dataesr/scientific-paragraphs-categorization huggingface dataset:
MODIS provides radiance measurements in 36 spectral bands between 0. 44 and 15 µm, with different spatial resolutions: 250 m (bands 1 and 2), 500 m (bands 3-7), and 1 km (bands 8-36)  (Bennouna et al. , 2013). MODIS aerosols retrievals are carried out separately over land and ocean using two independent algorithms  (Bennouna et al. , 2013). Numerous works such as  Kharol et al. (2011 ), El-Metwally et al. (2010) , and  Baddock et al. (2009)  have presented a comprehensive description of the Terra MODIS and have also outlined its operation. In this study, the MODIS data aboard the Terra (EOS AM) satellite were used: https://giovanni. gsfc. nasa. gov/giovanni/ (last access: 10 January 2020). The MODIS AOD data utilized in this work were collected for the 2002-2016 period over an 0. 5 • × 0. 5 • (latitude and longitude) area centred on each site in order to analyse local and regional aerosol loadings.
The Ozone Monitoring Instrument (OMI) data product OMSO2G is also used to analyse the transport of the SO 2 plume from the source region to South Africa. OMI is a nadir-viewing spectrometer that has been operating aboard the National Aeronautics and Space Administration (NASA) EOS AURA satellite since July 2004. The AURA satellite occupies a near polar sun-synchronous orbit at an altitude of 705 km  (Krotov et al. , 2016). A full technical description of the OMI data product is given in the OMI Algorithm Theoretical Basis Documents  (Barthia et al. , 2002). The OMSO2G products used in this work are selected at Level 2. 0, version 3, and are accessible from http://disc. sci. gsfc. nasa. gov/Aura/data-holdings/OMI/ (last access: 15 January 2020). The OMSO2G products are obtained from reflected solar radiation measured in spectral range from 310 to 340 nm.
The data used in this work are reprocessed with the new algorithm based on principal component analysis (PCA;  Li et al. , 2013) , which reduces the retrieval noise by 50 % compared with the previous version. The OMSO2G products are available for four vertical distributions in a sampling grid of 0. 125 • × 0. 125 • with respect to latitude and longitude  (Krotov et al. , 2016). The stratospheric layer (STL) data set is used for this investigation. Earlier works have reported that this data set is suitable for studying volcanic eruptions  (Sangeetha et al. , 2018; Li et al. , 2013; Krotov et al. , 2016). The estimated uncertainty in SO 2 values under cloud-free conditions for the four vertical distributions ranges from 0. 1 to 0. 4 DU and from 0. 7 to 0. 9 DU at equatorial and high latitudes respectively.
Previous works revealed that observations at UV wavelengths are helpful for the characterization of the optical and microphysical properties of the volcanic plume  (Porter et al. , 2002; Mather et al. , 2004; Sellitto et al. , 2017). The Angström exponent α P and turbidity β P of the volcanic plume are calculated at a specific wavelength pair, 380-1020 nm, given by Eqs. (  2 ) and (3). α P = - ln AOD P (λ 1 ) AOD P (λ 2 ) ln λ 1 λ 2 β P = AOD P (λ 1 ) • λ α 1 (2) As reported by  Sellitto et al. (2017) , the use of a spectral interval that is as large as possible leads to a decrease in the uncertainties of α P and β P. According to this research, the uncertainties of the derived α P and β P values are calculated as follows: σ α p =   1 ln λ 1 λ 2   σ AOD P (λ 1 ) AOD p (λ 1 ) 2 + σ AOD P (λ 2 ) AOD p (λ 2 ) 2 σ β p = λ α p 1 σ AOD P (λ 1 ) 2 + AOD p (λ 1 ) • ln λ 1 2 σ 2 α p (3) The spatio-temporal data analysis carried out between MODIS and sun photometer instruments helps both to detect the passage of the volcanic plume and its contribution to the total aerosol column variation over the selected sites.
