the reintroduction-related literature, and details of the literature research protocol and complete lists of reintroduced species can be found in Thévenin et al. 30. For functional traits, we used the dataset published by Wilman et al. 36  who compiled functional trait values for all 9993 and 5400 extant bird and mammal species from the literature. These traits represent how a given organism impacts the community structure and species interaction network, with potential consequences for ecosystem functioning  6, [37] [38] [39].
They can hence be considered as "effect" traits, which differ from "response" traits, i. e. traits that determine the response of organisms to environmental change  40, 41. These traits are relevant to the "Eltonian niche", i. e. a multidimensional space describing biotic interactions and resource-consumer dynamics related to the acquisition of energy and nutrients. This dataset provides information on body mass, diet type, foraging behaviour along a vertical gradient (foraging stratum) and period of foraging activity (e. g. nocturnal, diurnal). Body mass is a continuous variable given in grams. A species' diet is described as a multistate nominal variable representing whether the species' diet includes one or several of the following eight categories: Invertebrate, Vertebrate, Fish, Carrion, Nectar, Seed, Fruit and Plant (e.
g. grass, ground vegetation, seedlings, weeds…). For birds, the foraging stratum is also given as a multistate nominal variable with seven different discrete levels from below the water surface to aerial foraging. For mammals this variable is categorical, with species assigned to only one category (ground level including aquatic foraging; ground foraging; scansorial; arboreal; aerial). Finally, the period of foraging activity is given as a multistate nominal variable for mammals (diurnal, nocturnal and/or crepuscular), while it is a binary variable for birds (nocturnal vs diurnal). Functional trait values for reintroduced birds and mammals are available in Supplementary Dataset. We built up functional trees from functional traits distances between each pair of species  14. We calculated pairwise functional distances using a mixed-variable coefficient that allows various types of variables to be included.
For a given subset of species, the higher the FD, the more functionally dissimilar the species are within the subset. For each taxonomic group (terrestrial mammals and terrestrial birds), we calculated the FD of the subset of reintroduced species [FD reint ] using the pd. query function from the PhyloMeasures R-package  46. For each taxonomic group, we compared this value to the FD value expected for a random subset of species of the same size in the associate regional species pool. For that purpose, we used the pd. moments function (Phylomeasures package), which provides optimized algorithms to compute the exact expressions of the expectation [µFD] and the SD[sdFD] of the FD for a given number of species in a functional tree. We considered that a subset of reintroduced species was representative of the regional functional diversity if the FD reint value did not significantly depart from the associated 95% confidence interval calculated as µFD ± 1.
Results We found that the diversity of trait values combinations carried by reintroduced species of mammals in Europe is not highly representative of the functional diversity of the continental assemblage. The functional diversity (FD) of the 28 reintroduced species of mammals was lower than expected under our null model (FD reint = 3. 857, µFD = 4. 807, sdFD = 0. 443, p-value = 0. 03) (Table  1 ). However, our analysis showed that several reintroduced species are among the most functionally distinct species of the terrestrial mammal assemblage in Europe (Supplementary Dataset). Thus, despite taxonomic biases being associated with relatively low functional diversity, our analysis showed that the median FDist of the 28 reintroduced mammals is higher than expected under our null model (median FDist reint = 0.
Discussion Functional diversity has been advocated as a biodiversity measure that accounts for dissimilarities in species' forms and functions, and here we provide new insights on how reintroduction targets can be representative of the diversity of a continental assemblage of species and thus contribute to the conservation of biological diversity at large scales. The data we collected show that reintroductions of birds in Europe are taxonomically biased and mainly involve raptorial and game species. Interestingly, although these taxonomic biases are associated with less phylogenetic complementarity for reintroduced birds at the European scale  30  , our results show that reintroductions of terrestrial birds in Europe targeted diverse combinations of functional traits. Our findings also indicate that reintroduction programs involved species of terrestrial mammals in Europe that are individually more functionally distinct than if drawn at random from the continental assemblage, but that, collectively, these species carry less functional diversity than expected.
The functional differences described here mostly concern species' resource use patterns. Resource use may not reflect finer divisions in some functional groups and may be less appropriate to accurately describe some ecosystem processes. It may thus overlook the important role of some individual species  61. While the idea of prioritizing species based on their functional originality is promising, it remains challenging in practice and enhancements of trait databases are needed for vertebrates. Information on functional traits has been made increasingly available for animals but the number of traits considered, and the extent to which they relate to an ecological function is still limited compared to plants  40, 62. In the dataset we used, diet types and body mass provided a proxy for the trophic level of a species, but will mostly help differentiate herbivores from carnivores.
