dataesr/scientific-paragraphs-categorization huggingface dataset:
5. This process also requires the knowledge of the full selection policy. These bounds have been implemented in the R package SansSouci, which is available from https://pneuvial. github. io/sanssouci/. The coverage of these bounds is evaluated via extensive numerical experiments in Section 4, which typically reflect the impact of the estimation error of Γ in the different bounds. Our conclusion is that while the existing approaches, ignoring the latent HMM structure, are over-pessimistic, the plug-in approach can be slightly over-optimistic. A good trade-off is provided by bootstrap-based strategies, which ensure a correct coverage while taking full advantage of the HMM structure.
Semi-simulated data based on DNA copy numbers In order to test the robustness of our methodology we have considered a more realistic scenario, which does not rely on a probabilistic simulation model. Using the R package jointseg (Pierre-Jean et al. , 2019), we have generated synthetic copy number profiles as proposed (and further described) in Pierre-Jean et al. (  2015 ): given a number m of loci and a number K of regions, we draw uniformly K -1 breakpoint positions, thus defining K regions. Then, we draw K region labels, corresponding to the number of DNA copies for each parent (a. k. a. parental CN). Finally, for a region of size m k , we draw m k samples by resampling from a real DNA copy number data corresponding to this type of region. These data are available in the R package acnr (Pierre-Jean and Neuvial, 2017), which contain annotated CN profiles from several cancer data sets.
Influenza-like illness (ILI) We apply the proposed method to the weekly incidence rates of influenza-like illness (ILI). This data were collected from the Sentinelles Network, a national surveillance system in France. Sun and Cai (2009)  studied this data set between January 1985 and February 2008, to be comparable we will restrict ourselves to this period. They stated that the incidence rates can be classified into one of the two categories: aberration or usual. The usual is the null states and the aberration is the alternatives one. The weekly ILI incidence rates are standardized according to the sizes of the underlying population and the representativeness of the participating physicians. Sun and Cai (2009)  also applied a log transformation to reduce the skewness of the original data, hence so will we.
Acknowledgements This work has been supported by  ANR-16-CE40-0019  ( SansSouci ),  ANR-17-CE40-0001  ( BASICS ),  ANR-19-CHIA-0021-01  ( BiSCottE ), the  UPSaclay Excellency Chair   REC-2019-044 , and by the  GDR ISIS  through the  "projets exploratoires" program  (project  TASTY ).
Author contributions 5 Acknowledgements V. C. was supported by Institut national de recherche en informatique et en automatique (Inria) Paris. D. P. was supported by Sorbonne Alliance University with an Emergence project MATHREGEN, grant no. S29-05Z101 and by Agence Nationale de la Recherche (ANR) under the project grant number ANR-22-CE45-0024-01. OC was funded by grants PICT-2017-2307 and PICT-2019-03828) from the Agencia Nacional de Promoción Científica y Tecnológica of Argentina. The authors sincerely thank Alberto Ceccarelli, Leo Otsuki, Sophie Hecht and Fabian Rost for their careful reading of the manuscript and their excellent suggestions, as well as Elly Tanaka for incessant and stimulating discussions. 6 Declaration of interest 7 Computational methods.
Coupling a combinatorial method to data mining tools is an example of a suitable strategy  [19]. The broad availability of open access, coherent and good quality datasets (i. e. containing both outstanding and average results with the associated measurement error) is key to the successful development of combinatorial design. The development of Machine Learning (ML) in the context of materials science has led to accelerated and enhanced material designs. ML models have been used on data gathered from literature alone  [18] , or as a guide to generate high-throughput experimental data  [20] [21] [22] , or from CALPHAD (CALculation of PHAse Diagrams) data -on HEA amongst others  [23]. These previous studies have shown a good ability to describe and predict the properties of the studied materials.
