dataesr/scientific-paragraphs-categorization huggingface dataset:
(Fig. 5 ). The holes that are drilled into each section are also shown on the map along with the corresponding unique hole ID number. The map was used in conjunction with the lookup table mentioned previously. To create these "virtual" organs, the contours of the organs were manually drawn on each section in 3DSlicer. The volume corresponding to the organ was then created by the software by extrapolating between the different sections previously outlined. Next the geometric shape of the organs was adjusted manually using an anatomical atlas, to give them a shape closer to reality. The ATOM CIRS ® dummy represents a man (1. 73 m; 73 kg) whose dimensions are very close to these of the ICRP 110 reference male voxel phantom (1. 76 m; 73 kg). Therefore, vertical boxes made up of water: 25 cm × 25 cm × 15 cm and 30 cm × 30 cm × 15 cm.
 Validation step 1: comparison with the MCNPX calculation code  Dose comparison A summary of the results for the different configurations is shown in Table  4. For each geometry, the mean relative difference between SEED and MCNPX calculated from all the calculated absorbed doses assessed for the considered geometry is given. In addition, the mean relative difference was also estimated for the 4 simple (87 absorbed doses assessed) and the 4 complex (576 absorbed doses assessed) geometries and for all the 663 calculated absorbed doses (simple + complex geometries). Regarding the eight geometries, the mean relative difference ranges from 0. 40% (geometry 2) to 2. 83% (geometry 8). The overall mean relative dose difference between the two calculation codes is around 2%. Differences are a bit larger for complex geometries (2.
As the relative dose difference is directly linked to statistical uncertainty, it can be assumed that simulating a much larger number of particles (unreasonable in terms of calculation time for a real application of the SEED tool) would have made it possible to reduce significantly the average relative dose difference for "complex" geometries. This study has revealed that modeling was simpler and faster with SEED than with MCNPX, in particular if the time of preparing the input file is also included. This observation is not surprising, because SEED is a dedicated software that allows modeling the majority of the scene only with the mouse, thanks to the graphic interface that facilitates the 3D vision of the scene whereas MCNPX is a generalist Monte-Carlo code. For MCNPX, the input file must be written entirely on the keyboard and viewing in 2D only makes the validation of the geometry more difficult.
For example, the construction time of geometry 8 required more than 7 hours on MCNPX against 3 hours for SEED. However, if the geometry had been directly constructed in Geant4 (that means without any dedicated GUI), it would probably have taken as long as with MCNPX. This tool therefore shows great potential regarding this optimization of modeling time. In addition, modeling assistance features had not yet been implemented at the time of validation step 1 (copy and paste, object grouping, etc. ). These tools help to increase time savings in modelling. This validation step 1 made it possible to validate all of SEED's functionalities, from modelling the scene to reading the results of the simulations. Several methods for facilitating the readout and display of the obtained results are under development: direct access to SEED output files, tooltips on dose layers by positioning the mouse pointer on the volume or voxel concerned, digital tables integrated into the software and printable.
CONCLUSION AND PERPECTIVES In the case of an accident involving a large number of victims, it is necessary to perform a rapid dosimetric triage according to the severity of the exposures, in order to organize the treatment as well as possible. Recent scientific advances have made it possible to help carry out this sorting, but the numerical simulation had not yet been investigated. SEED is a dedicated tool for numerical dosimetric reconstruction. The two validation studies made it possible to test the SEED software and to confirm the logic of the software and the ergonomics of the interface. For gamma sources, it was possible to ensure the adequacy of the results of this software based on Geant4 with the reference code (MCNPX) on the one hand, and with experimental dosimetry by thermoluminescence on the other hand.
ACKNOWLEDGMENTS The authors would like to thank:  The French defense procurement agency (DGA) and its defence innovation agency (AID) for the initial financing of the project.  The company "SymAlgo Technologies" for the development of the GUI and for the fruitful discussions regarding SEED features.  The company "Institut de Soudure Group" for having supplied the radioactive source and for having provided their assistance in carrying out the experiment in good safety conditions.  The company "Polatom" for having communicated to us the precise technical parameters of the radioactive source.  The French National Gendarmerie for having helped our team in organizing the gamma irradiation on its military training field.  Dr Jocelyne Aigueperse for having initiated the collaboration between IRSN and SPRA which was at the origin of this new dosimetric tool.