Site Latitude  sites. In particular, the Comodoro site has the weakest correlation (0. 51) and the lowest MBE value of -9. 7 % ± 3. 2 % (Fig. 8e, f ). Previous studies have already pointed out the bias in the AOD data sets collected by MODIS and groundbased instruments in the Southern Hemisphere between 45 and 65 • S  (Zhang and Reid, 2006; Shi et al. , 2011; Lehahn et al. , 2010; Madry et al. , 2011; Toth et al. , 2013). Madry et al. (2011)  suggested that this bias could be due to the production of sea salt particles from the near-surface high winds occurring along this zonal band. Toth et al. (2013)  investigated the quality of MODIS data sets in this zonal band by comparing them with CALIOP and sun photometer observations. They showed that about 30 %-40 % of the observed bias with the ground-based observations could be attributed to cloud contamination.
ready. noaa. gov, last access: 5 January 2020) used in this publication. Review statement. This paper was edited by Petr Pisoft and reviewed by two anonymous referees. Financial support. The authors acknowledge the South African-French PROTEA programme (project no. 42470VA) and CNRS-NRF LIA ARSAIO (Atmospheric Research in Southern Africa and Indian Ocean) for supporting the research activities. This work was supported by the French-Argentine ECOS-Sud A16U01 project and the Université de la Réunion through the federation Observatoire des Milieux Naturels et des Changements Globaux (OMNCG) of the OSU-R (Observatoire des Sciences de l'Univers -La Reunion).
Acknowledgements. We thank  Eduardo Quel ,  Lidia Otero ,  Paulo Artaxo ,  Brent Holben ,  Salvador Jacobo ,  Stuart Piketh ,  Gillian Maggs-Kolling ,  Derek Griffith ,  Sivakumar Venkataraman , and their staff for establishing and maintaining the  Bariloche, Neuquén, Buenos Aires, São Paulo, Comodoro ,  Rio Gallegos ,  Gobabeb, Pretoria , and Durban sites used in this investigation (http://aeronet. gsfc. nasa. gov/,last access: 5  March 2020 ). The authors would like to thank the members of the  Servicio Meteorológico Nacional (SMN)  for their contribution to data acquisition over South America. The authors are grateful to  JICA (Japan International Cooperation Agency)  and  JST (Japan Science and Technology Agency)  for the funding support of  SAVER-Net  ( South American Environmental Risk-Network ).
C11-Bodipy staining Cells were seeded at 100,000 cells per well in 12-well plates. Once adherent, the cells were pre-treated with the lipophilic free-radical traps (Fer-1, Vit E) and iron chelators (DFO and CPX). The cells were then irradiated with 20 or 50 Gy. Twenty-four hours after irradiation, the cells were harvested, washed with phosphate-buffered saline (PBS), followed by staining with Bodipy TM 581/591 C11 (Thermo Fisher Scientific, D3861) (2 μM final concentration) in PBS, at 37 °C in the dark for 10 min. Cells were then washed with PBS before being incubated with Sytox Blue (1. 25 μL final concentration) in PBS. Samples were acquired with a BD LSRII flow cytometer (BD Biosciences) and results were analyzed with FlowJo 10. 4 software.
Statistical analysis of nuclear morphological data A hierarchical generalized linear mixed model (HGLMM) as implemented in Genstat v19 (VSN International (2017), Genstat for Windows 19th Edition, VSN International, Hemel Hempstead, UK. Web page: Genstat. co. uk) was fitted to the data. Fixed terms included the cell-type, the irradiation dose and their interaction; random terms included the biological replicate, the plates nested within the replicate and the subsamples nested within the plate. In the case of cell area, nuclear area, nuclear roundness and MN+ cells, an HGLMM was fitted considering a normal distribution for both fixed and random effects. In the case of number of objects and number of micronuclei, a Poisson distribution of the fixed effects and a Gamma distribution of the random effects were considered.
Acknowledgements We thank  Inge Bruggeman  for the sequencing of  K-Ras  and  H-Ras  mutations in the cell lines. We thank the  VIB Flow Cytometry Core , especially  Gert Van Isterdael , for assistance with the flow cytometry experiments. We thank the  VIB  and IRC core facilities for their excellent support. We thank  Wim Declercq  for helpful discussions and critical reading. S. A. holds a post-doctoral fellowship from  FWO (Flanders Research Organization). B. C. holds a Ph. D. fellowship from  FWO. K. V. holds a mandate for clinical and translational research funded by the  Foundation against Cancer (Stichting tegen Kanker) ,  Belgium. P. V. is a senior full professor at  Ghent University  and senior PI at the  VIB Center for Inflammation Research (IRC). Research in the Vandenabeele group  is supported by  Flemish grants (EOS MODEL-IDI, FWO Grant   30826052 ), FWO research grants (G.
