dataesr/scientific-paragraphs-categorization huggingface dataset:
2019 (Mombarg et al. , 2021)). Our focus is on the prograde dipole modes and radial orders n ∈ [-100, -10], as these are most commonly observed mode geometry in γ Dor and SPB pulsators (e. g. Van Reeth et al. 2016; Li et al. 2020; Pedersen et al. 2021). The mode periods were computed using the stellar pulsation code GYRE (v5. 2; Townsend 2003; Townsend & Teitler 2013). In Fig. 6 , we show the predicted period spacings, ∆P n = P n+1 -P n , as a function of period, P n , for the three different values of X c /X ini. To produce mode trapping when atomic diffusion (in- cluding radiative levitation) is taken into account, a low mixing efficiency close to the core boundary is required, and a high efficiency further outward. Our models show this can be realized by the prescription given in Eq. 13, as we observe mode trapping for all stellar masses studied here when we choose η = 60.
The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation -Flanders (FWO) and the Flemish Government -department EWI. JSGM, TVR and MM gratefully acknowledge support from the Research Foundation Flanders (FWO) through grants V429020N, 12ZB620N and 11F7120N, respectively. MR acknowledge the support of the French Agence Nationale de la Recherche (ANR), under grant ESRR (ANR-16-CE31-0007-01). Part of the numerical computations have been performed using the HPC resources from CALMIP (grant 2020-P0107), which is gratefully acknowledged. We thank Dominic M. Bowman for his comments on the manuscript. A special thanks to Bill Paxton and his MESA team, and also to Rich Townsend and his GYRE team for all the efforts put into these projects that have been paramount to this work.
We are grateful for the fast response of the anonymous referee, and for their comments which have helped to improve the clarity of this manuscript. Software: MESA (r11701;  Paxton et al. 2011 Paxton et al. , 2013 Paxton et al. , 2015 Paxton et al. , 2018 Paxton et al. , 2019)) , MESASDK  (Townsend 2019a,b) , GYRE (v5. 2; Townsend & Teitler 2013; Townsend et al. 2018) , matplotlib  (Hunter 2007) , numpy  (Harris et al. 2020)  13.
Ergonomics Evaluation and Human-Robot Collaboration To plan appropriate collaborative actions, collaborative robots need to have an estimation of the current human posture  [20, 29]. They must be able to calculate the physical, physiological, and/or cognitive state of the human to act accordingly. The perception of the human state relies on sensors that can be placed in the environment, embedded in the robot, or worn by the human. State-of-the-art motion capture techniques based on infrared cameras and reflective markers, or more recently on inertial technology, remain widely used to provide high-fidelity and high-frequency measurements of human kinematics  [30]. The captured data are fitted to a DHM designed to have similar properties to the human operator (height, weight, and structure).
The result is an intuitive visualization tool, called Latent Ergonomics Map (LEM), which provides a synthetic representation of the ergonomics of motions. The LEM is coupled with a real-time simulation of the human using a DHM that displays localized ergonomics information using colored spheres placed in relevant body parts. Both the LEM and the simulation are updated online, with the information coming from the human motion tracking system and eventually with the robot's data if the human is working in interaction with a robot. The ergonomics visualization tools are shown in Figure  1. The only requirement of our system is that it requires a real-time human motion tracking system like wearable or environmental sensors. In this work, the human posture is tracked using an Xsens MVN motion tracking suit.
g. , mass) are computed from anthropometric data available in the literature  [32] , assuming a homogeneous density of the links and scaling with the human body mass. The posture of the DHM is calculated using a one-to-one mapping from the original human poses, which are measured in our case by a human skeleton or motion tracking system. The position of the floating base of the DHM is calculated using the position of the pelvis, which is provided by the motion tracking system. The DHM enables the estimation of the human internal torques, as described in previous work  [12]. However, here we do not use force plates to retrieve feet wrenches: instead, we approximate the wrench acting on the human feet with the weight force generated by the body. This approximation introduces an error in the joint torque estimation, but it is tolerable for our visualization purposes as long as the joint torques are not used in a control loop.