Properties characterization Samples are discretized by 4 mm steps along the gradients to be systematically characterized. Property characterization of asgrown thin films focuses on finding a composition leading to an alloy both strong and ductile, an antagonistic combination whose optimization is critical for structural materials  [51]. Composition, crystallinity and mechanical properties were assessed to build the alloy libraries, by EDX (energy-dispersive X-ray spectroscopy), EBSD (electron backscatter diffraction), XRD (X-ray diffraction) and nanoindentation. Composition evaluation was performed with a field emission gun (FEG) ZEISS Gemini scanning electron microscope SEM 500 equipped with a silicon drift detector (SDD) (EDAX OCTANE ELITE 25 Si 3 N 4 windows-60 mm 2 ). EDX measurements were performed and analyzed with the EDAX TEAM software.
The acquisition was done on a 10 lm 2 zone, during 30 s, at 20 kV, once per discrete composition. All subsequent characterizations were performed in directly adjacent areas. An EBSD map was obtained once for each discrete composition on a 10 lm 2 zone, with a 7 nm step, on a FEG ZEISS Gemini SEM 500, with a charge coupled device (CCD) detector (EDAX HIKARI SUPER) and OIM software. Image Quality (IQ) and Confidence Index (CI) were extracted in a. csv file by an automated treatment using MTEX Matlab toolbox  [52]. EBSD CI and IQ were then associated to each composition. Mechanical properties were acquired through five 700 nm deep indents per composition (5x1 matrix perpendicular to gradient axis, each separated by 20 lm), using a Berkovitch tip on a Nanoindenter MTS-XP machine with continuous stiffness measurement at a constant strain rate d dt = 0.
Dataset construction For each characterization, relevant data should be added to the dataset. In order to improve dataset quality on nanoindentation replications, statistical processing is applied. Due to a surface detection failure or tip pollution, computed Young's modulus and hardness can be biased and bear no physical meaning: all compositions with both an H value under 2 GPa and an E value under 10 GPa are removed from the dataset. Then, for each composition, possible outliers are automatically identified and extracted from the threshold replications, using Dixon Q-test  [55]  on the minimum and on the maximum value of hardness and modulus (independently).
The entire composition domains can be visualized at once and explored through an interactive representation. Finally, linear and planar gradients can also be visualized without deformation (more details are given in Supplementary Notes 4). The overall approach detailed in this method is summarized in Fig. 1 : a) Linear or planar gradients are selected, in a quaternary system for instance, passing at least once through SCD points; b) Linear gradients being deposited as thin films by confocal magnetron sputtering; c) Characterization of linear gradients by EDX, nanoindentation, XRD and EBSD. Each 10 cm gradient is discretized into approximately 22 zones; d) From this raw experimental library, datasets are built; e) Four different Machine Learning model families are trained and the best one enables the identification of compositions yielding optimized properties.
812, with a maximum near the niobium vertex. Ductility values predicted in the next section will be calculated from predicted hardness and Young's modulus and not from the data computed here. Fig. 6. a and d. allow observing a possible correlation between microstructure and mechanical properties: the highest hardness values are observed on CrMo to Zr gradient, near CrMo binary, before a clear break. This abrupt change corresponds to a crystalline to amorphous transition on the EBSD phase representation, which is a hint of the well-known dependency of the mechanical properties from both microstructure and composition.
20-gradient dataset Four families of models were optimized, trained and evaluated. All the results are given in Supplementary Notes 6. In this paper, we will focus on neural networks predictions, trained with EBSD and XRD classes and with mechanical properties dataset without outliers. These models are the most predictive and they do not show any obvious artifacts. Properties are predicted over the entire composition, with a 2 %at step for each element. XRD-and EBSD-predicted amorphous or nanocrystalline compositions hull are represented in Fig. 7. a. Neural Network predictions from EBSD mapping leads to a larger amorphous domain than the domain predicted from XRD, as expected. However, these domains overlap. In the following discussion, definition of amorphous compositions will be based on mathematical intersection of these two domains: compositions are considered as amorphous if both EBSD-based predictions and XRD-based predictions give them as such, and as crystalline if EBSD-prediction or XRDprediction gives them as such.
