dataesr/scientific-paragraphs-categorization huggingface dataset:
Accepted manuscript / Final version From Table  5 , we can observe that some methods achieved high precision up to 100% for a specific distortion type. This means that features of these methods are well-designed for a specific distortion type but do not generalize well to all distortion types. On the other hand, the proposed method provided very stable results for all distortion types considered. [20]  85. 53 89. 61 92. 68 Mateusz  [22]  82. 42 90. 34 84. 55 Kang  [32]  86. 05 84. 82 88. 17 Kede  [34]  89. 67 85. 63 90. 28 Golestaneh  [36]   Finally, from Table  6 , we can notice a significant decrease in performance of all methods, except DIIVINE, compared to their performance on the other datasets. Because, LIVEMD dataset contains images with multidistortion types making them difficult to recognize.
Distortion identification confusion For a complete evaluation of our model, normalized confusion matrices are plotted to discuss mis-classified distortions. Each row in a confusion matrix represents an actual label which we call true label, while each column represents a predicted label. The purpose behind plotting confusion matrices is to show the number occurrences or probability for a class being classified as another one. Figure  7  depicts normalized confusion matrices of our model on the four considered datasets, from which we can notice that the proposed model always perfectly identified and classified distortion types of all datasets, except for CSIQ dataset, where noise distortion is confused with blur distortion. Because neither precision nor recall, alone, gives all the information on the performance of a model, F1score of our model for each distortion type is computed and provided in Table  7.
The good result reported in this table shows that our model is accurate and robust in the classification of distortions. 8  shows the classification performance of each architecture on the four datasets. It is clear that the using DenseNet architecture as a features extractor provides the best performance. Furthermore, in addition to providing the highest performance, this architecture is the one with the lowest number of parameters, thus requiring less memory and computational resources. Second, in Table  9  and Figure  8 , we show the classification performance when the proportions of training data vary between 20% and 80% on the four datasets considered. We can conclude that the classification performance is not strongly dependent on the size of training data. From 75 images as a training dataset, we can achieve a high classification accuracy of over 80% for all datasets, which shows the efficiency of our model.
Evaluation on LVQ dataset For distortion classification in laparoscopic videos, our model is evaluated on the LVQ dataset and compared to multiple state-of-the-art methods, including DIIVINE, BRISQUE and Zohaib's method  [15]. It is important to specify that the LVQ dataset contains both single and multiple distortions, which makes it more challenging. Table  10  shows the overall accuracy and the precision per distortion of our model compared to stateof-the-art methods. We can notice that DIIVINE and BRISQUE methods perform relatively poorly on smoke and uneven illumination distortions, because they were designed to process natural scene images and not laparoscopic videos content where such distortions are common. Zohaib's method provides good results but with different performance for the different distortions.
However, our proposed method offers the best accuracy and outperforms all the considered methods using a single end-to-end model. This illustrates the efficiency and adaptability of our proposed model to different types of distortion encountered in different applications. Figure  9  shows the confusion matrix of our model for each of the distortions on the LVQ dataset. It provides additional information, in particular, allows to visualize which distortions are confused with others. We can see that the videos containing defocus blur are sometimes classified as containing both defocus blur and uneven illumination, this is due to the fact that the dataset does not contain enough samples of videos containing both defocus blur and uneven illumination distortions, so that the model learns to efficiently distinguish when a video contains solely defocus and when it contains defocus with uneven illumination.
Accepted manuscript / Final version Table  11  shows the performance of our model in terms of precision and F1-score for single and multiple distortions on the LVQ dataset. From this table, we can notice that our model identifies and classifies perfectly videos containing a single distortion type, while 98. 61% precision and 99. 30% F1-score are reported for videos with multiple distortions due to the the complex interactions and masking effect between distortions. Another important aspect to consider when classifying distortions in laparoscopic videos is the run time. For such application, real-time performance are required. Table  12  shows the average classification time per frame for DIIVINE, BRISQUE and our model, obtained on intel core i7 system with 32GB RAM and NVIDIA GeForce GTX 1080 running on Windows OS.
