dataesr/scientific-paragraphs-categorization huggingface dataset:
14B ). Each mineral phase is clustered each other, and the dolomite samples are well separated away from the ankerite ones. Furthermore, the dolomite and ankerite are located on the trendline between calcite and magnesite, and between calcite and siderite, respectively (Fig. 14B ). The sum of the PC1 and PC2 for the other datasets were 77. 04% and 72. 96%, respectively (Fig. 14C  and D ). The PCA plot using the net area shows well scattered among the carbonates, but magnesite and rhodochrosite are gathered from each other (Fig. 14C ). The last plot consisting of the LIBS total area and four elemental concentrations from pXRF (Fig. 14D ) has a similar distribution with the one using the total area only (Fig. 14B ). Therefore, this result shows that LIBS data using the total area is able to provide effective carbonate classification.
Conclusion This quantitative study aimed to compare chemical compositions with LIBS data using natural carbonate endmembers, and two synthetic mixtures of three carbonates in a two-component system. They were used to approach the quantitative relationship between their spectral characteristics and also between their mineralogical proportions. Based on the values of determination coefficient and predictive ability, we demonstrated that all the LIBS parameters for each element, especially the intensity or net area, are linearly correlated with the elemental concentrations acquired from a portable XRF device. Furthermore, the LIBS spectral ratios using Fe#-Mg/Mn and Fe/Ca-Mg/Mn confirmed clear separation of the single carbonate clusters from each other. While the calcite-ankerite mixture displayed a good level of correlation with both the Mg/Ca and the Fe/Ca ratio using the intensity and the total area method, the Mg/Ca ratio using the intensity method showed a higher linear tendency only in the calcite-dolomite mixtures due to low Fe content in both calcite and dolomite endmember.
Meanwhile, a significant result was obtained from the Fe/Ca ratio using the intensity and total area method with the different iron content in the ankeritedolomite mixtures. These quantitative results suggest that the spectral data using the intensity method from handheld LIBS are effectively applied to determine the chemical composition and discrimination of carbonate minerals. The linear correlations in the carbonate mixtures show potential for subsequent quantitative analysis for mineral mixtures in the field. In the future in this NEXT project, this spectral information will be applied to the core samples from the Elvira deposit, providing mineralogical and elemental information for both qualitative and quantitative analysis. The PCA using the region of interest from LIBS data could also be helpful to apply carbonate discrimination and quantification in carbonate mixtures.
We investigate the effect of an axial Poiseuille annular flow on the stability of Taylor vortices via numerical simulation using CFD Ansys Fluent software. The working conditions are identical to those of the Taylor-Couette experimental device of the LaSIE laboratory, where the inner cylinder is rotated. An incompressible fluid of density ρ= 998 kg/m 3 , with a kinematic viscosity  = 1. 004 * 10 m 2 /s at a temperature T= 19. 5 °C is considered. The geometrical parameters of the flow system are characterized by a height H=275 mm, a radius ratio η=0. 804, and an axial aspect factor Γ=45. 45. The axial Reynolds number and Taylor number are respectively in the ranges of 0 ≤ R ≤ 12 , and 0 ≤  ≤ 142. 25. Flow control is carried out according to two distinct protocols to bring out the effect of axial flow on the evolution of the Taylor vortex Flow (TVF).
Geometrical Configuration Numerical simulations are implemented on ANSYS Fluent software, with a structured hexagonal mesh (Fig. (  1 )) in the radial, azimuthal, and axial directions (r, θ, z). Several mesh configurations are tested. Comparison of the obtained numerical values for critical characterizing values (transition to TVF and WVF) in terms of Taylor and Reynolds numbers with those in literature allowed to validate the adopted mesh with 772,800 cells (13*200*275). Pressure is discretized based on a second-order scheme. A third-order MUSCL scheme is used to discretization of the momentum equations. The hydrodynamic field Pressure-Velocity is coupled using PISO scheme. The convergence criterion is fixed to 10 -5 , and the time to Δ = 0. 0002 s.