The resulting map is the Latent Ergonomics Map. This representation allows us to visualize in an immediate way when a person is in a non-ergonomic (associated with red color) or ergonomic (associated with green color) posture. In future work, we would like to use the presented space to provide corrections to humans and plan better ergonomically optimized trajectories. The LEM is created in an offline phase, requiring a dataset of human movements and the definition of the ergonomics score. Training a LEM proceeds in two steps. The first step is to create and train a Variational Auto-Encoder (VAE) to represent the human postures. A schematic of the training procedure is shown Figure  3. A VAE is an autoencoder based on variational inference  [39]. Let [x k ] K k=1 be a data-set of K independent and identically distributed samples of some continuous observation variable x of unknown distribution.
Training the VAE requires a dataset of human movements. For example, the AnDy dataset  [30]  contains many examples of human activities, recorded with the Xsens motion tracking suit, which can provide both wearable sensing data and estimated postural data. To simplify, let us assume that we input human postures fitted to the DHM (R 66 ). We train the VAE with batches of human postures from the training dataset to find the parameters of the encoder-decoder networks, to minimize the loss function Equation (2) via backpropagation. Once the VAE is trained, the second step consists in creating the ergonomics landscape projected on the latent map. The procedure is illustrated in Figure  4. We uniformly sample in the latent space a set of 2D vectors (z i,j = [z i , z j ] ∈ R 2 ). We reconstruct the samples using the decoder.
Some movement examples are shown in the video attachment. The robot used is the Panda (Franka Emika) robot controlled using libfranka and FrankaROS libraries. In both experiments, the human is equipped with an Xsens MVN motion tracking suit, which provides real-time (240Hz) information about the current human pose: joint angles and link's positions, orientations, velocities, and accelerations. Both the human's and the robot's sensors measurements are streamed online to the module, to visualize and simulate their movements in the simulation (Dart  [42] ) as shown in Figure  7. An overview of the main modules used in the two scenarios is shown in Figure  8 , which details as well the flow of information from the Xsens sensors and the Franka robot. The simulation receives the sensed information, from both humans and robots, and their corresponding models are updated.
Creating and Visualizing the LEMs To obtain the LEMs for every ergonomics score, we first created and trained the VAE representing the human body postures. The VAE architecture consists of 5 layers with 66 inputs, 200 hidden neurons, 2-dimensional latent space, and 66 outputs, where rectifier, tanh and identity activations are used the hidden layers and the output layer. The hyperparameters of the VAE are chosen based on the reconstruction error and the training time by grid search. To train the latent space, we perform first a pre-training with a large human motion dataset (AndyData-lab-onePerson dataset  [30] , containing more than 5 h of recorded data), then a fine-tuning with smaller task-specific datasets acquired for each experiment (see below). This latter step is optional. The first training with a large dataset is sufficient to generate a LEM, in particular, it enables to obtain a generic LEM that is task-independent and that captures a variety of different postures.
In our experiments, however, the fine-tuning enables us to obtain a task-specific LEM that is more specialized to represent the postures of a specific task, while maintaining the generalization capabilities. All the datasets were normalized between zero and one and augmented using intermediate and mirrored poses before training. The VAE training was carried out according to the procedure explained in Section 3. 3. During training, we optimized both the weight of the encoder and the decoder. The weight optimization was done on an Intel Core TM i7-8850H with 6 cores at 2. 6GHz, requiring about 20 min. The trained encoder can be used to visualize example movements in the latent space. This can be useful for preliminary insights into the postures for different activities. For example, Figure  9  shows the sequence of postures, projected in the 2D latent space, associated with the movements from the dataset of  [38] , which contains activities such as bending, kicking, lifting objects, and walking.