Thus defined, phase prediction still does not return two clearly delineated structure domains. A closer look at the distribution of the compositions predicted classes (see Supplementary Figure  41 ) reveals three different regions: one in which all compositions are crystalline, one in which all compositions are amorphous, and a transition zone in which neighboring compositions are either. The hulls of all three domains are plotted in Fig. 7. b. XRD dataset is overlaid on the predicted hulls on Fig. 7. b. which shows a good matching between prediction and data. However, the isolated circled (in red) experimental amorphous/nanocrystalline composition is outside of the corresponding hull. This shows that the model is not overfitting the data and is thus quite generalizable. It is interesting to note that the binary classification modeled by Machine Learning allows us to reconstruct a fairly clear transition zone as observed experimentally.
The same observations stand for other model-dataset couples (Supplementary Figure  42 ), however all Pareto optimal compositions are not exactly the same from one model to another. Moreover, for all kinds of model, the error can be plotted depending on data repartition (see Supplementary Notes 5 for detailed computation). It appears that the error increases while approaching binary edges, which is expected as they were not screened in details on purpose. Predictivity of the model will thus be evaluated on validation sets, chosen along bina-ries where prediction of properties are the worse. Here, three binaries were chosen to evaluate the model predictivity on the border: Nb-Ti, Zr-Mo and Cr-Nb. This choice is detailed in Supplementary Notes 7.
Validation of neural networks for hardness and ductility index prediction. Hardness, ductility index and XRD phase class of initial and validation datasets are plotted in Fig. 10  and show a good consistency with preceding experimental data. Predictions of neural networks trained with the initial 20gradient dataset can be compared to experimental results of the validation datasets (Supplementary Figure  17 ). While Nb-Cr properties are quite accurately predicted, prediction on Zr-Mo et Nb-Ti properties are less accurate. It shows that the method requires to be iterated in order to increase the model accuracy on the seeming zones of interest.
Method iterations in the amorphous/nanocrystalline domain Each binary sample is added one by one to the original 20gradient dataset, after outliers were removed by a Dixon test. The neural networks models are trained again with these new datasets, new predictions are performed and Pareto optimal compositions are identified. The evolution of the Pareto compositions is represented in Fig. 11  (for amorphous zone). It shows that the optimal compositions identified from modeled properties are modified by adding new data in the dataset, for neural network models but also for the rest of the models (see Supplementary Figure  43 ). A converged optimum based on Pareto front is identified throughout the iterations: Zr 2-4 Nb 0-4 Mo 0-4 Ti 18-22 Cr 72-74 is for instance a good compromise for a hardness of 11 to 13 GPa and a ductility index of 0.
However, considering ML as a mere standalone tool for data mining of disparate pre-existing experimental data is insufficient to reveal its full potential in the ICME (integrated computational materials engineering) approach. Instead, it was shown here that ML models give a more favorable outcome with training datasets tailored to their operating principles. This need for ML-oriented data can be fulfilled with SCD-based high-throughput experiments, which result in the rapid and automated acquisition of evenly distributed data and their associated error. It is worth stressing the importance of making such data open source, so that it can readily benefit all ulterior ML studies. The dataset proposed here is meant to integrate with other materials science databases. It is intended be supplemented with further elements, alloys and processing parameters.
It directly illustrated here that, depending on the target properties, the optima can lie away from the center of the composition space as well as from equimolar points. Thus, it seems that increasing the complexity of concentrated alloys will show diminishing returns. Using a rational approach to optimize alloys of moderate complexity could prove more favorable. The method proposed here can easily be transposed for ternary and quaternary systems. Furthermore, the data produced could also allow focusing on simpler subsystems from a complex dataset, which constitutes a supplementary asset. The second is, on a similar note, connected to the recyclability of complex alloys. The efficiency gain offered by high-performance materials is only desirable if they can sustainably be used and reused. It was shown that achieving maximum performance might require fine compositional tuning, which, in turn, would mean reduced recyclability.