Approval was obtained from the Ethics Committee for sport science research (CERSTAPS; IRB00012476-2022-15-03-166). The sample size was calculated a priori using G*Power (version 3. 1. 9. 6, free software available at https://www. psychologie. hhu. de/ arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower. html (accessed on 10 October 2019)) with the following criteria: effect size of 0. 35, power of 0. 8, probability error of 0. 05 with the main outcome being the root mean square. A sample size of 16 individuals was indicated.
Data Recording, Preprocessing and Initial Analyses Torque and EMG activity of the long head of BF and ST muscles were recorded using a Biopac MP150 system and associated software (AcqKnowledge 4. 2 for MP systems, Biopac System, Santa Barbara, CA, USA) at a 1000 Hz sampling frequency. EMG used two pairs of silver chloride surface electrodes placed over muscle bellies according to SENIAM recommendations. The interelectrode distance was 2 cm (center to center). The reference electrode was fixed to the right patella. Low impedance (<5 kW) of the skin-electrode interface was obtained by shaving, abrading with sandpaper and cleansing with alcohol. The EMG signals were amplified with a bandwidth frequency ranging from 10 to 500 Hz (common mode rejection ratio = 110 dB, gain = 500). Analyses of the maximal voluntary contractions started with a manual identification of the maximal voluntary torque value and timepoint using AcqKnowledge software (AcqKnowledge 4.
2 for MP systems, Biopac System, Santa Barbara, CA, USA). The best contraction, as witnessed by the maximal voluntary torque, was only retained for subsequent analyses. Then, torque and raw EMG signals were extracted over a 1 s time window being 0. 5 s apart the so-identified maximal voluntary torque. Torque and EMG were also extracted using 1 s windows within the sub-maximal voluntary contraction plateau (i. e. , between seconds 3 and 4). EMG raw data were band-pass filtered (20-300 Hz, 4th order) and root mean square (RMS) was calculated over 250 ms sliding windows using the "biosignalEMG" R package  [33]. The maximal voluntary torque and corresponding maximal RMS values were calculated. The mean torque and RMS values were calculated during sub-maximal voluntary contractions. An example of the EMG signal processing during maximal and submaximal contraction is shown in Figure  2.
Sample Entropy SampEn is often used as index of physiological complexity  [28, 34]. For instance, complexity (and SampEn) has been shown to be greater in concentric contractions and to decrease with fatigue  [30]. SampEn was calculated using the "TSEntropies" R package  [35]. To be computed, EMG time series x i was first embedded in a delayed m-dimensional space. The probability B m (r) that two sequences match for m points was computed by counting the average number of vector pairs within a tolerance of r. The embedding dimension m was set to length 2 and tolerance r to 0. 2 multiplied by the SD of the original EMG time series  [30]. This process was repeated for an embedding dimension of m + 1 to obtain the probability A m (r). SampEn was the negative logarithm of the ratio between these two probabilities: SampEn (m, r) = ln(A m (r)/B m (r)) (1) nalEMG" R package  [33].
A recurrence occurs when the distance between y i (d) and y j (d) is smaller than a threshold radius r. The recurrence is shown with a black dot. Authors  [41]  indicated that fine changes in surface EMG can be detected by the percentage of recurrence (%Rec) and percentage of determinism (%Det). %Rec quantifies the density of the recurrent points and reflects the current state of the system. %Det quantifies the ratio of the recurrence points that form diagonals higher than a minimal length (l min ) and reflects the amount of rule-obeying structure in the signal dynamic. A deterministic system produces longer recurrence diagonals and fewer isolated points than uncorrelated random time series. RQA and parameters' optimization were achieved using "crqa" R package  [42]. In the present study, the l min was set at 3 points, the radius r was fixed at 25% of the maximum distance to limit too high %Rec  [37].
