dataesr/scientific-paragraphs-categorization huggingface dataset:
This study was funded by the CO 2 Human Emissions (CHE) project, which received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 776186. The authors kindly thank the scientists who provided the measurements used in this study. In particular, the MIPAS averaging kernels were provided by Michael Kiefer. The surface measurements from the NOAA network have been performed by scientists affiliated with NOAA (Stephen Montzka, Carolina Siso, John B. Miller, Fred Moore). The airborne measurements from HIPPO have been made with the help of Eliott L. Atlas at the University of Miami (for the HIPPO campaign). Dan Yakir facilitates the collection of flask samples at WIS. Shohei Hattori facilitates the collection of flask samples over Japan. Financial support.
In addition, the safety factor of 3. 3 is obtained against the initial predicted stress of 932. 46 MPa with a corresponding safety factor of 0. 8129. Real time parametric prediction and optimisation using Lunar and Quasar (ODYSSEE software package) enabled the examination of the casing structural responses based on the pertinent parameters. In effect, a very good agreement was found between "KNN" and Lunar predictions on parameters influencing casing buckling phenomena and the corresponding Mises stress. Lunar optimisation provided the ideal parameter values for the attainment of pre-define von Mises stress as a function of other factors. This quick approach shows both accuracy and validation of the two independent procedures arriving at the same conclusion. We found that concurrent investigation of the casing buckling attributing factors and optimisation using FEA and ODYSSEE package is sufficient to maintain casing structural integrity during shale gas extraction process.
Data Mining and machine learning Machine learning is the use of a machine/computer to learn in analogy to how the brain learns and predicts. It combines statistics and computer science techniques and depends on a new class of learning algorithms that improve with time, as well as the availability of large datasets to train the systems. In some cases, the methods are directly inspired by the way the brain works, as is the case with neural networks  (Theodoridis, 2015). According to  Mitchell (2006)  machine learning is defined as a well modelled learning problem -where a computer programme learns from experience (E) with respect to some tasks (T) and some performance measure (P) if its performance on (T) as measured by (P) increase with experience (E). Fig. 7  presents schematic diagram of how the machine learning task is accomplished.
Data Mining using "R" Like any other machine learning tasks, the relevant libraries deployed in this study include caret, pRoc, mlbench and ggplots2 using results of finite element analysis study in Section 3. 1. The classification and Regression Training ("caret") package contains functions to streamline the model training process for complex regression and classification problems. The pROC package contains tools for visualising, smoothing and comparing receiver operating characteristic (ROC curves). The basic unit of the pROC package is the 'ROC' function. It will build a ROC curve, smooth it, if requested (if smooth = TRUE), compute the area under the curve AUC (if auc = TRUE), the confidence interval (CI) if requested (if ci = TRUE) and plot the curve if requested (if plot = TRUE). The mlbench library converts X (which is basically a list) to a data frame.
Data Mining and machine learning for critical deformations The Lunar is an Artificial Intelligent (AI) software platform that utilised different solvers to perform real time parametric simulation interactively to make prediction and optimisation. Lunar uses past experiences (results) in order to predict new responses with ROM methods (Reduced Order Model method). In contrast, Quasar is a web-based machine learning software that is used for prediction and forecasting  (Kayvantash, 2015). Specifically, in the Lunar and Quasar the parametric prediction and optimisation this study utilised 258 instances which represent 50% of the simulation data for the training set. On the other hand, the testing set comprises of 129 instances which represent 25% of the simulation data. Furthermore, the relevant data associated with casing (Design of experiment -DOE) and the corresponding responses are predicted, and optimisation performed to determine parameter sets for a desired target.
This analysis in Lunar is carried out using the simplified workflow shown in Fig. 9. Additional data modelling in Quasar (machine learning package) is performed using principal component analysis (PCA) to determine the distribution of the sample's designs. The variance of attributing parameters is computed and plotted to estimate how these attributes are diminishing. Furthermore, matrix concatenation operation is carried out on the DOE datasets and the corresponding responses for the generation of heatmap of von Mises stress on the casing.
Effect of slip plane and casing inner diameter on casing stress Lunar evaluated the effect of changing slip plane and inner diameter on casing von Mises stress over time is examined on the testing dataset (DOE). The (grey shadow) is generated assuming the slip plane and inner diameter were to change 100 times for selection and design purposes. The analysis in Lunar predicted the corresponding responses for the new design of experiment (DOE). The influence of each parameter is investigated and corridor of each evaluated. Fig. 18(a  and b ) present the corridor for slip plane and inner diameter respectively on von Mises stress over the period of investigation. As expected, and as it can be seen from our study results, different parameters (see Table  4 ) have different influence on casing von Mises stress particularly for time dependent data as shown on Fig.