Surveillance system Implemented on 10 January 2020, the individual-based surveillance system for COVID-19 was designed to detect cases as early as possible (see  [3]  for a detailed description). Possible cases were isolated in one of the COVID-19 reference hospitals when possible or at home. These cases were interviewed using a standardised questionnaire that collected information on sociodemographic characteristics, clinical characteristics and history of exposure (including potential infectors). Data were entered into a secure web-based application (Go. Data, World Health Organization). Respiratory samples were taken from all possible cases and tested for SARS-CoV-2 using reverse transcription polymerase chain reaction (RT-PCR). From 17 to 29 January 2020, a possible case was defined either as a patient with a severe acute lower respiratory infection requiring admission to hospital and with a history of travel to or residence in Wuhan, China in the 14 days before symptom onset or a patient with an acute respiratory illness irrespective of severity and with a history of at-risk exposure, mainly with a confirmed case  [3].
Contact tracing Confirmed cases were kept in isolation and interviewed about any contacts that occurred during the time they were symptomatic and the day before symptom onset, in accordance with European recommendations  [14]. Based on their type of exposure, identified contacts were classified into three levels of exposure risk: negligible, low or moderate/high risk (Table ). Only contacts who developed symptoms compatible with COVID-19 were tested for SARS-CoV-2 using RT-PCR. If the test was positive, they were considered secondary confirmed cases and their contacts were then traced in the same fashion as a primary case. Following the procedures, only contacts with low or moderate/high risk were followed up (Table ). However, because the investigation teams were quickly overloaded in the exponential growth phase of the epidemic, most contacts (97%) who were identified and entered into the database were moderate/high-risk contacts.
Statistical analysis We first analysed contact tracing data for confirmed cases (hereafter called cases) and their contacts, who were entered into the database between 24 January 2020 and 30 March 2020. We defined an index case as a case whose detection initiated contact tracing. Therefore, secondary cases can become index cases if their contacts are traced. We described contact patterns between index cases and their traced contacts by constructing the corresponding contact matrix. We compared this to the age-specific contact matrix for the French population obtained from the COMES-F study performed in 2012  [16]. We analysed the differences in the age-specific mixing patterns using linear regression. We estimated the secondary clinical attack rate -i. e. , the proportion of symptomatic cases among the contacts of an index case  [17].
We investigated the factors associated with the risk of a contact becoming a case (i. e. , developing symptoms and testing positive) using multivariable logistic regression (Supplementary Text S2). Finally, we analysed all infector/infectee pairs using pairs identified through prospective contact tracing (pairs between an index case and a contact who became a case) and pairs identified through retrospective epidemiological investigations in Oise. We computed the mean number of secondary cases generated by each case based on contact tracing data (using the number of secondary cases observed among traced contacts of each index case) or retrospective data (where cases at the end of the observed chain of transmission were considered to have no secondary cases) (Supplementary Text S3). We also estimated the serial interval -i.
Description of contact tracing data Between 24 January and 30 March 2020, 6,082 contacts (6,028 unique individuals) of 735 cases were traced and entered into the database. The median age of the index cases was 50 years (interquartile range (IQR): 36-65) and 52% (384/734) were female. The median age of the contacts was 38 years (IQR: 21-55) and 56% (3,123/5,593) were female. Cases had 8. 3 contacts traced on average (median 4, range 1-146). We observed non-random contact patterns by age: the index cases tended to have more traced contacts of similar age (Pearson correlation coefficient (r) = 0. 32, p < 0. 01) (Figure  1A ). These patterns were consistent with age-specific contacts measured before the COVID-19 pandemic in the French general population  [16]  (r = 0. 80, p < 0. 01) (Figure  1B  and Supplementary Figure  S1 ).
The infector/infectee pairs followed a similar assortative pattern by age (r = 0. 29, p < 0. 01) (Figure  1C ). The 735 cases who had their contacts traced and entered into the database represented 5% (735/14,400) of the total number of COVID-19 cases over the study period. This proportion decreased over time, from 31% at the beginning of the epidemic to 2% in weeks 12-13 (week 11 was the last week of the individual.