Automatic Age Estimation (AAE) has attracted attention due to the wide variety of possible applications. However, it is a challenging task because of the large variation of facial appearance and several other extrinsic and intrinsic factors. Most of the proposed approaches in the literature use hand-crafted features to encode ageing patterns. Deeply learned features extracted by Convolutional Neural Networks (CNNs) algorithms usually perform better than hand-crafted features. The main contribution of this paper is an extensive comparative analysis of several frameworks for real AAE based on deep learning architectures. Different well-known CNN architectures are considered and their performances are compared. MORPH, FG-NET, FACES, PubFig and CASIA-web Face datasets are used in our experiments.
Motivations and paper organization Different shallow learning surveys for AAE exist  (Fu et al. , 2010; Ramanathan et al. , 2009). In fact, to the best of our knowledge, there is no comparative study that combines the most popular deep learning models, with the existing state-of-the-art CNN architectures for AAE. The main contributions of this work are as follows: • In this paper, an extensive comparative analysis of several frameworks for real AAE based on deep learning architectures is given. Several well-known CNNs and public datasets are used. The best configuration for each architecture based on Morph dataset is stated and different configurations are tested. • The used CNNs are pre-trained on ImageNet to solve object category classification. This first initialization leverages and transfers the knowledge from object recognition domain to facial age estimation domain.
An evaluation framework of knowledge transfer from face recognition task across AAE is performed to study the performance of knowledge transfer from related tasks. • The AAE from face images is a challenging topic because of a mixture of extrinsic and intrinsic factors. Thus, the robustness of the best architecture is evaluated under expression changes, "crossing" ethnicity and "crossing" gender. The structure of our proposed work is organized as follows: the next section illustrates the related work. Section 4 represents the global schema and the several studied CNN architectures. In Section 5, more details about the datasets and the different experiments are given. Then, the performance of real age estimation of the several frameworks are studied. After discussing the different results and presenting a comparison with the existing approaches, section 6 concludes the paper.
The active appearance model (AAM) is also used for face ageing by learning a linear model for shape and intensity from images and a set of landmarks  (Lanitis et al. (2002) ). Fifty parameters of the AAM are fed to a classifier and the problem is formulated as a regression problem. The BIF features have been investigated for age estimation in  (Han et al. (2013) ). The ageing subtly on faces are encoded using standard deviation (STD) operator. The normalization with the STD operator reveals local variation capturing vital ageing information like wrinkles and eyelid bags. A series of local descriptors and their combinations which fuse and exploit texture as LBP and SURF and local appearance-based descriptors as HOG have been evaluated under a diversity of settings and the extensive experiments carried out on two large databases: Morph and FRGC  (Huerta et al.
(2014) ). To better explore the connections between facial features and age labels, distance metric learning and dimensionality reduction are performed in addition to the traditional two-step framework of age estimation algorithms (ageing patterns learning + age estimator learning). Reducing the dimensionality of the extracted features can alleviate the over-fitting problem  (Chao et al. (2013) ). The number of samples for each age is not balanced in different datasets. To overcome the potential data imbalance problems, a label-sensitive concept and several imbalance analysis are introduced in  (Chao et al. (2013) ). Age estimation accuracy is improved through a combination of the proposed hybrid features and the hierarchical classifier. The wrinkle feature is extracted using a set of region-specific Gabor filters, each of which is designed based on the regional direction of the wrinkles, and the skin feature is extracted using a local binary pattern (LBP), capable of extracting the detailed textures of skin  (Choi et al.
Datasets for Automatic Age Estimation Several datasets have been used for AAE in the literature: Morph  (Wang et al. , 2015; Yi et al. , 2014; Niu et al. , 2016; Rothe et al. , 2016; Yang et al. , 2013) , FG-NET  (Wang et al. , 2015; Rothe et al. , 2016).
Age estimation The pre-trained CNNs for ImageNet classification task have an output softmax layer of 1000 channels, one for each of the object classes. However, the age estimation is a non-linear regression problem. We want to predict the age which is a continuous value rather than a set of discrete classes. Thus, the last softmax layer from each CNN architecture is removed and replaced with 1-layer neural network to learn the age regression function. In this work, the real age estimation is considered as a regression and not a classification problem. To avoid overfitting due to the small number of training data comparing to the high dimension of the features, a dropout layer is added before the last layer. The regression is learned by optimizing the mean squared error (MSE) loss function defined by: L = 1/N n i=1 (Y i -Ŷi ) 2 (1) where L is the average loss for all the training samples, Y i is the estimated age and Ŷi is the real age.
