dataesr/scientific-paragraphs-categorization huggingface dataset:
As an unbalanced training dataset can hinder the performance of supervised learning algorithms  [44] , we set an upper limit at 1000 segments per behaviour for the training dataset. These segments were randomly selected for the over-expressed categories at each training trial. For each trial, we evaluated the efficiency of the different methods by computing the number of wellidentified behaviours (true positive, TP, and true negative, TN) and of behaviours considered to be misclassified (false negative, FN, and false positive, FP) into a confusion matrix. We calculated three indicators for each behaviour: (i) 'Sensitivity' = TP/(TP + FN), also called true positive rate, hit rate or recall, measures the ability of a method to detect the target behaviour among other behaviours; (ii) 'Precision' = TP/(TP + FP), also called positive predictive value, measures the ability of a method to correctly identify the target behaviour; and (iii) 'Specificity' = TN/(TN + FP), also called selectivity or true negative rate, measures the ability of a method to avoid wrongly considering other behaviours as the target behaviour.
The best true positive rates, for the seven classifiers, were obtained in the 'Scratching' category despite its low occurrence in the dataset, meaning that this behaviour was relatively well identified when it occurred. The activity budget, representing the percentage of the total mean time allocated to each behavioural category, showed similar proportions between the predictions and the observations (figures  5  and 6 ). This result highlights the ability of our method and the WS model to predict the behaviours of immature green turtle in natural conditions. The main differences between the observed and predicted activity budgets were seen in the 'Resting' and 'Swimming' behaviours (figure  5  and 6 ). These differences were small and represented less than 3% of the total observed time (table  2 ).
This method allowed us to identify key behaviours such as feeding and scratching, which had previously been either misidentified or not identified at all due to the lack of discriminative signals in the raw data obtained from raw acceleration and/or gyroscopic data obtained with loggers fixed to the carapace of the turtle. Although our choice of derived signals makes our approach specific to sea turtles, this principle can be applied to numerous species if the different signals are considered with care before the study. When carrying out automatic behavioural identification from multi-sensor data using supervised learning algorithms, one of the main difficulties is the segmentation of the multi-sensor data to obtain homogeneous segments that are representative of the various behavioural categories.
This consists of splitting behaviours into groups based on signals that are easily interpretable in a dichotomic way (variables such as depth were used to attain this in our study). A change-point algorithm can be used to achieve a more specific segmentation based on other signals, with a possible ad hoc adjustment of the contrast required to evaluate whether two successive values do or do not belong to the same segment (such as the manual penalty of the PELT algorithm). In this paper, we demonstrate this approach for the green turtle (figure  3 , R-script in electronic supplementary material), but there is no reason it could not be easily adapted for other species. This will certainly necessitate the identification of the optimal hyper-parameters as well as the informative signals for the segmentation according to the species, but the approach of combining automated segmentation and machine learning methods with well-thought-out descriptive variables should apply as well.
201710-0005). Data accessibility. The R-script to visualize the raw acceleration, gyroscope and depth profile associated with the observed behaviours of the immature green turtles have been uploaded as part of the electronic supplementary material. The same is true for the R-script to automatically identify sea turtle behaviour from the labelled data. The datasets containing the acceleration, gyroscope and depth recordings of the 13 immature green turtles as well as their observed behaviours are available within the Dryad Digital Repository: https://doi. org/10. 5061/dryad. hhmgqnkd9 [76].
Acknowledgements. This study was carried out within the framework of the  Plan National d'Action Tortues Marines de Martinique et Guyane Française. The authors also appreciate the support of the  ANTIDOT  project ( Pépinière Interdisciplinaire Guyane, Mission pour l'Interdisciplinarité, CNRS ). The authors thank the  DEAL Martinique and Guyane , the  CNES , the  ODE Martinique , the  ONCFS Martinique and Guyane , the  ONEMA Martinique and Guyane , the  SMPE Martinique and Guyane , the  ONF Martinique , the  PNR Martinique , the  Surfrider Foundation ,  Carbet des Sciences, Plongée-Passion , the  Collège Cassien Sainte-Claire  and the  Collège Petit Manoir  for their technical support and field assistance. We are also grateful to the numerous volunteers and free divers for their participation in the field operations.
Modelling the mechanical behaviour of granular materials using the insight of physics, such as discrete element method (DEM), usually costs a lot of computing resources as a result of the storing and transferring of a large amount of particle and contact information. Unlike DEM, the micro-mechanical (MM) model, based on statistics of directional inter-particle contacts of a representative volume of an element, imposes a much lower computational demand while retaining granular physics. This paper presents such a kinematic hypothesis-based MM modelling framework, programmed by a dynamic coding language, Julia. The directional local law of a recently developed model is selected as an example of the implementation. The entire code of the MM model programmed by Julia is structured into several functions by which multilevel loops are called in an order.