The latter is normalized in the range [0, 1] → [GREEN, RED], using the maximum score value that is set after the maximum score registered for that body part on the dataset used for training the LEM in the offline phase. The reason for the latter normalization is to better spot the difference in ergonomic scores between similar movements. In our setup, it is possible to select the ergonomic score to visualize on the DHM by interacting with an easy-to-use GUI (some examples in the video attachment). Figure  1  shows the DHM displayed during a manipulation task, together with the RULA LEM: while the LEM provides a synthetic visualization of the overall ergonomics score, the spheres on the DHM enable to visualize the individual scores on the different body parts. This is convenient to identify the body parts that are more at risk, from the ergonomics standpoint, which are colored in red; those that are not at risk are colored in green, and the intermediate values in yellow.
Funding: This work was partially supported by the  European Union   Horizon 2020 Research and Innovation Program  under Grant Agreement No. 731540  (project  AnDy ), the  French Research Agency (ANR)  under Grant No. ANR-20-CE33-0004  (project  ROOIBOS ), the  CHIST-ERA  project  HEAP , the  European FEDER  in the context of the  CPER Sciarat  and the CreativLab, and the  Lorraine University of Excellence (LUE)  project  C-Shift.
[23] , highlights the presence of sandy soil at about 10-12 m depth below the crest level resting on a gravelly sand layer. In addition to the mechanical characterization, collected CPT data were used to evaluate the susceptibility of levee materials to liquefaction. Laboratory tests were also performed on collected samples to determine the physical and mechanical properties of levee materials and characterize their cyclic strengths through cyclic triaxial testing. Investigations results showed that the existing levee is composed of 10 m backfill (soil 1) overlaying a thin 1 m sandy layer (soil 2) and several meters of consolidated marl (soil 3), which constitutes the soft rock substratum without liquefiable layers. Some of the properties values provided for these layers (soil 1 to 3) are summarized in Table  1.
Several piezometers positioned along the whole levee showed that the highest and lowest GWT levels were at 7. 3 m and 9. 5 m below the crest level over a time period of 22 years from 1996 to 2018. Since no near-field seismic ground motion data were available for the considered site so far, a fictional earthquake with a magnitude   and peak horizontal ground acceleration   set at 5. 3 and 0. 25 , respectively, was simulated for liquefaction haz- F (%) Figure 2. Example of profiles of tip resistance q c and normalized friction ratio F with depth. In addition to the mechanical characterization, collected CPT data were used to evaluate the susceptibility of levee materials to liquefaction. Laboratory tests were also performed on collected samples to determine the physical and mechanical properties of levee materials and characterize their cyclic strengths through cyclic triaxial testing.
Probabilistic Approach Since GWT tends to vary over time due to precipitation events during the levee s lifetime, a probabilistic methodology is proposed herein to take this variability into account over a time period of 22 years (from 1996 to 2018) for which a satisfactory amount of data is available. Given the lack of data at the site of interest, GWT measurements were also collected from piezometers positioned at neighboring sites over the investigated time period to determine more accurately their annual maxima. The whole collected data were first sorted into blocks of years, from which the maximum annual values were determined. A frequency analysis was then performed using two packages, Renext and Potomax, developed by IRSN within the R software environment. These codes apply the Maximum Likelihood Estimation (MLE) in order to evaluate the distribution function parameters as well as the quantiles of the obtained samples.
Confidence intervals (CIs) are identified for return periods of up to 1000 years. Figure  5  displays the 70% and 95 % CIs for GWT data where it can be clearly seen that the majority of field measurements are within these intervals, with no remarkable outliers. However, the widening of the 70% and 95% CIs for high return periods may reflect some uncertainty with regard to those GWT estimates. In the proposed probabilistic approach, the GWT estimate during subsurface exploration was kept constant at 8 m depth below the crest level, while the GWT during the earthquake was assumed to vary according to various scenarios provided by the frequency analysis. time period to determine more accurately their annual maxima. The whole collected data were first sorted into blocks of years, from which the maximum annual values were determined.