Knowledge transfer for AAE The used CNNs are pre-trained on ImageNet, this is not optimal as large scale face recognition datasets are now available and facial age estimation task can benefit from pre-training with face related tasks  (Antipov et al. (2017) ). Thus, an evaluation framework of knowledge    We use MORPH dataset, four tests have been realized: • training on Asian, Black, White and others and testing on Hispanic, • training on Asian, black, Hispanic and others and testing on White, • training on Asian, white, Hispanic and others and testing on black, • training on black, white, Hispanic and others and testing on Asian. The results of the different experiments are shown in Table 5. MAEs between 2. 62 and 4. 73 years are obtained. Testing on Hispanic or white gives very close MAEs, while testing on black gives the higher MAE.
Thus, we can empirically conclude that hispanic, white and asian races share some close ageing features. On the other hand, blacks have more unique ageing features that cannot be learned from other ethnicities. xvii The influence of crossing gender on AAE: patterns in adult human faces reflect the masculinization or feminization that occurs at puberty: larger jawbones, more prominent cheekbones and thinner cheeks and lips are the patterns of male faces that differentiate them from female faces  (Little et al. (2011) ). The influence of crossing gender on AAE based on shallow learning has been studied in  (Guo and Mu (2010) ). The average MAE changes to 8. 38 years in "cross gender" age estimation. It has been demonstrated that gender affects the AAE in shallow learning scenario. In this paper, we studied the cross-gender age estimation using CNN architecture trained on MORPH dataset.
CNNs require a lot of training data due to the large number of parameters in the model to learn. Contrariwise, not all the datasets used in our experiments contain enough images to train the deep CNNs. Besides, the training process is very time-consuming, it can take from hours to months on the optimization. Meanwhile, using a GPU with its parallel architecture can considerably reduce the computational time. The larger the number of GPUs, the less the computational time of training. To overcome these problems, a transfer learning strategy (Pan and Yang (2010) ) is set up with two steps:• pre-training step: the randomly initialized networks are first trained by a related task that owns enough labeled images, x.
configuration for each architecture based on Morph Dataset. From the previous evaluations, in case of No Transfer Learning, we can conclude that the Xception deep network is the best model followed by Incep-tionV3, ResNet, VGG19, and VGG16 respectively for AAE. In case of Transfer learning, VGG16 model is the best followed by VGG19, ResNet, Xception, and Incep-tionV3 respectively. In case of partial Transfer learning, ResNet model gets the best performance followed by, VGG19, VGG16, Xception, and InceptionV3 respectively. An illustration of some of the good and bad results of AAE from MORPH dataset is shown in Figure 2, first row. Computational Complexity. For the experiments, we used a Tesla K80 GPU with a 12 Gb memory and 61 Gb RAM. The computation complexity for the different methods is summarized in Figure 4.
It can be seen that, when Gaussian noise is less (variance is small), the Xception model can estimate accurately the human age. However, with the increasing of variance, the xiii accuracy of the age estimation get worse. In general, satisfactory results have been obtained by using the deep age estimator based on Xception architecture for the images added with Gaussian noise and the resulting MAE is varying from 2. 52 to 4. 63 for a variance from 0. 01 to 0. 1. On the contrary, this model is more sensitive to blur noise than Gaussian noise and the Xception based model shows an MAE varying from 2. 96 to 12. 73 for a blur length from 10 to 30 pixels. Thus, the age estimation framework based on Xception model is robust to small amounts of Gaussian noise and motion blur. 5. 5. Generalization of the pre-trained CNNs on MORPH to other datasetsTo evaluate the performance of the different CNN architectures trained on MORPH dataset, the model is tested on another dataset.