Even though the µ GM model (developed by Pouragha and Wan  [38] ) could be derived based on both hypotheses, the static hypothesis is still recommended. Theoretically speaking, both could be used. The major difference between the two is that the derivation direction of the constitutive relationship is different. The kinematically based MM derives from strain to stress, whereas the statically based MM is the opposite. On the other hand, a convenient programming language also could help a lot. For scientific researchers, the Julia language  [3, 4]  is a perfect choice. The Julia language is an open source dynamic language publicly released in 2012. Hundreds of individuals have contributed to its development since its release. One of the main benefits to using Julia is its computational efficiency which exceeds other dynamic languages, such as MATLAB  [26]  or Python  [39].
It also exhibits equivalencies of static languages, such as FORTRAN  [30]  or C++  [43]. With the approximate computational efficiency of static languages and functions of dynamic languages, Julia is a unique combination of both coding classes, giving the user a high-level performance while still providing an interactive and constructive interface. Other advantages include its flexibility and open source availability. As a relatively new language, Julia has undergone significant changes between versions, with version 1. 0 released in late 2017. Multiple publications and advances in Julia have been made since its release in 2012. Unfortunately, no such MM model is available in Julia, which limits the high-efficiency analysis of boundary value problems, starting with grain scale. The aim of this paper is to answer two questions: why do we develop the MM model, and how do we program an MM model in Julia? The 3D-H model, as an example of an MM model, is first reviewed.
Flow chart This section shows the entire program of the 3D-H model implemented in the Julia language. As an example, the drained triaxial compression test with 200 kPa of confining pressure is considered. The program includes a main program (main. jl shown in Listing 1) and some subroutines (such as mixload. jl, hexagon. jl and so on). The flow chart shown in Fig. 6  can be illustrated in the following steps: 1. The main program main. jl (shown in Listing 1) first reads model parameters from the sample() function. Nt and Np are the numbers of angle subdivisions of θ and φ, respectively. Note that even if the initial states of all mesostructures are the same, their subsequent evolution might be different. Thus the dimension of microstate variable matrices (such as state_F, state_d and so on) needs to be consistent with the number of angle subdivisions.
Conclusions In this paper, the programming method of the MM model using Julia language, in the context of computational granular materials, was presented. The following conclusions can be drawn from the results and discussions: 1. The entire code of the 3D-H model was clearly formulated and programmed. Each function and object is expected to be easily understood by illustrating with a flow chart. 2. A global mixed method was presented for solving linearised mixed control constraint equations. The effectiveness of this method has been validated by simulating drained triaxial compression tests, in which the boundary conditions consisted of imposing the vertical strain and the lateral stresses. 3. Both the 3D-H and DEM models can reproduce the experimental results on Labenne sand, involving only a few parameters.
In    contrast to the DEM model, where all contact information is transferred and stored, the MM model runs faster using a multiscale approach. 4. The Julia language should be one of the most appropriate languages for programming the MM model because a lot of loops need to be calculated. The easier understanding of how objects are accessed and passed through functions in Julia allows some implementation techniques to be used to make further improvements to the program. The 3D-H model belongs to the family of MM models, which use the multiscale approach. This study could, therefore, provide guidance for similar multiscale approach programming.
This article proposes a QGIS plug-in called TempMap, to obtain near surface air temperature maps from an urban meteorological network, the topography description and three parameters describing the urbanized surface fraction. The objective being to facilitate the communication and consideration of local climate in French urban planning practices, some methodological choices were done related to the meteorological situation robustness, the choice of the mapped indicator and the availability of the surface data description. The plug-in allows to apply spatial analytical modelling based on a hybrid interpolation and is here applied to the CAPITOUL atmospheric database of Toulouse (France), the MApUCE urban database and the topographical information from the French National Geographical Institute to obtain air temperature maps for four Local Weather Types.
The input data come from field-based, mobile temperature measurements as well as land-use databases. Here, several methods are compared (kriging and cokriging) to produce temperature maps. The results demonstrate the fairly satisfactory accuracy of co-kriging methods (<0. 25°C), despite a margin of error that tends to increase quite strongly (above 1°C) as one moves away from the measurement survey path. It should be noted that  Bergeron (2014)  does not focus on factors related to the physical geography of space, such as terrain. The work of  Lapparent and al. (2015)  highlights precisely the influence of height on temperature. A regression-kriging method is also preferred here. A simple linear regression model is constructed using height as the predictor variable. Residues from this regression are then krigged and added to the regression result.