Acknowledgements We acknowledge the  IDRIS  and  GRICAD  for computational resources, and the network  MIAI@Grenoble Alpes  (French  ANR-19-P3IA-0003 ) for fruitful discussions, in particular with  Rémi Molinier  and  Emilie Devijver. The authors also thank the members of the  Consortium des Moyens Technologiques Communs (CMTC) ,  Rachel Martin  and  Frédéric Charlot  as well as the  Centre of Excellence of Multifunctional Architectured Materials (CEMAM , French  ANR-10-LABX-44-01 ) for characterization support, and  Francis Baillet  for his help on Mixture Designs. The authors have no funding to declare.
Data availability Datasets and Machine Learning trained models are available in a Zenodo repository: Accelerated exploration of multinary systems | Zenodo. All Python and Matlab modules are available in the GitLab https://gricad-gitlab. univ-grenoble-alpes. fr/garele/acceleratedexploration-of-multinary. git. A Ph. D. thesis covering more details on this study and complementary analysis is also available for reading https://cnrs. hal. science/tel-04043261v1.
The information that can be retrieved from these data provides a snapshot of the livestock mobility network at each period of the year and could be used to develop tools to improve the surveillance system, adapted to the period concerned. Network-based approaches are widely used in veterinary epidemiology to study the role of animal mobility in the spread of diseases, with the aim of developing effective strategies for disease surveillance and control  [11, 12]. Networkbased approaches make it possible to depict livestock movements as a spatial network in which the nodes represent villages, administrative units, markets, or herds, and a link is created each time at least one animal is moved from one node to another. However, while network methods have been extensively applied to engineer surveillance system in European countries, thanks to the existence of vast live animal movement traceability datasets (i.
The influence of temporality on the structure of the network can significantly affect the spread of a disease, which consequently can only be accurately predicted if the chronology of links is accurately represented  [20, 21]. In this work, we used a temporal network approach to assess the influence of structural changes (i. e. , creation and disappearance of nodes and links) on the diffusion of animal diseases over time. We used data collected in 2020 by the Senegalese Veterinary Services to build a representation of the network, and adapted tools from complex networks to assess the risk of being infected and the role of different Senegalese areas in spreading infections over the course of the year. To this end, we relied on measures of the vulnerability and reachability of nodes. Vulnerability gives an indication of the likelihood a node will be infected, while reachability gives an indication of the time to infection.
Descriptive and Network Analyses. We analyzed mobility data using a complex network approach. LPS data were used to build three oriented and weighted networks, one for each species plus one species-independent network: the nodes corresponded to the departments of origin and destination; a direct link existed between two nodes if at least one animal was moved from the department of origin to the destination department; the link was weighted according to the number of animals moved along it. A cluster analysis was performed to explore the behavior of the different nodes over the study period. Due to the large variations on the number of animals exchanged, nodes were ranked based on their activity, defined as the effective number of animals traded each month; the number being positive if the inflow of animals was greater than the outflow (importing behavior), otherwise negative (exporting behavior).
Because of our focus on the control of transboundary animal diseases, the departments of Mali and Mauritania, which export livestock directly to Senegal, were chosen as sources of the disease, as the majority of Senegalese imports of small ruminants and cattle are from these two countries  [3, 8]. To explore the effect of temporality on the structure of the network, and hence on diffusion of the disease, we compared results obtained with a static representation (in which the structure of the network remains unchanged throughout the year) with results obtained with a temporal representation. In the first case, all the links recorded in the dataset were present at the same time, the time of activation was not taken into account, while in the second case, we included changes in the structure in every week of the study period.