The meaning of our gestural productions is articulated out of our body, permeated with our past experiences and our relationships to others. Our gestures are the result of the hybridization between what is universal, generic through the setting in motion of human bodies as they are all naturally structured, and what belongs to each and every single individual, their development, and their history. The starting point of our work together was our questioning of disembodied linguistics. The forms produced by human beings in interaction are the main object on 2 Affordances can be defined here as the potential offered by physiological constraints that shape the possible movements of each part of the human body. 3 ANR-08-COM-021 Project, Communication Langagière chez le Jeune Enfant, Principal Investigator, Aliyah Morgenstern, Morgenstern and Parisse 2012; https://colaje.
By using an experimental design in which judges recognize labels associated with videos of gestures, it is possible to validate the semantics of gestures and the physiological structuring of their meaning. Thanks to his method which combined corpus analyses and perception tests, Boutet analyzed and isolated gestures with very similar forms and sought to clarify a network of semantic relationships based on the constraints and affordances of the body. In order to conduct corpus analyses, Dominique Boutet constantly created detailed "templates" using the ELAN software (https://archive. mpi. nl/tla/elan; Wittenburg et al. 2006)  which made it possible to annotate all the kinesiological parameters he highlighted. This enabled him and the various colleagues who worked with him to capture the internal composition of gestures as well as their differences, however slight they may be, using internal criteria based on the specificities of the part (or parts) of the body that are in movement (or set in motion).
These thresholds concerned the deviation of the positions on each axis, for a defined number of successive images, and enabled us to automatically identify the three types of head movements. We wanted to confront our manual annotations and the annotations derived from the Kinect (filtered thanks to our thresholds), in order to progressively adjust the two types of inputs. This approach makes it possible to check the annotator's perception in 2D and to adjust the thresholds so that the two coincide as much as possible in order to objectify and facilitate annotation in the future. We then acquired 9 a more precise piece of equipment with IMU, the Neuron, (see description above). Although the tool is more invasive, our older signing participants seemed to cope with it very well. As in the previous study, we worked on the video data in parallel, segmenting and decomposing the lexical units in ELAN, in order to calculate signing rate (average duration of a sign, average duration of its gestural core, the stroke).
We started investigating how to explore gesture from other perspectives. For instance, we used motion capture which can combine different modalities involving proprioception or kinesthesia and free our perspective from a vision-centered analysis (Jego, Meyrueis and Boutet 2019). b) In the field of digital arts,  (Boutet, Tramus, Blondel, et al. 2016 ) proposed an unconventional contribution to gesture annotation: they analyzed improvisations between a participant and a virtual agent in an interactive art installation InterACTE  (Batras et al. 2016). The participant was invited to interact with the shadow of a virtual character using upper-limb gestures. This character was able to generate gestures picked in a database and also to imitate the participant's movements using a Kinect motion capture camera which tracks the body but not the fingers.
Clinical decision support systems (CDSSs) implementing cancer clinical practice guidelines (CPGs) have the potential to improve the compliance of decisions made by multidisciplinary tumor boards (MTB) with CPGs. However, guideline-based CDSSs do not cover complex cases and need time for discussion. We propose to learn how to predict complex cancer cases prior to MTBs from breast cancer patient summaries (BCPSs) resuming clinical notes. BCPSs being unstructured natural language textual documents, we implemented four semantic annotators (ECMT, SIFR, cTAKES, and MetaMap) to assess whether complexityrelated concepts could be extracted from clinical notes. On a sample of 24 BCPSs covering 35 complexity reasons, ECMT and MetaMap were the most efficient systems with a performance rate of 60% (21/35) and 49% (17/35), respectively.
Annotation tools: ECMT, SIFR, cTAKES, and MetaMap We implemented four automatic semantic annotators to extract data from BCPSs. Currently, two systems are widely used in the biomedical field for the English language  [4] , MetaMap and cTAKES. Since we work on a corpus of French BCPSs, we also considered two systems that work for the French language, i. e. , ECMT and SIFR  [5]. x MetaMap was developed by the National Library of Medicine (NLM) to map biomedical text to concepts in the Unified Medical Language System (UMLS). The tool uses a hybrid approach combining natural language processing (NLP), knowledge-intensive approach, and computational linguistic techniques. x cTAKES for Clinical Text Analysis and Knowledge Extraction System uses rule-based and machine learning to extract information from clinical text.
x ECMT (Extracteur de Concepts Multi-Terminologique http://ecmt. churouen. fr) is a webservice inspired by the CISMef algorithm for information retrieval with Doc'CISMeF. ECMT works for the French language with seven terminologies and supports semantic expansion features. x SIFR for Semantic Indexing of French Biomedical Data Resources (http://bioportal. lirmm. fr/annotator) annotator is an openly available web service enabling both recognition and contextualization of concepts from 30 medical terminologies and ontologies.