COVID-19: coronavirus disease; PPE: personal protective equipment. Adapted from  [3]. based-surveillance at the national level) (Figure  2A ). The proportion of cases who had their contacts traced and entered into the database also varied by region (Figure  2B ). The most heavily affected regions (Ile-de-France and Grand Est), representing 54% of cases, had the lowest proportion of traced cases (1%). The secondary clinical attack rate decreased over time, from 6. 8% at the beginning of the epidemic to 1. 8% in weeks 12 to 13 (Figure  2C ), and varied by region, from 0% to 8. 7% (Figure  2D ). The secondary clinical attack rate increased with the age of the contact irrespective of sex, ranging from 4. 7% (95% CI: 3. 2-6. 7) for contacts aged 0-14 years to 12. 2% (95% CI: 8. 0-17. 7) for contacts who were 75 or older (Figure  3A ).
Discussion Using data from contact tracing and epidemiological investigations conducted during the initial phase of the COVID-19 pandemic in France, we characterised the secondary clinical attack rate and the factors associated with the risk of a contact becoming a case among the 6,082 contacts of the 735 index cases. We also analysed chains of transmission and estimated key parameters of spread among 328 infector/infectee pairs. Overall, 4. 1% of contacts identified and entered into the database became secondary cases. Since only symptomatic contacts were tested and some people infected by SARS-CoV-2 do not develop symptoms  [19, 20] , some asymptomatic infections were probably missed among contacts. As around 20% of SARS-CoV-2 infections are asymptomatic  [19, 20] , we hypothesise that about 60 asymptomatic secondary infections might have been missed among contacts and that about 5% of the contacts (rather than 4.
1%) might have been identified as secondary infections if asymptomatic contacts had been tested. In addition to the management of asymptomatic infections, the definition of a contact might vary between studies and therefore impact the estimates of secondary attack rates. Cheng et al. found a lower secondary clinical attack rate of 0. 7%  [10] , but the number of contacts identified per index case was much higher (27 on average, compared with eight in our study) and may have included more low-risk contacts than in our study; in our study, most contacts identified and recorded in the database were moderate/high-risk contacts. Conversely, in studies where 5-10 contacts were identified per index case during the early pandemic, the secondary attack rate (including asymptomatic patients) was 3. 7-11.
The mean number of secondary cases identified per index case was 0. 3-0. 9. These values are lower than the estimates for the reproduction number of SARS-CoV-2 in the absence of interventions, which is around 2. 5-3  [26]. This difference can be explained by a combination of factors: the number of secondary cases could be reduced due to contact tracing and isolation measures; asymptomatic infections are not observed in the study settings; and some symptomatic secondary cases might have been missed or not recorded in the database despite contact tracing. Other studies conducted in a similar context obtained similar results, with 0. 4-0. 7 secondary cases identified per index case  [6, 8, 13]. We estimated the dispersion parameter of the secondary cases' distribution to be between 0. 15 and 0.
Acknowledgements We are indebted to the work of public health staff in Santé publique France regional offices and headquarters and  Agences Régionales de Santé , who are involved in COVID-19 surveillance and greatly contributed to contact tracing, retrospective investigations, data collection and data management. We thank  Sylvie Haeghebaert ,  Caroline Vanbockstael  and the  Service de Santé des Armées  for contributing to retrospective investigations in Oise, and  Daniel Lévy-Bruhl  and  Sibylle Bernard-Stoecklin  for useful discussions. We thank all patients and contacts involved in the study as well as the medical staff. Funding statement: This work was supported by the  LabEx Integrative Biology of Emerging Infectious Diseases (IBEID)  (Grant Number  ANR-10-LABX-62-IBEID ),  Santé Publique France , project  Theracov , the  INCEPTION  project ( PIA/ANR-16-CONV-0005 ), the  European Union 's  Horizon 2020 research and innovation program  under grants  101003589  (RECOVER) and 874735 (VEO), and the  National Research Agency (ANR)  through the  ANR-Flash call  for  COVID-19  (Grant  ANR-20-COVI-0018 ).