A frequency analysis was then performed using two packages, Renext and Potomax, developed by IRSN within the R software environment. These codes apply the Maximum Likelihood Estimation (MLE) in order to evaluate the distribution function parameters as well as the quantiles of the obtained samples. Confidence intervals (CIs) are identified for return periods of up to 1000 years. Figure  5  displays the 70% and 95 % CIs for GWT data where it can be clearly seen that the majority of field measurements are within these intervals, with no remarkable outliers. However, the widening of the 70% and 95% CIs for high return periods may reflect some uncertainty with regard to those GWT estimates. In the proposed probabilistic approach, the GWT estimate during subsurface exploration was kept constant at 8 m depth below the crest level, while the GWT during the earthquake was assumed to vary according to various scenarios provided by the frequency analysis.
79 mV/10 6 sperm/mL) and in the literature (1. 34 mV/10 6 sperm/mL), the lowest sORP index being 1. 38. In all oligozoospermic patients (n = 115), 97% of the sORP indexes were greater than 0. 79, and 92% of them shown an index greater than 1. 34 (data not shown). It is interesting to note that the higher the sperm concentration, the lower the sORP index. When sperm concentration was greater than 50 × 10 6 sperm/mL, it was no longer possible to observe a significant difference with the NSP group. In order to assess whether the correction method used to normalize the data did not induce a bias in the results obtained in this study, the contribution of spermatozoa in the establishment of the absolute sORP value by the MiOXSYS was investigated. Absolute sORP was measured in sperm and seminal fluid alone.
Discussion This study aimed to confirm the utility of the MiOXSYS in the context of male infertility investigation. MiOXSYS technology evaluates the oxido-reduction potential present in sperm samples and sORP absolute values represent a potential difference expressed in millivolts (mV) while the sORP index report this data on the sperm concentration (mV/10 6 sperm/mL). Sperm samples from patients going to the Fertilys fertility center were analyzed and grouped according to the WHO 2010 sperm parameters reference values (World Health Organization 2010). Significant decreases (P < 0. 0001) in sperm concentration, total sperm count, motility, vitality and normal morphology were observed in ASP patients, when compared to NSP patients, as expected. Routinely analyzed in the Fertilys laboratory,  DNA fragmentation and chromatin decondensation were evaluated in ASP and NSP patients.
They build machine learning-based strategies on structured information to derive classification and regression models  (Ren and Ji, 2017; Shu et al. , xxxx; Aboubakar et al. , 2020). This direction is unreliable without consistent datasets of hoax and non-hoax samples, somehow hardly collected and accessible. Then considerable amount of tasks are to be realized to make these datasets exploitable. To avoid such constraints, this work opts to look for propagation features to limit hoax floods. Moreover, we suppose that hoaxes already exist and we want to make their dissemination as reduced as possible. The third direction refers to study how hoaxes propagate and spread on the social graph. For that, one can make qualitative analysis of patterns to recognize fake news propagation  (Du et al. , 2014; Najar et al.
Smart contract Blockchains exploit smart contracts as procedures to manage interactions between entities on the network  (Khedim et al. , 2018; Suciu et al. , 2018; Titouna et al. , 2018). According to Bayon et al. (Bayón, xxxx) , ''A smart contract is an automatable and enforceable agreement. Automatable by computer, although some parts may require human input and control. Enforceable either by legal enforcement of rights and obligations or via tamper-proof execution of computer code". Therefore, smart contracts operate as independent and autonomous entities, whose behavior is completely predictable (the same input will always produce the same output)  (Christidis and Devetsikiotis, 2016). A smart contract is launched by messages/transactions sent to its address. They allow expressing business logic in code mainly in the form if.
then statements. They should describe all possible outcomes of the contract. A smart contract lives within a blockchain and its state is scrutinized by every network participant to know which action to take. Fig. 1  depicts a smart contract related to renting an apartment while paying in crypto-currencies. Moreover, Smart contracts have many advantages  (Cuccuru, 2017; Jean and De Filippi, xxxx) : 1. Reliability: It is virtually impossible to shut down the entire computers participating in the blockchain simultaneously. As a result, this database is always online and its operation never stops. 2. Removal of intermediaries: smart contracts make it possible to automate a process, while eliminating intermediaries. 3. Unstoppable: The entire code of the smart contract is immutable, in the sense that its code is published and therefore written in a transaction making the application unassailable and unstoppable.