Method Average time per frame (seconds) DIIVINE  [27]  3. 30 BRISQUE  [54]  0. 08 Our model 0. 05 by challenge organizers on a private dataset containing different laparoscopic videos than those provided in the training dataset, yielded an F1-score for single distortion equals to 90. 7 and F1-score for a mixture of single and multiple distortions equals to 93. 3. The ranking was done based on a weighted combination of classification accuracy and F1-score as defined in Eq. (  12 ), on the basis of which our solution was ranked 3rd. Accepted manuscript / Final version.
2018 ) which is the post-processing standard of the SPHERE data center  (Delorme et al. 2017). The PACO implementation is based on the algorithm described in  Flasseur et al. (2018a,b,c). A Matlab™ implementation of the REXPACO main routines is available online 9 as a purpose of illustration. It is based on GlobalBioIm 10 , an opensource software for image recon-Fig. 10. Influence of the regularization on the REXPACO reconstructions. xgt (1 st column) is the ground truth elliptical disk injected at the level of flux αgt = 1 × 10 -5. The flux distribution x is reconstructed with a slight amount of regularization (µ → 0 + ; 2 nd column), with µ MSE derived by minimizing the optimal MSE criterion (3 rd column, see Eq. (  20 )) and its estimate µ SURE through the SURE criterion (4 th column, see Eq.
In a nutshell, the proposed combination of the two algorithms works as follows: (i) PACO and REXPACO are launched independently on a given ADI sequence; (ii) based on the S/N map produced by PACO and on the flux distribution produced by REXPACO, the user identifies manually putative point-like sources; (iii) REXPACO and PACO are then alternatively applied. During step (iii), the astrometry and photometry of the selected point-like sources are refined by PACO, based on the residual data obtained after subtraction of the disk contribution as currently reconstructed by REXPACO. Next, the flux distribution of the disk can be refined by REXPACO on updated residuals obtained after subtraction of the refined point-sources contribution estimated by PACO. Step (iii) is repeated until convergence of the disk and point-like sources components.
Conclusion In this paper, we have introduced a new post-processing algorithm, named REXPACO, dedicated to the reconstruction of flux distributions from ADI data sets in the presence of extended features, like circumstellar disks. To the best of our knowledge, REXPACO is one of the first methods specifically designed for this task. It encompasses the statistical modeling of the fluctuations of the nuisance component (i. e. , stellar leakages and additional sources of noise) at the scale of small patches of a few tens of pixels. The resulting statistics of the nuisance term are accounted for in a regularized reconstruction framework following an inverse problem approach based on a forward image formation model of the off-axis sources in the ADI sequences. We have shown that suitable spatial regularizations ( 1 norm and 2 -1 edgepreserving) reduce residual reconstruction artifacts.
Acknowledgments We thank  A. Boccaletti  ( LESIA, Paris, France ) who provided the transmission of the SPHERE coronagraphs. We also thank the anonymous referee for her/his careful reading of the manuscript as well as her/his insightful comments and suggestions. This work has made use of the SPHERE Data Center, jointly operated by  OSUG/IPAG (Grenoble, France), PYTHEAS/LAM/CESAM (Marseille, France) ,  OCA/Lagrange (Nice, France) ,  Observatoire de Paris/LESIA (Paris, France) , and  Observatoire de Lyon/CRAL (Lyon, France). This work has been supported by the  Région Auvergne-Rhône-Alpes  under the project DIAGHOLO, by the  French National Programs  (PNP and PNPS), and by the  Action Spécifique Haute Résolution Angulaire (ASHRA)  of  CNRS/INSU  co-funded by  CNES.
Machine learning AI, specifically Machine Learning (ML), made a revolution in the way we classify and predict systems' future states by using historical data to create and train predictive models. The standard approaches used to train a learning model can be broadly classified in three different ways. One of the most widely adopted is supervised learning, which is used when the training dataset contains the desired output, known as labels, that must be predicted by ML models. For example, in images classification problems, training images generally need to be first classified by human experts to provide data to train the AI models. Those models will generalise this classification to be able to handle unseen images. When training datasets are not labelled or classified with a specific result or prediction, unsupervised learning can then be considered.