Some tools provide an estimation of the UHI based on derivative indicators as for example the Urban Heat Island Interactive Maps provided by the California Environmental Protection Agency 1 based on degree-hours per day on a Celsius scale, whether averaged (monthly or seasonally) or standardized, that are less intuitive than 1 https://calepa. ca. gov/climate/urban-heat-island-index-for-california/urban-heat-island-interactive-maps absolute air temperature values as the user can not make the experience of those atmospheric conditions. In past collaborations with urban planners we have seen that this point is very important. • The method to calculate the geostatistical model has to be reproducible by the GIS team within a French local authority or a design office. The method have been encapsulated into an open-source QGIS plug-in and the surface data used to calculate the relation between air temperature and urban surface is free of charge and available for most French cities and will be available for the whole territory soon in the framework of the PAENDORA project 2.
Case study The QGIS plug-in was developed and tested through a partnership between the research team, the metropolitan government of Toulouse (France) and their Urban Planning Agency in the framework of the national funded research project MApUCE: "Applied Modelling and Urban Planning: Urban climate and Energy". In France, the stakeholders' interest in this kind of tool is explained by two factors. Firstly, recent developments in French environmental law, following European directives and international treaties, oblige from 2012 every French city 3  over 50,000 inhabitants (20,000 from 2015) to implement a Territorial Climate and Energy Plan addressing mitigation and adaptation actions; for this reason, spatial urban climate information is currently in demand by public authorities. Secondly, the development of lightweight atmospheric sensors, as well as more extensive dissemination of collaborative data, make such atmospheric data more easily available for cites.
Climatic Dataset As Toulouse Metropole's network of 80 meteorological stations remains in development at the time of this writing, data from a 27 stations network set, between February 2004 and March 2005 and during the CAPITOUL campaign  (Masson et al. , 2008) , has been used to test the tool. The zone of study, illustrated in Figure  1 , includes nearly half of the Toulouse metropolitan area, and is mainly centered on the municipality of Toulouse, with a surface area of 300 km². The ground station network in CAPITOUL consisted of 21 light stations, measuring air temperature and relative humidity at 12-minute sampling intervals, situated in the city center and in the suburban areas of Toulouse. Five permanent ground stations of Météo-France were also available, which measured horizontal wind velocities at a height of 10m.
Two of these towers, one 15 km southwest of Toulouse (Le Fauga, situated in a grassland area) and another 40 km northwest (Saint-Sardos, situated in an irrigated cornfield), measured solar radiation components and turbulent fluxes with a 30-minute sampling interval. Finally, a pneumatic tower situated in the old heart of Toulouse was considered the reference station when describing the properties of the city center. This central 30m tower was situated on a building's rooftop, itself located 20m above the ground. Temperature, relative humidity, wind speed, pressure, upward and downward global short-and long-wave radiation, and turbulent flux measurements were recorded at three different heights  (47. 5m, 38. 5m, and 26m  above the ground). Depending on expected wind gusts, the mast height was modified, and four positions are thus defined in the dataset (positions 1 to 4, from low to heavy winds respectively).
Topography and urbanized surface fraction data Urbanized surface fraction, here called "City Fraction", was here calculated from the MApUCE morphological database  (Bocher et al. , 2018). This urban database contains a set of indicators, computed at three urban scales -corresponding to building, block (group of contiguous buildings), and islet (typically a group of buildings surrounded by a road), herein referred to as Reference Spatial Units (RSU) -and based on input datasets provided by French Institutes. The database, is currently available for a set of forty cities and will be available for the whole French metropolitan territory at the end of 2020, it can be consulted at http://mapuce. orbisgis. org/, and is presented as a polygon vector layer with 40 variables relating to the structure and the occupation of the urban surface (buildings density, roads, vegetation, buildings average height, etc.
Software Two GIS applications have been used to primarily process data: SAGA GIS (v 2. 3. 1) and Quantum GIS (v 2. 14. 12 LRT, www. QGIS. org). QGIS is a popular open-source and freely available GIS software tool. It was chosen because it offers platform independence (Linux, Unix, Mac OS, and Windows) and a "plug-in" system for extending software functionality. It has an active user base, with development communities worldwide. Those aspects justified the choice of QGIS regarding private label GIS software. The spatialization method has been mainly developed with SAGA GIS, which has been chosen for its wide range of geostatistical algorithms  (Conrad et al. , 2015). QGIS includes a library with SAGA GIS algorithms, which allows users to reproduce the method directly from QGIS. The method has been thus automated and encapsulated in a QGIS plug-in coded in Python, with a user interface designed on Qt Designer (version 2.