4. Borderless: smart contracts do not depend on a supervisory authority i. e the borders, physical location and jurisdiction of the country. 5. Open source: It means that source code of the smart contract should be accessible and verifiable to the community. 6. Autonomous: Once a smart contract has been created and published, it becomes accessible to the other members of the blockchain. It belongs to any point of the blockchain which can add its own rules and conditions. In addition, smart contract is of interest due to these properties which give a smart contract ability and flexibility to ease decision making. This work relies on the smart contract logic without deploying the whole environment of blockchains. Its role is to help making decision whether the group should consume or not an incoming hoax.
Environment Experiments have been conducted on an Intel(R) Core (TM)2 Duo CPU L9400 @1. 86, 1867 MHz, 2 cores, 2 processors with 4 GigaBytes of DDR 2 memory. The artifact used to simulate different scenario of the experiments has been built with MatLab whereas the smart contract code has been written in Python. MS Excel has been exploited to structure groups into matrices. All the three elements are coupled to provide ability to test the proposed approach. All scripts are available in the GitHub at https://github. com/calvinkda/smart_contrat.
We performed a pan-GWAS analysis on 230 complete genomes. A total of 249 genomic markers strongly associated with various traits (Bt species, Bt subspecies and Bt clusters a to d) were identified. Among them, 128 showed potential informativeness as genomic markers after a TBLASTN validation step. Although the Bt species is defined by the presence of genes encoding Cry/Cyt toxins, the identified markers are not related to these genes, due to the high diversity of Cry and Cyt families within the species. For each designated trait, we discovered one marker or a combination of up to six markers able to identify all the Bt in the study dataset. We wrote a Python script using 14 markers for the automated identification of Bt from a genomic assembly.
Dataset Definition A total of 286 genomes were used, corresponding to 216 FBO-associated Bc isolates, 18 commercial Bt strains  [17] , one Bt reference strain, and 51 Bc isolates of various origins associated with robust metadata, whose complete genomes were retrieved from the NCBI RefSeq database (Supplementary Table  S1 ). In addition to the detection of parasporal insecticidal crystals under a microscope (NF EN ISO 7932/Amd1), Bt membership was confirmed by the presence of insecticidal protein-coding genes using the BtToxins_Digger pipeline v1. 0. 5  [32]. For Bt genomes collected from NCBI, only BtToxins_Digger was used to confirm Bt membership. Using R v4. 0. 1, a random draw was carried out to split the whole dataset into a study dataset (SD) and a validation dataset (VD) (Supplementary Table  S1 ), representing 80% (n = 230) and 20% (n = 56) of the whole dataset, respectively.
Core Genome Phylogeny Core-genome sequences from Panaroo were aligned using MAFFT v7. 471  [40]. A phylogenetic tree of the SD was built with the maximum likelihood method using iQtree   [34]  to obtain an estimation of the core and accessory genomes of the study dataset (SD). Then, the presence/absence matrix output from Panaroo was used for the GWAS analysis performed using Scoary, thereby providing a list of associated genes for each trait. Only genes with at least 80% sensitivity and 100% specificity for the SD were selected. A TBLASTN validation step of the selected genes was performed after recovering the protein sequences from Panaroo. The genes were validated when the respective sequence criteria of coverage and identity were met and when exhibiting at least 80% sensitivity and 100% specificity for the validation dataset (VD).
In Silico Validation of Genetic Markers To certify the reliability of the selected markers, we carried out a TBLASTN v2. 7. 1+ against the VD to validate candidate marker sequences meeting the required conditions. For each cluster associated with a given marker, a protein sequence was retrieved from the Panaroo output and screened for in the VD genomes. Markers were selected only when sequence coverage and identity were strictly higher than 90% compared with genomes displaying the same trait (intragroup), and when the sequence coverage and identity were strictly lower than 80% compared with genomes not associated with the specific trait (intergroup). This extra step made it possible to test the pan-GWAS selected markers in silico against a new dataset of genomes, and to confirm the sensitivity and the specificity of the markers.