The response of planetary surfaces to impacts is also highly important for understanding the bodies' geophysical evolution, and for providing accurate estimates of surface ages  (Marchi et al. 2015). Experimentally, slow impacts have been studied for low-gravity conditions using drop-tower setups  (Goldman & Umbanhowar 2008; Altshuler et al. 2014; Murdoch et al. 2017 Murdoch et al. , 2021;; Brisset et al. 2020) , parabolic flights  (Brisset et al. 2018) , space-shuttle missions  (Colwell & Taylor 1999; Colwell 2003) , and fluidized granular beds  (Costantino et al. 2011; Brzinski et al. 2013). The physical limitations of these setups, however, coupled with their high cost and complexity, have made it difficult to construct a large database with established collision information. Thanks to increasing computational resources and the development of robust algorithms in the last decades, numerical modeling has emerged as a useful tool to help extend these low-gravity test campaigns.
(2020). The normal and tangential contact forces in pkdgrav are calculated using the linear Hooke model, and the relevant equations and relationships can be found in Sects. 2. 1-2. 3 of  Schwartz et al. (2012). The rolling and twisting friction models that are implemented in the Chrono code are described in Sect. 3. 4 of  Sunday et al. (2020) , and the friction models that are used in pkdgrav are described in Sect. 2. 2 of  Zhang et al. (2017). Since Chrono and pkdgrav use different force and resistance models, the codes require different input parameters. In some cases, the names of the input parameters appear to be the same, but the values are different because they are related to the specific models in each code. For example, pkdgrav uses an input value called the shape parameter to account for the contribution of a particle's shape to the bulk friction of a material.
The inter-particle friction is controlled by adjusting both the shape parameters and the friction coefficients. The friction models in Chrono do not include a shape parameter, so the userspecified values for the friction coefficients are notably different than those used by pkdgrav. Another important difference between the codes is how they determine the stiffness and damping coefficients for the particleparticle and particle-wall contacts. Both codes include the option to directly specify the normal and tangential stiffness coefficients, but they also have built-in functions that can derive the values from other input parameters. In this study, we opted to use the built-in functions because these methods are frequently adopted by code users to determine the optimal coefficients for the contacts.
Simulations with Chrono Chrono is an open-source code that can be used to simulate rigid body interactions, soft body interactions, and even interactions between solids and fluids  (Tasora et al. 2016 ). Among other capabilities, it includes modules for performing Finite Element Analysis (FEA) and for developing sensors for autonomous vehicle systems  (Elmquist et al. 2021 ). This study makes use of the Chrono::Multicore module (in Chrono 5. 0. 0 and older, Chrono::Multicore is named Chrono::Parallel). Chrono::Multicore allows users to simulate granular systems with SSDEM (known as SMC, or the SMooth Contact model in Chrono) using shared-memory parallel computing with OpenMP. Contacts between particles are identified and evaluated according to a two-phase collision detection algorithm  (Mazhar et al.
2013 ), and the system is advanced with a first-order Euler implicit linearized integrator. The SSDEM code in Chrono::Multicore was recently modified to include rolling and twisting friction and to include additional force and cohesion models  (Sunday et al. 2020). The rolling and twisting friction models are velocity dependent and were implemented to match the models found in early versions of pkdgrav  (Schwartz et al. 2012). The modified Chono::Multicore code (available in Chrono 5. 0. 0 and newer) was validated though a series of simple two-body collision tests, piling simulations, and rotating drum tests  (Sunday et al. 2020). Table  1  summarizes the parameters that were used for the Chrono simulations in this study. Following the work of  Schwartz et al. (2014) , the normal coefficient of restitution (COR) for the walls is set to 0.
Simulations with pkdgrav Pkdgrav is an N-body gravity tree-code  (Richardson et al. 2000 (Richardson et al. , 2009 (Richardson et al. , 2011;; Stadel 2001 ) that models interactions between grains according to the SSDEM implementation by  Schwartz et al. (2012). The code was later improved by  Zhang et al. (2017)  with a new rotational resistance model for twisting and rolling friction, by  Zhang et al. (2018)  with a cohesion force model for characterizing van der Waals cohesive forces between interstitial fine grains, and by  Maurel et al. (2018)  with the introduction of "reactive walls", namely inertial walls that can react to the forces exerted by the grains (in contrast to traditional walls in pkdgrav whose motion is predefined and is not affected by contacts with grains). Advantages of pkdgrav include full support for parallel computation (supporting both shared-memory parallel computing with Pthreads and distributed-memory parallel computing with MPI), the use of hierarchical tree methods to rapidly compute long-range inter-particle forces (e.
g. , gravity) and to locate nearest neighbors for computing short-range contact forces and potential colliders, and options for particle bonding to make irregular rigid aggregates. Pkdgrav uses a second-order leapfrog integrator: in each step, particle positions and velocities are alternately "drifted" and "kicked" (see  Richardson et al. 2009, for details). Collision searches are performed during the drift step by examining the trajectories of neighbors for each particle to ensure no collisions are missed. A hierarchical tree data structure is used to detect collisions between particles at each time step and generate particle neighbor lists in O(N log N) time, where N is the number of particles in the simulation  (Richardson et al. 2011). The code has been validated through comparisons with experiments and other numerical codes for diverse applications, such as hopper discharges  (Schwartz et al.