For static networks, the speed/rate of infection was estimated from the length of the shortest paths, converting the links into temporal units, specifically weeks. If a node was reached by more than one source, the shortest infection time (for temporal networks) or the shortest path (for static networks) was chosen. All descriptive analyses and static/temporal network analyses were carried out using R software with the following packages: ggplot2 for graphs  [34] , ggplot2 and tmap for maps  [35] , FactoMineR  [36]  and factoextra  [37]  for cluster analysis, and sna  [38]  and tsna  [32]  for static and temporal network analyses, respectively. A freely available version for the deterministic SI model is available on gitlab using the command git clone git@forgemia. inra. fr:umr-astre/sentinel_nodes.
Results 4. 1. Summary Statistics. The database contained information on 8,861 livestock trade movements from January to December 2020. The network is composed of a total of 88 nodes, corresponding to an administrative unit of level 2, of which 45 are Senegalese (departments) and 590 unique links, i. e. , origin-destination combinations. The movements concerned Senegal as the origin and/or destination of 87% of the movements and eight other countries: Mali (9%), Gambia (2%), Mauritania (1%), Guinea, Guinea-Bissau, Burkina Faso, Niger, and Nigeria (<1% each) as either the origin or as the destination of movements. Focusing on Senegal, a total of 6,511 national movements and 2,350 international movements, over, respectively, 458 and 132 unique links, involving 87,017 cattle and 553,718 small ruminants, were recorded in the dataset.
Independently of the species involved and the network representation (temporal or static), the department of Kanel never gets infected because no movements have been recorded to this area in our dataset. In general, in all three networks, maps representing aggregate networks strongly overestimated both the quantity of potentially infected nodes and the earliness of infection compared to those of temporal networks. In addition, there is a difference in the potential sanitary risk between the cattle temporal network and small ruminants temporal network, and the latter showing an average wider and potentially greater disease propagation. However, the combined network of cattle and small ruminants (the livestock network) is under the greatest sanitary risk. Figure  7  also shows that, particularly for the livestock network and the small ruminants network, the periods around religious festivals (weeks 30 and 31 for the Tabaski, and weeks 40 and 41 for the Grand Magal of Touba) are characterized by a large number of infected departments, some of which are infected early.
Future works should, thus, also consider stochastic models like Kim et al. [39]  for specific diseases. In the model we used here, we aggregated data at the spatial scale of an administrative department, based on the assumption that the diffusion within a department is homogeneous. In practice, the presence of markets or transhumance corridors could attract movements in specific parts of the department, thereby increasing the risk in certain locations over the risk in other parts of the same department. Data on movements within departments were rare in our dataset (because of the way data were collected) and further field studies are recommended to collect data at a finer scale.
Acknowledgments The authors are grateful to  Dr Mbargou Lô , Head of the  Veterinary Services Directorate , and  Dr Mathioro   Fall, Head of Animal Health Protection Division. The authors acknowledge the support of  Yves Amevoin ,  Alioune Ka ,  Fallou Niakh , and  Khady Ndiaye , and all those who assisted in the LPS collection. We thank all the donors who supported the  CGIAR Livestock research program  through their contributions to the  CGIAR  trust fund. This study was partially funded by the  Project Eco-PPR (European Commission  through the  International Fund for Agricultural Development  (grant number  2000002577 ) and the  CGIAR Livestock research program ,  RVF OIE twinning program  ( CIRAD-ISRA ) granted through the  EBO-SURSY  project ( European Union   FOOD/2016/379-660 ).
Therefore, the aim of this work is to reduce uncertainties in current bottom-up estimates of CH 4 emissions as well as soil oxidation by implementing an inverse modelling approach in order to better quantify CH 4 sources and sinks for the most recent years (2008 to 2019). More precisely, the objective is to detect occurring trends in the CH 4 emissions and potential changes in seasonal emission patterns. The implementation of the inversion included footprint simulations obtained with the atmospheric transport model FLEXPART (FLEXible PARTicle dispersion model), various emission estimates from inventories and land surface models, and data on atmospheric CH 4 concentrations from 41 surface observation sites in the Arctic nations. The results of the inversion showed that the majority of the CH 4 sources currently present in high northern latitudes are poorly constrained by the existing observation network.