Confirmed and traced cases and proportion of confirmed cases with contacts traced and in database by week B. Proportion of confirmed cases with contacts traced and in database by region C. Secondary clinical attack rate by week D. Secondary clinical attack rate by region Secondary clinical attack rate and factors associated with the risk of a contact of a COVID-19 case to become a case. France, 24 January 2020-30 March 2020 (n = 735 index cases; 6,028 contacts; 248 secondary cases) A. 5 1. 0 Proportion of traced cases Proportion of traced cases 0. 2 0. 4 0. 6 0. 8 0. 0 IDF GES BFC COR PAC HDF ARA PDL MAR NOR NAQ CVL OCC BRE RUN MAY Region 0. 12 0. 12 Secondary clinical attack rate 0. 02 0. 04 0. 06 0. 08 0. 10 Secondary clinical attack rate 0. 02 0. 04 0. 06 0. 08 0. 10 0. 00 0. 00 W03-W08 W09 W10 W11 W12-W13 MAR MAY RUN NOR COR ARA CVL OCC BFC PDL IDF BRE HDF PAC GES NAQ Week 2020 Region of index case COVID-19: coronavirus disease; W: week.
Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recently emerged is deepfake. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable. This paper presents a survey of algorithms used to create deepfakes and, more importantly, methods proposed to detect deepfakes in the literature to date. We present extensive discussions on challenges, research trends and directions related to deepfake technologies.
While some deepfakes can be created by traditional visual effects or computer-graphics approaches, the recent common underlying mechanism for deepfake creation is deep learning models such as autoencoders and generative adversarial networks, which have been applied widely in the computer vision domain  [2] -  [4]. These models are used to examine facial expressions and movements of a person and synthesize facial images of another person making analogous expressions and movements  [5]. Deepfake methods normally require a large amount of image and video data to train models to create photo-realistic images and videos. As public figures such as celebrities and politicians may have a large number of videos and images available online, they are initial targets of deepfakes. Deepfakes were used to swap faces of celebrities or politicians to bodies in porn images and videos.
As the democratization of creating realistic digital humans has positive implications, there is also positive use of deepfakes such as their applications in visual effects, digital avatars, snapchat filters, creating voices of those who have lost theirs or updating episodes of movies without reshooting them  [14]. However, the number of malicious uses of deepfakes largely dominates that of the positive ones. The development of advanced deep neural networks and the availability of large amount of data have made the forged images and videos almost indistinguishable to humans and even to sophisticated computer algorithms. The process of creating those manipulated images and videos is also much simpler today as it needs as little as an identity photo or a short video of a target individual. Less and less effort is required to produce a stunningly convincing tempered footage.
Recent advances can even create a deepfake with just a still image  [15]. Deepfakes therefore can be a threat affecting not only public figures but also ordinary people. For example, a voice deepfake was used to scam a CEO out of $243,000  [16]. A recent release of a software called DeepNude shows more disturbing threats as it can transform a person to a non-consensual porn  [17]. Likewise, the Chinese app Zao has gone viral lately as less-skilled users can swap their faces onto bodies of movie stars and insert themselves into well-known movies and TV clips  [18]. These forms of falsification create a huge threat to violation of privacy and identity, and affect many aspects of human lives. Finding the truth in digital domain therefore has become increasingly critical. It is even more challenging when dealing with deepfakes as they are majorly used to serve malicious purposes and almost anyone can create deepfakes these days using existing deepfake tools.
III. DEEPFAKE DETECTION Deepfakes are increasingly detrimental to privacy, society security and democracy  [55]. Methods for detecting deepfakes have been proposed as soon as this threat was introduced. Early attempts were based on handcrafted features obtained from artifacts and inconsistencies of the fake video synthesis process. Recent methods, on the other hand, applied deep learning to automatically extract salient and discriminative features to detect deepfakes  [56] ,  [57]. Deepfake detection is normally deemed a binary classification problem where classifiers are used to classify between authentic videos and tampered ones. This kind of methods requires a large database of real and fake videos to train classification models. The number of fake videos is increasingly available, but it is still limited in terms of setting a benchmark for validating various detection methods.