Experimental Results We have implemented our complete approach in a tool, called SMPT (for Satisability Modulo P/T Nets), and made our code freely available under the GPLv3 license. The software, scripts and data used to perform our analyses are available on Github (htttps://github. com/nicolasAmat/SMPT) and are archived in Zenodo  [2]. The tool supports the declaration of reachability constraints expressed using the same syntax as in the Reachability examinations of the Model Checking Contest (MCC). For instance, we use PNML as the input format for nets. SMPT relies on a SMT solver to answer sat and unsat-core queries. It interacts with SMT solvers using the SMT-LIBv2 format, which is a well-supported interchange format. We used the z3 solver for all the results presented in this section.
We display our results in the chart of Fig. 5 , which gives the number of feasible problems, for each tool, when we change the timeout value. We also provide the computation times, for the same dataset, in Table  2. We observe that our performances are on par with Tapaal, which is the fastest among our three reference tools on this benchmark. Our tool is actually quite mature. In particular, a preliminary version of SMPT  [1]  (without many of the improvements described in this work) participated in the 2021 edition of the MCC, where we ranked fourth, out of ve competitors, and achieved a reliability in excess of 99. 9%. Even if it was with a previous version of our tool, there are still lessons to be learned from these results. In particular, it can inform us on the behavior of SMPT on a very large and diverse benchmark of bounded nets, with a majority of reachability properties that are not invariants.
Graph generation To be able to evaluate our heuristics, we generate a dataset of 32 graphs with diversity in the number of task types, the costs of the tasks, and the graph shape. To generate a graph, we generate tasks while filling a pipeline of workers 2. We affect each task to its best worker. Consequently, at the end of the generation process, we know the scheduling that minimizes the makespan and have a lower bound for a hardware configuration that corresponds to the pipeline. We also generate a predecessor matrix P randomly. This predecessor matrix is of size v × v and P i,j. It represents the average number of predecessors of tasks of type i that are of type j. Our graph generation method uses this predecessor matrix as input and adjusts the predecessors of the newly created tasks so that they match the values of the matrix.
Software We select four applications that are already parallelized with StarPU to evaluate our scheduler: ScalFMM  (Agullo et al. , 2014)  is an application that implements the fast multipole method (FMM). The FMM algorithm computes the n-body interactions between the particles directly and across a tree mapped over the simulation box. We use it with two test-cases based on the testBlockedRotationCuda program. The first one runs with the default parameters and 10 million particles. The other one runs with a block size of 2,000, a tree height of 7, and 60 million particles; QrMUMPS computes the QR factorization of sparse matrices  (Agullo et al. , 2013)  using the multifrontal method  (Duff & Reid, 1983). When it was extended to heterogeneous architectures in 2016 by Florent LOPEZ  (Lopez, 2015) , Heteroprio was the fastest scheduler of StarPU for this application.
In our experiment, we choose to measure the factorization time of the TF16 matrix  (Thiery, 2008) , from the JGD_Forest dataset; Chameleon is a library for dense linear algebra operations that supports heterogeneous architectures  (Agullo et al. , 2010). We select the same operations as the ones considered by the authors for the benchmarks presented in their user guide: a Matrix Multiplication (GEMM), a QR factorization (QRM), and a Cholesky factorization (POTRF). We use a block size of 1,600 and a matrix size of 40,000 for the Matrix Multiplication and the QR factorization. For the Cholesky factorization, we use a matrix size of 50,000; PaStiX is a library which provides a high performance solver for sparse linear systems  (Hénon, Ramet & Roman, 2002; Lacoste, 2015). We consider two stages of the example program named 'simple': the LU factorization and the solve step.
The program generates a Laplacian matrix. We choose a matrix size of 100 3. For a given set of parameters (scheduler, hardware configuration, etc. ), each application is run 32 times. All these applications can be configured to use StarPU and, therefore, the task-based model. The codelets (low-level kernels) are encapsulated into tasks that are submitted to StarPU. The four applications have CPU and CUDA kernels and at least one task that has both a CPU and a CUDA implementation. For the latter hybrid tasks, the scheduler is responsible for making the proper processor type choice. Finally, the tested applications are all written in C, except for QrMUMPS which is written in Fortran. To make the applications usable for our tests, we change parts of them. We update QrMUMPS and ScalFMM so that they use performance models, which are needed by our automatic strategy but also by most schedulers.