This learning will then perform automatically a classification of new data by identifying clusters in the training data. And when dataset can be represented as an environment, reinforcement learning is used to explore and exploit this environment. The type of learning and related algorithms are chosen in function of the type of datasets. Algorithms can also be tuned to work on a specific dataset by using transfer learning  [7]. The transfer learning allows the use of algorithms pre-trained on similar datasets, which reduces the training time. During the last decade, a technique called Deep Learning [8] [9] (DL) has gained popularity and is increasingly being used in various applications. One particular advantage of DL techniques is their ability to easily deal with the crucial task of features selection, which is essential to achieve accurate results.
This makes DL techniques ideal candidates to develop intelligent monitoring systems for Metal AM, due to the expected complexity of the features representing defects in the monitored signals. In DL, different architectures can be implemented, each achieving better or worse results depending on the type of data. To classify images, for the type of data collected in the EBM AM case study presented in this paper, a CNN appears to be the most appropriate DL architecture  [10]. As described below, CNN has made a revolution in the world of image recognition  [9]. Fig. 1. Schematic of an EBM printer  [5]  Thus, for the proposed case study, this paper is focusing on testing five well-known CNNs:  AlexNet: AlexNet is the first CNN to win on 2012 ImageNet Large Scale Visual Recognition Challenge image recognition contest  [11].
Additive manufacturing with machine learning In the literature, experiments have been made to monitor printing quality using ML and images extracted from the AM process. Scime and Beuth  [16]  focused on using data acquired by monitoring the melt pool to predict layers' quality. The printing quality was predicted by ML using filtered images. The acquisition of melt pool images requires the use of a highspeed camera to properly capture the physical phenomena. Thus, this experiment has generated 6 400 images per second (~13 GB per second), which are required to monitor the melt pool accurately, making real-time acquisition and analysis unrealistic. In another study, Scime and Beuth  [17]  used powder bed images taken post melting for quality assessments. These techniques reduce the input data to a reasonable size for realtime control.
Data processing with CNN For this research, Python 3. 7. 4 and Pytorch 1. 3. 1 were used. Calculations were performed on the supercomputer ARCCA using P100 GPU. The uses of graphics cards speed up the data processing time, making possible to classify on images in less than 0. 01s. The CNNs presented in section 2. 2 were used as a straight forward solution to classify the images using Pytorch  [19]. The CNNs training was done using the same hyperparameters for all. Their momentum was set to 0. 9 and the learning rate to 0. 001. To process those images, each CNN was trained twice, once with transfer learning (called pre-trained) and once without it (called untrained). This allows to compare the capacity of the CNNs to learn from scratch on the data. With transfer learning, a previous CNN had been trained on complex datasets and had learned to extract features from images.
5°, 21. 0° and 22. 2° assigned to the (11 ̅ 0), (110), (020) planes respectively  (Hindeleh and Johnson 1974; Ibbett et al. 2008; Karacan and Soy 2013; French 2014; Kafle et al. 2014; Kale et al. 2020 ) and were related to the cellulose II crystalline structure after mercerization  (Kafle et al. 2014 ). An additional peak is observed at 2 = 25. 4° and was assigned to the anatase phase of titanium dioxide (TiO2) which gives a white coloration to the V fiber  (Reddy et al. 2003). The XRD pattern of CA show a diffraction peak around 9. 1• attributed to the crystalline peaks of CA II  (Deus et al. 1991). In addition, a diffraction peak at around 19. 7•, which was commonly assigned to the less ordered or amorphous region of the cellulose chains  (Freire et al. 2006). Wavelength (cm -1 ) n (b-link) n (COC) n (CH) n (C=O) n (CO) n (OH) CA C F d (OH) d (C-H) A deconvolution procedure was applied on the XRD patterns according to the position of the different peaks defined previously, using the open software Fityk.