MORPH is a standard dataset and the images are taken in the same conditions. We tested the CNN architectures on FG-NET dataset which displays a big variability. The face images in FG-NET are collected from personal photographs. Thus, the images present different illuminations, different viewpoints and expressions. The results are shown in Table 7. Testing on FG-NET dataset without any fine-tuning presents an MAE varying from 10. 8 to 13. 2, VGG16 outperforms other architectures but the error increases by more than 7 years in the different tests. When a fine-tuning is applied, the accuracy of AAE increases and Xception CNN outperforms the other trained CNNs and achieves an MAE of 3. 6 years. The transition from ideal conditions of faces acquisition (MORPH dataset) to faces in the wild conditions (FG-NET dataset) shows an increased error rate, a fine-tuning improved considerably the results.
transfer from related task (face recognition) and from general task (ImageNet) for AAE is performed. Two more experiments are then realized. In the first experiment, the Xception model is pre-trained using the Public Figures Face (PubFig) dataset and in the second experiment, it is pre-trained using CASIA-Web face dataset. An MAE of 2. 01 years is obtained when pre-training with CASIA-WEB dataset and it outperforms the MAE of 2. 35 when pre-training with ImageNet. However, the MAE reaches 4. 07 years when pre-training with PubFig dataset. It can be explained by the fact that PubFig dataset contains less images (58K) than CASIA-Web face dataset (500k). Pre-training on ImageNet outperforms pre-training using PubFig on age estimation task. Xception has a large number of parameters (22 millions) and a lot of data is needed to train it.
Therefore, PubFig dataset does not contain enough data to train the upper layers of the deep network. Consequently, training the lower layers of Xception model using ImageNet recognizing shapes and sizes and refining the upper layers using faces achieves better results. Thus, AAE can benefit more from general task when several millions of images are used in pre-training than face related task when the face related task dataset is small. 5. 7. Comparison with existing methods of AAE from face images The proposed frameworks with well-known CNN architectures are compared with existing shallow and deep learning based methods tested on Morph II dataset (see Table 6). The state-of-the-art approaches based on handcrafted features are tested mainly on small datasets and few approaches are tested on Morph II.
frameworks based on CNN architectures. The framework based on Xception network outperforms the state-of-theart methods based on deep or shallow learning for automatic age estimation with an MAE of 2. 35 years when pre-trained on ImageNet and an MAE of 2. 01 when pretrained on CASIA-Web face dataset. The knowldge transfer evaluation demonstrates that AAE can benefit more from general tasks when several millions of images is used in pre-training than face related task when the face related task dataset is small. The layer-wise transfer learning evaluation demonstrates that the optimal number of layers to fine-tune on AAE task depends on the depth of the network, on its architecture and on its layers types. Using the best deep age estimator, we investigate the effects of gender, expression changes and ethnicity on AAE.
Comparison of the age estimation MAEs by the proposed CNN architectures and the state-of-the-art methods on MORPH II dataset. * The IMDB-WIKI used for pre-training. ** The CASIA-WEB Face used for pre-training. § The PubFig used for pre-training. † Half cleaned IMDB-WIKI used for pre-training. TL: transfer Learning, MV loss: mean-variance loss, DR: Dimensionality Reduc-.
The generalization of the pre-trained CNNs on Morph dataset to other dataset. To evaluate the performance of the different CNN architectures trained on Morph, the model is tested on FG-NET dataset. Patel, V. M. , Chellappa, R. , 2015. Unconstrained age estimation with deep convolutional neural networks. IEEE international conference on computer vision workshops, pp. 109-117. Liu, X. , Li, S. , Kan, M. , Zhang, J. , Wu, S. , Liu, W. ,. , Chen, X. , 2015. Agenet: Deeply learned regressor and classifier for robust apparent age estimation. IEEE International Conference on Computer Vision Workshops, pp.
The average number of images per subject is 4. However, the ethnicity and gender distributions are very unbalanced as shown in Table4. • FACES Dataset: is a database of facial expres- sions in younger, middle-aged and older men and women, conceived between 2005 and 2007 (Ebner et al. (2010)). It contains face images of 171 subjects with ages ranging from 19 to 80 years old. Each in- dividual has faces in two sets of six facial expression (neutrality, sadness, disgust, fear, anger, and happi- ness), resulting in a total of 2052 images. For more details, refer to Table 3. ). Album 2 of the dataset is used. It contains 55134 images of more than 13000 subjects, spanning from 2003 to late 2007. Ages range from 16 to 77 years. • FG-NET Dataset: is a public ageing database, released in 2004 (Panis et al.