LAMP Primer Design and Viral DNA Standard Synthesis The LAMP primers were designed for detecting the past and present mpox isolates based on phylogenetic analysis of sequences associated with the 2022 epidemic  [5, 9]. Twelve sequences of mpox including Clade I, Clade IIa, Clade IIb and 3 vaccinia virus strains deposited in NCBI were aligned using MEGA (version 11. 0. 11) (Figure  1 ). Two inverted terminal repetitions (positions 5840nt-6339nt/190765nt-191264nt) were identified in the region surrounding the N4R gene in the reference mpox strain of this study (MPVX-FRA-2022-TLS67, GenBank accession: ON602722. 2) for LAMP primer design. This target region was synthesized and then inserted into the pUC57 plasmid (Sangon Biotech, Shanghai, China) as a template. The primer designs were made utilizing the NEB LAMP Primer Design Tool (version 1.
Less-resourced languages are usually left out of phonetic studies based on large corpora. We contribute to the recent efforts to fill this gap by assessing how to use open-access, crowd-sourced audio data from Lingua Libre for phonetic research. Lingua Libre is a participative linguistic library developed by Wikimedia France in 2015. It contains more than 670k recordings in approximately 150 languages across nearly 740 speakers. As a proof of concept, we consider the Inventory Size Hypothesis, which predicts that, in a given system, variation in the realization of each vowel will be inversely related to the number of vowel categories. We investigate data from 10 languages with various numbers of vowel categories, i. e. , German, Afrikaans, French, Catalan, Italian, Romanian, Polish, Russian, Spanish, and Basque.
In this particular preliminary study, we use the data from Lingua Libre, an open access, crowdsourced linguistic library from Wikimedia France (https://lingualibre. org/, accessed on 30 August 2022), and the open access alignment tool WebMAUS, the Munich Automatic Segmentation System  (Kisler et al. 2017; Schiel 1999) , to investigate a sample of 10 languages, 8 of which are less-resourced. Our ambition is that the initiation of such practices would allow the scientific community to study larger numbers of languages, most of which are usually understudied because they are under-represented in oral corpora, thus opening promising avenues for the investigation of typological comparisons and broader theoretical questions. The second goal of the paper is to apply this new methodology to the question of vocalic variation with regards to inventory size (ISH), and thus demonstrate the extent to which our methodology can be useful to provide a new insight on old questions.
Materials and Methods To test our methodology, we use the data from Lingua Libre (https://lingualibre. org, accessed on 30 August 2022), a participatory linguistic media library developed by Wikimedia France and launched in 2015, with the aim to counter the lack of oral data as well as of diversity in the languages represented on the Internet. As a crowd-sourcing tool, any speaker can log into Lingua Libre, fill in a profile with basic metadata, and record themselves (or guests) reading lists of words in their native language. The device detects pauses, which allows for the recording to end when the word has been read and the next recording to start automatically after, therefore effortlessly generating relatively short audio files for each word. Each audio file contains the recording of a word and is supposed to be titled on the same template of 'Language-Speaker name-Item name'.
For example, for the recording 'fra. -Guilhelma-avril. wav', the language of the recording is French ('fra'), the speaker ID is 'Guilhelma', and the recorded item is 'avril', which means April. In February 2022, the Lingua Libre library contained almost 677,000 recordings in 147 languages across 737 speakers. Among them, 34 languages displayed at least 1000 words, across a varied number of speakers. As an open-access, crowd-sourced tool, it has the advantage of offering an ever-growing data set. To the best of our knowledge, however, besides our own research project, Lingua Libre was used only once for academic purposes, i. e. , to estimate the transparency of orthographies in 17 languages using an artificial neural network  (Marjou 2021). The workflow for data extraction is as follows. First, the recordings are selected from the Lingua Libre database 1.