The Park model provides extremely good fits to the experimental data over the entire range of water activity for the all cellulosic fibers. The Park's model parameters were compared for the sorption and desorption isotherms for each fiber. Regardless the fiber nature, differences were obtained only for the Henry sorption. The sorption and desorption kinetic curves were analyzed by using the PEK model which has provided excellent fits for the all cellulosic fibers. Regarding characteristic times, similar trend was noticed for sorption and desorption which agree with the BET isotherm. Similar characteristic times   were found in sorption and desorption but different behaviors were observed in sorption and desorption for the slow process. This difference is linked to the presence of sorption sites and their accessibility.
Statistical analysis The Median test is used to analyse differences between IgG medians concentrations. The comparisons between IgG median concentrations in Zinder, Dosso and Gaya are performed to investigate the potential impact of SMC on antibody responses. Positive and negative cassette results as well as results between districts were compared. The comparison between IgG median concentrations are performed by Mann-Whitney test. Data are analysed with SPSS software version 16. 0. P-values ≤ 0. 05 are considered statistically significant.
Nucleic acid detection and sequencing A molecular study was performed in all sick animals (n = 13). Addi- CDV strains, including 375-bp sequences from all lineages described so far, was constructed using ClustalW and neighbour-joining algorithm, Kimura-2 parameter model (MEGAX software,  Kumar et al. , 2018). The consensus tree was generated from 1000 bootstrap resampling. Pairwise distances between sequences were also calculated using the Kimura-2 parameter model (MEGA X software,  Kumar et al. , 2018).
Statistical analysis A generalized linear model was developed to identify factors associated with the presence or absence of antibodies against CDV in badgers. The presence/absence of antibodies against CDV was the response variable, whereas geographical area, sex, age, death cause and sampling year were explanatory factors. A logistic link function was applied, and a binomial error distribution was assumed. Data were analyzed using SPSS 17. 0 (IBM, Chicago, IL, USA). Additionally, the 95% confidence interval (95% CI) was calculated for each year and the lower and upper limits were delimited  (Kohn & Senyak, 2021). Comparations among the years were performed by the Fisher's exact test using GraphPad Prism v. 8. 0 (GraphPad Software Inc, San Diego, CA, USA).
Antibodies against CDV in free-ranging badgers from 2008 to 2020 A total of 297 out of the 684 badgers (43. 4%) included in the serological study were positive by CDV ELISA. The mean annual seroprevalence ranged from 25% in 2020 to 63. 64% in 2015. Fisher's tests between years indicated numerous significant differences (Table  S3 ), suggesting important prevalence fluctuations through time with maxima in  2008, 2012, 2015, 2017 and 2018 and minima in 2014 and 2020  (Figure  4a ). The rate of seropositivity varied with geographic region (Figure  4b  Sequences are described following this naming scheme: GenBank accession number-location-species-year (see Table  S1 ). Red branches indicate a putative Iberian sub-lineage (long black branch). Sequences obtained in this work are highlighted with a black diamond (short black branch) tributions of each group can be found in Figure  S6.
total of 16 (19. 51%) individuals were positive for CDV by RT-PCR: 11 of 13 sick individuals and five of 69 wild carnivores found dead between November 2020 and June 2021 during passive surveillance. The positive cases included seven badgers (six of them were sick badgers), four foxes (all sick), four European martens (one of them was the sick marten and the remaining were from passive surveillance) and one stone marten (from passive surveillance). Sequences were obtained from 11 animals, revealing four different sequences with the following GenBank accession numbers: MZ169061, MZ605429, MZ605430 and MZ605431. The sequences showed 98% similarity with the closest canine morbillivirus strain (KY214447. 1), isolated from a wolf in Portugal in 2017. All four sequences clustered together in the phylogenetic tree (Figure 3), and they showed very low distance between them.
A total of 16 (19. 51%) individuals were positive for CDV by RT-PCR: 11 of 13 sick individuals and five of 69 wild carnivores found dead between November 2020 and June 2021 during passive surveillance. The positive cases included seven badgers (six of them were sick badgers), four foxes (all sick), four European martens (one of them was the sick marten and the remaining were from passive surveillance) and one stone marten (from passive surveillance). Sequences were obtained from 11 animals, revealing four different sequences with the following GenBank accession numbers: MZ169061, MZ605429, MZ605430 and MZ605431. The sequences showed 98% similarity with the closest canine morbillivirus strain (KY214447. 1), isolated from a wolf in Portugal in 2017. All four sequences clustered together in the phylogenetic tree (Figure 3), and they showed very low distance between them.