Outcome analyses Patients were grouped according to RHR at baseline (<70 bpm, 70-75 bpm, >75 bpm) and the groups were studied further by subdividing them into groups with sinus rhythm or AF and HFpEF (ejection fraction ≥50%) or heart failure with mildly reduced ejection fraction (HFmrEF; ejection fraction 40-49%). The cut-offs for RHR were chosen according to previous literature in patients with HFrEF. 5, 6, 9, 10, 12 HR >70 bpm is the cut-off from where risk for HHF is increased,  5, 9, 10  while the risk is increased for CVD at >75 bpm. 5, 12 Consistently, the treatment effect for HHF after RHR reduction with ivabradine is positive at >70 bpm,  5, 6  while the death endpoints become significantly reduced by heart rate reduction with ivabradine at >75 bpm. While these interventional data are obtained in patients with HFrEF, similar findings were observed in HFpEF.
The frequencies of the pre-specified safety outcomes were investigated in a logistic regression model adjusted with the same covariates as the Cox model. The association between hazard and RHR as continuous variable was analysed non-parametrically using restricted cubic splines allowing for non-linear relationships. Four knots (5th, 35th, 65th, and 95th percentile of baseline RHR) were chosen for the analysis. Hazard ratios (HR) and 95% confidence bands depending on RHR are evaluated using 60 bpm as reference (HR = 1). All analyses were performed by the sponsor, after agreeing on a statistical analysis plan with the executive committee of EMPEROR-Preserved using SAS version 9. 4 (SAS Institute, Cary, NC, USA). All p-values reported are 2-sided and p < 0. 05 was considered as statistically significant in all cases.
5 GeV  [46]. The ATLAS search based on 36 fb -1 of Run 2 data  [47]  sets upper limits on BðH → aa → bbμμÞ between 1. 2 × 10 -4 and 8. 4 × 10 -4 for a-boson masses in the range 20 ≤ m a ≤ 60 GeV. In this paper, the full Run 2 dataset corresponding to an integrated luminosity of 139 fb -1 is used and the search is extended down to m a ¼ 16 GeV and up to m a ¼ 62 GeV. Additionally, boosted decision tree (BDT) techniques are used to improve the separation of the signal from the SM backgrounds, increasing the analysis sensitivity, especially for higher m a.
III. DATASET AND SIMULATED EVENTS The data used in this analysis were collected in Run 2 of the LHC during the 2015-2018 data-taking period with pp collisions at a center-of-mass energy of ffiffi ffi s p ¼ 13 TeV. The dataset corresponds to an integrated luminosity of 139 fb -1. The lowest-threshold unprescaled single-muon and dimuon triggers are used to select the events  [52]. Single-muon triggers require the transverse momentum (p T ) of the muon to be above 20 or 26 GeV, depending on the data-taking period, while the dimuon trigger requires both muons to have a p T above 14 GeV. Simulated events are used in the estimation of the SM backgrounds. SHERPA 2. 2. 1  [53, 54]  was used as the baseline generator for the Drell-Yan ðDYÞ þ jets, Wð→ lνÞ þ jets, diboson and triboson backgrounds. It is a multiparton matrix element and parton shower (PS) generator including hadronization  [55] [56] [57] [58] [59] , with the NNPDF3.
Higgs boson production through gluon-gluon fusion (ggF) was generated using the NNLOPS program  [71, 72]  with Powheg-Box v2  [61, 63, 73, 74]. The vector-boson fusion (VBF) processes were generated with Powheg-Box v2 at NLO accuracy  [75]. The Higgs boson mass was set to 125 GeV. For both the ggF and VBF production processes, Powheg-Box was interfaced with PYTHIA 8. 212 using the AZNLO tune  [76]  for the simulation of the H → aa → bbμμ decays, where the a-boson is a pseudoscalar, as well as for parton showering, hadronization and the underlying event. The ggF Higgs boson production rate is normalized to the total cross section predicted at next-to-next-to-next-to-leadingorder accuracy in QCD with NLO electroweak corrections applied  [77] [78] [79] [80] [81]  and amounts to 48. 58 pb. The VBF production rate is normalized to an approximate NNLO cross section with the NLO electroweak corrections applied  [82] [83] [84] [85] , which amounts to 3.
2 around a muon be less than 15% and 30% of the muon p T , respectively. Jets are reconstructed using the anti-k t algorithm  [91]  implemented in the FastJet package  [92]  with a radius parameter of R ¼ 0. 4. The inputs to the jet clustering are built by combining the information from both the calorimeters and the ID using a particle-flow algorithm  [86, 93]. Jets with p T < 60 GeV originating from pileup are suppressed with the jet-vertex-tagger (JVT)  [94] , a multivariate algorithm combining track-based variables. Selected jets are required to have p T > 20 GeV and jηj < 2. 5. An algorithm (MV2c10) relying on multivariate techniques, taking as input the properties of displaced tracks and vertices reconstructed within a jet, is employed to identify (tag) jets containing b-hadrons  [95].