QGIS plug-in architecture A Python script automated this process, which was then encapsulated in a QGIS plug-in with a user interface designed with Qt Designer. This plug-in, called TempMap, aims to facilitate the use of the method by local authorities, engineering consulting firms or researchers. The interface and configuration of the tabs have been designed in two languages, French and English. The user is assisted by a help button adjacent to each of the input parameters. There is a "Help" tab that details the methodology. It is possible to modify the output resolution of the rasters that are produced, in order to fit the input data resolution and to satisfy the needs of the study, using the "Parameters" tab. The last tab contains information about authors and context of development ("About").
Configuration tab contain all the slots allowing to choose input data and outputs saving paths. When the plugin is launched ("OK" button), it applies the following process on input data, and then the RK method previously described. At first, clip raster tool is used from the GDAL library and produce a DEM model clipped by the extent of a 100m buffer around stations Capitoul layer, (DEM_CLIP). This new raster is resampled to a 100 m resolution (RESAMPLED_DEM_CLIP). Then, with the MApUCE Database, city fraction is computed in two steps: firstly, the three fields needed to create city fraction are rasterized using the GDAL library tools. Secondly, the three rasters are added with Figure  6. Architecture and process of the TempMap Plug-in.
Applying the plug-in to a dataset The plug-in can be download on GitHub and installed manually. It will be soon available in the Official QGIS Repository. Currently, the plug-in is compatible with all QGIS versions since 2. 14. 12. Here the French urban and topographical datasets are used as examples, but alternative datasets can also be used. The duration of the process may vary quite widely, depending on QGIS version, computer configuration and the number of input elements. For the 27 meteorological stations processed in our examples, the duration to model one time step varied between 22 seconds, and 47 seconds using standard laptops with RAM of 8 Go and 3 Go respectively.
Model evaluation The cross-validation method is often used to check the robustness of the statistical models. However, the small number of meteorological stations available for this study, limits the use of this method to effectively validate the resulting models, even if those stations provided an excellent dataset of air temperature measurements. Quality evaluation is here performed in three steps. First through the R bar squared of the multiple linear model, R 2 adj  (Hengl et al. , 2007). Then, the analyse of the R 2 adj of the final model after the residuals calibration by kriging. Finally, the quality of the map is assessed by comparing observed values with estimated values at each station  (Hengl, 2009). For the last, the assessment is done both for observed values and predicted values by stations and by urban zones.
Conclusions This paper presents a method to map near surface air temperature from point-based readings in urban environments. The air temperature is here statistically correlated to the land use and the topography through a regression-kriging, a combination of a linear regression and a kriging on residuals. Here freely available French databases of wide coverage at the national level are used, but the method can also be applied to alternative urban and topographical databases. More than the statistical method used itself the innovative character of this work is to use the Local Weather Types approach developed by  Hidalgo and Jougla (2018)   The QGIS plug-in was developed and tested through a partnership between the research team, the metropolitan government of Toulouse (France) and their Urban Planing Agency and responds to different types of constraints: • The meteorological situation represented in the mapping should be as intuitive as possible in order to not only be understood by the technical level but also explained by these to the political one during internal meetings.
• The level of technicality must be accessible to the GIS team of a French local authority or an engineering office. For that, as said before, a balanced choice on the input variables and software was done. The process is automated in a Python script, encapsulated as a QGIS plug-in, and made freely available to everyone. Maps presented in this paper have already be involved in several operational applications. For Environmental Urban Observatory platform that will probably contain this method or an adapted one, to make available to the public in real time the air temperature from their newly installed meteorological network. To end we would like to point some specificities relevant for the resulting maps analysis. First, even if the horizontal resolution is relatively high (100 m, here fixed by the DEM), the temperature information should not be interpreted at the pixel scale but rather at a higher spatial scale as the neighborhood one.
Acknowledgements The authors wish to thank  Mme. Kounkou-Arnaud  from  Météo-France  for the interesting exchanges at the early steps of this work. Likewise, the technical staff of the environment and urban regulation departments, as well as the urban planning agency, for their useful input to this study. This work was funded by the  French National Research Agency (Agence Nationale de la Recherche)  under grant # ANR-13-VBDU-004.
Blood/saliva and urine samples have been collected for genetic, epigenetic and toxicological (heavy metals, volatile organic compounds) studies. Structural (T1, T2, DTI) and functional (resting state fMRI) MRI brain scans have been performed on approximately 15% of the individuals. All data and biological samples are maintained in a databank and biobank, respectively. Discussion: The cVEDA has established the largest neurodevelopmental database in India, comparable to global datasets, with detailed environmental characterization. This should permit identification of environmental and genetic vulnerabilities to psychopathology within a developmental framework. Neuroimaging and neuropsychological data from this study are already yielding insights on brain growth and maturation patterns.