S2 ). The model is described by a set of ODE detailed in the Appendix (equation system S1). Since the model is deterministic, we can seed the simulations with a single exposed individual on day t 0. This model is solved numerically using the Numpy package in Python 3. 8. 3 to obtain a deterministic trajectory. Parameters were chosen with maximal likelihood given the observed daily mortality data, assuming that the daily mortality incidence is Poisson distributed, and independence between daily incidences (For more details, see the supplementary material). We also simulate a stochastic version of this model 1,000 times using a Gillespie algorithm with the package TiPS  (Danesh, Saulnier, et al. , 2020)  in R v. 3. 6. 3 (R Core Team, 2016). COVIDSIM: A non-Markovian deterministic model. Finally, we use an existing discrete-time model that has a similar structure to the continuous model mentioned above with an additional agestructure  (Sofonea et al.
, 2020). For comparison purposes, the generation time is set to be the same as in our DS model  (Nishiura, Linton, et al. , 2020) , and so the (non-exponential delay) from infection to death. However, two major differences are that this third model is not stochastic and does not allow for superspreading events. We restricted the parameter inference to the daily hospital mortality data described previously, with the main free parameter being the date of origin. We invite the reader to refer to  (Sofonea et al. , 2020)  for the scripts and further details on this approach.
Model calibration. To allow for model comparison and improve estimates, we fix some key parameters based on existing values, focusing on the French COVID-19 epidemic. Table  S1  lists all the parameters used along with key references. We compute the likelihood of the deterministic SEAIRHD model assuming a Poisson distribution of the daily mortality incidence data. Parameter inference with maximum likelihood is performed using the Nelder-Mead algorithm implemented by Scipy. minimize function in Python. The parameters used for the non-Markovian deterministic model correspond to the maximum likelihood set of parameters used in  (Sofonea et al. , 2020).
, 2005) , we observe a soar in the frequency of epidemic outbreaks dying out before reaching the threshold, which represent 75% of our simulations. When assuming deterministic and Markovian dynamics with our SEAIRHD model, the importation date of the first case of the epidemic wave that best fits the results is similar, with a delay of 63 days until daily mortality incidence reaches 100 cases. A stochastic implementation of the same model yields the same median delay of 63 days [95% CI: 56 -76 days], which is comparable to the DS model. However, consistently with earlier studies  (Grant, 2020; Sofonea et al. , 2020) , the ability of this memoryless model to capture the data is limited (Fig. S3  in the Appendix). Finally, the maximum likelihood parameter estimates from a deterministic but non-Markovian model, COVIDSIM  (Sofonea et al.
Acknowledgements Thomas Beneteau is supported by a doctoral grant from the  Ligue Contre le Cancer. We thank the  CNRS , the  IRD , and the  IRD itrop HPC (South Green Platform) at IRD  montpellier for providing HPC resources that have contributed to the research results reported within this paper. We also thank the ETE modelling team for discussion. A preprint version of this article has been reviewed and recommended by  Peer Community In Mathematical and Computational Biology  ( 10. 24072/pci. mcb. 100004). Thomas Beneteau et al. Peer Community Journal, Vol. 1 (2021), article e70 https://doi. org/10. 24072/pcjournal. 63.
| ME THODS We designed a retrospective study. All patients admitted to Toulouse University Hospital with COVID-19 during the period March 2020-April 2020, except patients admitted directly to an intensive care unit, were considered eligible. Patients were included according to the following criteria 1 : adults (over 18 years old) and laboratory confirmation of COVID-19 by real-time PCR, and /or chest computed tomography scan findings that meet the requirements for the diagnosis of COVID-19. Patients admitted directly to an intensive care unit were excluded. Demographic and biological data at inclusion were recorded. Hypertension was defined as the self-report of hypertension. As of admission, blood pressure was recorded five times a day, every day. Average daily blood pressure was taken into account.