To address this issue, Korshunov and Marcel  [58]  produced a notable deepfake dataset consisting of 620 videos based on the GAN model using the open source code Faceswap-GAN  [39]. Videos from the publicly available VidTIMIT database  [59]  were used to generate low and high quality deepfake videos, which can effectively mimic the facial expressions, mouth movements, and eye blinking. These videos were then used to test various deepfake detection methods. Test results show that the popular face recognition systems based on VGG  [60]  and Facenet  [40] ,  [61]  are unable to detect deepfakes effectively. Other methods such as lipsyncing approaches  [62] -  [64]  and image quality metrics with support vector machine (SVM)  [65]  produce very high error rate when applied to detect deepfake videos from this newly produced dataset.
The CFFN encompasses several dense units with each unit including different numbers of dense blocks  [79]  to improve the representative capability for the fake images. The number of dense units is three or five depending on the validation data being face or general images, and the number of channels in each unit is varied up to a few hundreds. Discriminative features between the fake and real images, i. e. pairwise information, are extracted through CFFN learning process. These features are then fed into the second phase, which is a small CNN concatenated to the last convolutional layer of CFFN to distinguish deceptive images from genuine. The proposed method is validated for both fake face and fake general image detection. On the one hand, the face dataset is obtained from CelebA  [80] , containing 10,177 identities and 202,599 aligned face images of various poses and background clutter.
Five GAN variants are used to generate fake images with size of 64x64, including deep convolutional GAN (DC-GAN)  [81] , Wasserstein GAN (WGAN)  [82] , WGAN with gradient penalty (WGAN-GP)  [83] , least squares GAN  [84] , and progressive growth of GAN (PGGAN)  [85]. A total of 385,198 training images and 10,000 test images of both real and fake ones are obtained for validating the proposed method. On the other hand, the general dataset is extracted from the ILSVRC12  [86]. The large scale GAN training model for high fidelity natural image synthesis (BIGGAN)  [87] , self-attention GAN  [88]  and spectral normalization GAN  [89]  are used to generate fake images with size of 128x128. The training set consists of 600,000 fake and real images whilst the test set includes 10,000 images of both types.
A recurrent convolutional model (RCN) was proposed based on the integration of the convolutional network DenseNet  [79]  and the gated recurrent unit cells  [96]  to exploit temporal discrepancies across frames (see Fig. 4 ). The proposed method is tested on the FaceForensics++ dataset, which includes 1,000 videos  [97] , and shows promising results. Likewise, Guera and Delp  [98]  highlighted that deepfake videos contain intra-frame inconsistencies and temporal inconsistencies between frames. They then proposed the temporal-aware pipeline method that uses CNN and long short term memory (LSTM) to detect deepfake videos. CNN is employed to extract frame-level features, which are then fed into the LSTM to create a temporal sequence descriptor. A fully-connected network is finally used for classifying doctored videos from real ones based on the sequence descriptor as illustrated in Fig.
The eye blinking shows strong temporal dependencies and thus the implementation of LSTM helps to capture these temporal patterns effectively. The blinking rate is calculated based on the prediction results where a blink is defined as a peak above the threshold of 0. 5 with duration less than 7 frames. This method is evaluated on a dataset collected from the web consisting of 49 interview and presentation videos and their corresponding fake videos generated by the deepfake algorithms. The experimental results indicate promising performance of the proposed method in detecting fake videos, which can be further improved by considering dynamic pattern of blinking, e. g. highly frequent blinking may also be a sign of tampering. 2) Visual Artifacts within Video Frame: As can be noticed in the previous subsection, the methods using temporal patterns across video frames are mostly based on deep recurrent network models to detect deepfake videos.