4 × 10 -5 (1. 9 × 10 -4 ) for m a ¼ 35 GeV (m a ¼ 52 GeV). The upper limits at 95% CL on BðH → aa → bbμμÞ range between 0. 2 × 10 -4 and 4. 0 × 10 -4 , depending on m a. These limits improve upon the previous ATLAS result based on 36 fb -1 of data  [47]  by a factor of 2-5 over the full m μμ range. A factor of ∼2 improvement in sensitivity comes from the larger dataset, and a further factor of ∼2 is achieved thanks to the use of multivariate techniques to discriminate between the signal and the SM backgrounds. Due to small number of background events at lower signal masses m a , the BDT training is less efficient in this region, and the gain from applying the BDTm a selection criteria is higher at higher m a. Taking as an example the favorable scenario with BðH → aa → bbμμÞ=BðH → aaÞ ¼ 0. 16%, the analysis probes the Higgs boson branching fraction into pseudoscalars down to BðH → aaÞ ¼ 1.
ACKNOWLEDGMENTS We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias ,  Colombia; MSMT CR ,  MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG , Germany; GSRI, Greece; RGC and  Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; JINR;  MES of Russia  and  NRC KI , Russian Federation; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
In addition, individual groups and members have received support from  BCKDF ,  CANARIE ,  Compute Canada  and  CRC ,  Canada ;  COST ,  ERC ,  ERDF ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex, Investissements d'Avenir Idex  and  ANR, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF, Greece; BSF-NSF  and GIF, Israel; Norwegian Financial Mechanism 2014-2021, Norway;  NCN  and  NAWA, Poland ;  La Caixa Banking Foundation ,  CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain ;  Göran Gustafssons Stiftelse , Sweden; The  Royal Society and Leverhulme Trust,  United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from  CERN , the ATLAS Tier-1 facilities at  TRIUMF (Canada) ,  NDGF (Denmark, Norway, Sweden) ,  CC-IN2P3  (France),  KIT/GridKA (Germany) ,  INFN-CNAF (Italy) ,  NL-T1  (Netherlands),  PIC (Spain) ,  ASGC (Taiwan) ,  RAL (UK)  and  BNL (USA) , the Tier-2 facilities worldwide and large non-WLCG resource providers.
Riparian forests are complex ecosystems shaped by their connectivity to a river system, which produces a mosaic of ages and species. Because of increasing anthropic pressure from factors such as damming or climate change, they are often endangered and suffer from a drop in groundwater accessibility and increased water stress. By combining hyperspectral, LiDAR, and forestry datasets along a 20 km corridor of the Ain River, this paper assesses the ability of remote sensing to characterize and monitor such environments. These datasets are used to investigate changes in site conditions and forest characteristics, such as height and canopy water content, along a gradient of ecosystem ages and for reaches under distinct geomorphic conditions (shifting, sediment-starved, incised). The data show that, over time, forest patches aggrade, and the forest grows and becomes more post-pioneer.
In contrast, LiDAR data provides three-dimensional information about both the vegetation cover and the topography of the reach under the canopy cover  [35, 43]. Therefore, it can be used to distinguish between the developmental stages of a specific species and help in studying vegetation growth  [28] , and it can contribute to the study of the interactions between channel topography and riparian vegetation  [34]. Recent research even showed that LiDAR allowed for discrimination between common riparian species along a corridor of the Selune River, France  [38]. Databases of topographic variables can be built for entire regions and be exploited to make quantitative assessments of human-driven channel changes at the regional level  [44, 45]. Although the acquisition of multiple LiDAR campaigns along long river corridors is costly, single campaigns have been used in conjunction with time-series acquisitions of aerial images to provide added information about channel topography  [34] [35] [36].
It has also been successfully paired with LiDAR imagery in forestry studies to classify species at the tree level  [53] , with some studies even featuring multi-date surveys targeted at phenological windows or flowering stages  [54] , or to contribute to forest inventories  [55]. In riparian environments, hyperspectral imagery with Landsat data has been used to provide in situ data for investigating the responses of pioneer species (such as poplars and willows) to groundwater decline following a drought event  [40]  and to investigate changes in competitor/stress tolerator/ruderal (CSR) strategies in hydrologically-altered reaches  [39]. These examples show the potential of hyperspectral imagery for investigating hydrogeomorphic feedback in riparian communities. Studies coupling hyperspectral data with LiDAR data in riparian environments are rare.
In addition, because of anthropic pressures or specific lithology, the geomorphic features of a river and its banks can vary along its length and lead to gradients of hydrological connectivity inside the forest (e. g. , more elevated riverbanks that are less frequently flooded, lack of lateral mobility). Both of these gradients can lead to changes in the characteristics of the riparian forest (e. g. , changes in species composition, forest growth, and sensitivity to water stress). Therefore, the aims of this study were to combine hyperspectral, LiDAR, and forestry data to: (1) Explore changing biophysical characteristics along age gradients. (2) Explore changing biophysical characteristics between river reaches with differing geomorphic features and hydrological connectivity. (3) Assess the use of random forest classifiers to predict forest connectivity in riparian forests.