In the present study, we extract a subsample of 50 items for each vowel in each language, to counter the fact that some languages have many more data points than others. The threshold 50 was chosen based on the minimum count of the targeted vowels in a sample of 1000 recordings for each of the selected languages. We also aim at having a similar number of speakers approximately 10 for each language. Second, the recordings are segmented and aligned using WebMAUS  (Kisler et al. 2017). WebMAUS is the online open-access version of the Munich AUtomatic Segmentation (MAUS) software  (Schiel 1999) , which is used to automatically time-align a recording based on its orthographic transcription. In general, MAUS creates a pronunciation hypothesis graph based on the orthographic transcript of the recording using a grapheme-to-phoneme converter.
The formants F1 and F2 are extracted between the range of 50 and 3000. During this process of data extraction and analysis, the following R packages are used: emuR  (Winkelmann et al. 2017) , PraatR (Albin 2014), and tidyverse  (Wickham 2017). The code and the data used for the analysis are available in the Supplementary Materials. For the present study, we limited our investigation to 10 languages: 5 Romance languages (Spanish, Catalan, French, Italian and Romanian), 2 Germanic languages (German and Afrikaans), 2 Slavic languages (Polish and Russian) and 1 isolate (Basque). Among them, only two (Spanish and French) can be considered as well-resourced in terms of phonetic data  (Mariani et al. 2012; Melero et al. 2012) , while the others are mid-to lowresourced, in particular compared to English  (Branco et al.
2012; Burchardt et al. 2012; Calzolari et al. 2012; Hernáez et al. 2012; Miłkowski 2012; Moreno et al. 2012; Trandabȃt , et al. 2012 ). These languages were selected because (i) they have at least 1000 recordings in Lingua Libre, (ii) they do not use tones as a distinctive feature, (iii) they do not use vowel harmony (i. e. , levelling in the quality of all the vowels in a word), (iv) they are manageable in MAUS, and (v) they have the three vowels /a, i, u/ in their MAUS inventory (for instance, English had enough data in Lingua Libre and a segmentation tool in MAUS, but the vowel /a/ was not taken into account by the MAUS inventory for English, which could only align /ae/ and /A/ for this language). During the analysis, we did not include metadata such as gender and geographic location due to two reasons.
First, these elements are not required by the recording system of Lingua Libre. Therefore, it is not systematically provided by speakers and a large portion of the speakers are not annotated with the information of gender and/or geographical location. Second, for speakers with this metadata, the information also requires manual cleaning. As an example, speakers provided geographical location at different levels, including towns, cities, and countries. Some speakers also provided non-existing location names. Due to these reasons, we did not include metadata in the current analysis. However, we plan on including them in future analyses after a process of data wrangling. In the following, we provide a short typological description for each language and the number of distinctive vocalic qualities, or vocalic timbres.
Languages are listed from the largest number of vocalic timbres to the smallest. Note that we decided to range languages according to their vocalic timbres, not their actual vowel inventory, since vowel inventories can vary from author to author (see e. g. ,  Moran and McCloy (2019) ). To do so, we used the inventory of each language as displayed in WebMAUS and manually counted the number of different vocalic timbres. During this process, long and short vowels were merged and nasal and rhoticized vowels as well as diphthongs and triphthongs were ruled out.
• German (deu): German is a Germanic language mostly spoken in Germany, Austria and Switzerland. It displays 14 vocalic timbres: /i, y, I, Y, e, ø, E, oe, a, @, O, o, U, u/. Our vowel inventories therefore range from 5 to 14, with languages across 4 typological groups, although all are Indo-European. Table  1  shows the numbers of [a], [i] and [u]-tokens for the sample of 1000 recordings from each language. The languages are ranked from the largest to the smallest number of oral vowels. The columns [a], [i], and [u] list the raw counts of each vowel across the 1000 recordings extracted for each language. As an example, within 1000 recordings of Basque, the vowel [a] is found 1376 times. The number of speakers is also included. We acknowledge that the number of speakers should ideally be the same across languages, but since the data of Lingua Libre is not controlled as in a formal experiment, we could only aim at having a similar number of speakers per language.