Data analysis Collision behavior is often characterized using three values: the peak acceleration of the projectile, the maximum penetration depth of the projectile, and the total duration of the collision. During the experimental trials, the projectile's acceleration was logged at a sampling frequency of 1. 4 kHz using an accelerometer mounted to the inside of the projectile. The projectile's velocity and position profiles were then obtained by integrating the acceleration data. The beginning of the collision is identified as the moment when the projectile is no longer in free-fall, and the end of the collision is defined as the moment when the projectile's acceleration falls below a threshold value of 0. 1g. For more information on the accelerometer specifications or the processing of the experimental data, please refer to  Murdoch et al.
Code performance The calibration tests described in Sect. 3. 3 provide an opportunity to compare the overall performances of Chrono and pkdgrav. As previously discussed, the two codes implement different force and resistance models, and thus require different input parameters to accurately model the glass bead material. Despite their implementation and parameter differences, both codes successfully reproduce the collision behaviors observed in the experiments. In this section, we focus on the computational aspects of the two codes. Chrono::Multicore is parallelized using the OpenMP application program interface (API) for shared-memory multiprocessing. We ran the Chrono simulations on an Intel ® Xeon ® Gold 6140 processor using 36 threads. Benchmark testing with this specific system has shown that the code's performance is optimal when the simulations are executed on 24 threads, but that there is little variation in the performance when the simulations are executed on 24-36 threads.
, the mixing and settling of the material in the container). The trend implies that an impact simulation with one million particles will take 2. 5 µs per step per particle to complete, just like the smaller system that was used for this study. However, a 0. 5 s simulation with one millions particles and a 1 µs time step will take approximately 14. 7 days to execute. Therefore, Using the Chrono::Multicore code to simulate very large granular systems is only feasible if the runtime of the simulation is short or if the simulation time step is relatively large. pkdgrav supports both shared-memory parallel computing with Pthreads and distributed-memory parallel computing with MPI. In this study, we ran the pkdgrav simulations using the MPI implementation on dual Intel ® Xeon ® IvyBridge E5-2670v2 processors running at 2.
Acknowledgements. C. S. is funded jointly funded by the  Centre National d'Études Spatiales (CNES)  and the  Institut Supérieur de l'Aéronautique et de l'Espace (ISAE)  under a PhD research grant, and F. T. was supported through a  PhD fellowship  from the  University of Nice-Sophia. All authors acknowledge support from  CNES , and F. T and P. M. acknowledge funding from  Academies of Excellence: Complex systems and Space , environment, risk, and resilience, part of the IDEX JEDI of the  Université Côte d'Azur. P. M. and Y. Z. acknowledge funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 870377  (project  NEO-MAPP ). Y. Z. acknowledges funding from the  Doeblin Federation  and the " Individual grants for young researchers" program , part of the IDEX JEDI of the  Université Côte d'Azur.
De Bruijn graphs play an essential role in computational biology. However, despite their widespread use, they lack a universal scalable representation suitable for different types of genomic data sets. Here, we introduce simplitigs as a compact, efficient and scalable representation and present a fast algorithm for their computation. On examples of several model organisms and two bacterial pan-genomes, we show that, compared to the best existing representation, simplitigs provide a substantial improvement in the cumulative sequence length and their number, especially for graphs with many branching nodes. We demonstrate that this improvement is amplified with more data available. Combined with the commonly used Burrows-Wheeler Transform index of genomic sequences, simplitigs substantially reduce both memory and index loading and query times, as illustrated with large-scale examples of GenBank bacterial pan-genomes.
This involves applications of de Bruijn graphs to variant calling and genotyping  [14] [15] [16] [17] [18]  , transcript abundance estimation  [19]  , and metagenomic classification  [20] [21] [22] [23]. In the latter application, -mer-based classifiers perform best among all classifiers in k inferring abundance profiles  [24]  , which suggests that de Bruijn graphs truthfully approximate the graph structures of bacterial pan-genomes, even if constructed from noisy assemblies from incomplete databases. Even if more advanced pan-genome graph representations are available, such as variation graphs  [25]  , de Bruijn graphs with large -mer lengths are still useful for indexing  [26, 27]. k 2 As de Bruijn graphs are one of the primary data structures in much of sequence analysis, the efficiency of many algorithms is directly tied to the efficiency of computation and representation of the graph.