dataesr/scientific-paragraphs-categorization huggingface dataset:
4) QUANTITATIVE RESULTS For quantitative evaluation, we have embedded synthetic test patterns with different intensity levels in a true anomaly-free crop SAR image. The fake anomaly map results are represented in Fig. 8. The values are based on true intensities characterizing the dataset with a decreasing significance from top to bottom. In this figure, anomaly detection maps are clipped at 10% of the higher value because some anomalies have a value close to the background. This makes the detection harder; thus, we reduce the dynamic range to have a better representation. One advantage of autoencoder-based detection is detecting abnormal areas of low intensities (left cross), contrary to the RX detector. The proposed A X E method outperforms all the other ones and even for the detection of low-intensity patterns.
PKR has been the target of strong diversifying selection and unusual gene duplication events in bats The scarcity of bat genome sequences limits the study of their immunity, their virus-host interface, and evolutionary history. To increase the robustness of our evolutionary analyses of bat PKRs, we sequenced and cloned additional coding sequences from 15 additional bat species (see Materials and Methods). Overall, 33 bat orthologous sequences of PKR have been included in our positive selection analyses, spanning 62 million years of evolution  (8). We compared models that disallow positive selection (models M1 and M7) to those allowing for positive selection (M2 and M8) using the Phylogenetic Analysis by Maximum Likelihood (PAML) Codeml package  (18). We found that PKR has evolved under strong and recurrent positive selection during bat evolution, leading to significant adaptive signatures at both the gene and the codon levels (PAML codeml M1 versus M2 and M7 versus M8, P = 4.
Collection of PKR orthologous sequences To complete our dataset, orthologous coding sequences of bat PKR were retrieved from GenBank by BLASTn searches of the "Nucleotide," "RefSeq Genome," "Transcriptome Shotgun Assembly," and "Whole-Genome Shotgun Contigs" databases, using the little brown bat (Myotis lucifugus) RefSeq coding sequence as query. In the case of unassembled bat genomes, PKR coding sequence was predicted from the genome contigs using Augustus  (47)  and GeneWise  (48)  with the little brown bat RefSeq protein as reference. In total, 19 bat PKR sequences were retrieved from public databases. PKR coding sequences from primates (n = 29), rodents (n = 25), artiodactyls (n = 23), and carnivores (n = 19) were obtained by tBLASTn searches of the Nucleotide database from GenBank using human (Homo sapiens), mouse (Mus musculus), cow (Bos taurus), and dog (Canis lupus familiaris) PKR protein sequence as queries, respectively.
Phylogenetic and positive selection analysis of PKR orthologous sequences PKR orthologous codon sequences were aligned for each mammalian group separately using the program PRANK  (49) , and the alignments were manually curated. We then built a phylogenetic tree, using the maximum likelihood method implemented in PhyML program  (50). Selection of the best substitution model was performed with the Smart Model Selection  (51)  program in PhyML and was always: GTR + G + I. Node statistical support was computed through 1000 bootstrap replicates. The detection of recombination events was assessed with the Genetic Algorithm for Recombination Detection (GARD) implemented in the Hypothesis Testing using Phylogenies (HyPhy) package  (52). For positive selection analyses, models that disallow positive selection (models M1 and M7) were compared to those allowing for positive selection (M2 and M8) using the PAML Codeml package  (18) , with the following parameters: codon frequencies F61 and F3x4 and starting omega  (dN/dS ratio) of 0.
Last, we determined whether and how PKR experienced episodic selection during bat and mammalian evolution, using the branchspecific analysis aBSREL  (19) , implemented in the HyPhy package. This program allows testing the significance of positive selection and quantifying the dN/dS ratio for each branch independently. Sequences from perissodactyls (n = 3) and proboscidean (n = 1) were also analyzed. Tree visualization and annotation were performed with Interactive Tree Of Life webserver (https://itol. embl. de/).
Following PCR amplification, all PCR products from cDNA and gDNA were cloned into the pMiniT 2. 0 Vector using the NEB Cloning Kit (NEB) and sequenced to ensure sequencing of a single DNA molecule. Phylogenetic reconstruction of the PKR paralogs followed the previously described phylogenetic analysis method. We combined different methods to map and predict the EIF2AK2 locus in the M. velifer genome. First, we performed a BLASTN search (cutoff of 10 -05 ) with PKR cDNA sequences from related Myotis species to identify the canonical locus and localization of EIF2AK2 gene copies. Second, we aligned the protein and RNA transcript sequences of PKR from related bat species on the M. velifer genome using the Fast Statistical Alignment software  (53). Third, we integrated RNA-seq data (see methods below), by mapping the RNA-seq reads using HISAT2 (v.
Protein structure prediction and docking models The 3D protein structures of D. rotundus and M. myotis PKR kinase domain, as well as the EPTV K3 protein, were predicted using the Iterative Threading ASSEmbly Refinement (I-TASSER) server  (57). The best model was carefully chosen on the basis of the C score, which assesses the quality of the models. The inferred protein structure of PKR was visualized and designed with Swiss-PdbViewer software  (58). Computational docking of bat PKR and EPTV K3 was performed to predict the complex structure between both proteins, using HDOCK webserver  (26). This software uses a fast Fourier transform-based search strategy to model different potential binding means between the proteins, and then, each binding mode is evaluated using the scoring function ITScorePP.
Blots were visualized using the Image Lab Touch Software (version 2. 0. 0. 27, ChemiDoc Imaging System from Bio-Rad) or film. In HeLa-KO cells, protein expression was assayed with 400,000 cell per well in six-well plates and transfected with 1. 4 g of the indicated PKR and viral antagonist-expressing plasmids. Cells were lysed after 48 hours with 1% SDS in phosphate-buffered saline (VWR), and then, proteins were separated on 4 to 12% Mini-PROTEAN GTX polyacrylamide gel (Bio-Rad) and transferred to nitrocellulose membranes. Proteins were probed with mouse anti-FLAG (1:5000; Sigma-Aldrich, F3165) or anti--actin as loading control (1:10,000; Sigma-Aldrich, A5441) and then with anti-mouse immunoglobulin G peroxidase-conjugated secondary antibody (1:10,000; Sigma-Aldrich, cat. A9044). Proteins were visualized as described above.
Acknowledgments: We are particularly grateful to the Poitou-Charentes association as well as the volunteers and field workers who have helped us during the field sessions:  V. Alt ,  M. Bely ,  G. Chagneau ,  M. Dorfiac ,  S. Dufour ,  C. Gizardin ,  G. Leblanc ,  M. Leuchtmann ,  E. Loufti ,  A. Le Guen , and  L. Trebucq. We thank  B. Larsen ,  M. Bucci , and  R. Ledgister  for the help with M. velifer sample collection; Pima County, AZ,  USA  for sampling permission; and  Dovetail Genomics  for sequencing. For the advices and the RNA-seq analysis that we used for the PKR transcripts, we thank  M. Sémon ,  M. Cariou ,  C. Rey ,  C. Dechaud , and  R. Bulteau , the students from the  Master UE NGS  and the  Master Biosciences, École Normale Supérieure de Lyon ,  Université Claude Bernard Lyon 1, Université de Lyon ,  69342 Lyon Cedex 07 , France; as well as  B.
Funding: D. P. and L. E. are supported by the  ANR LabEX ECOFECT  [ ANR-11-LABX-0048  of the  Université de Lyon , within the  program Investissements d'Avenir  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency ]. D. P. , L. E. , and S. J. are supported by the  French Agence Nationale de la Recherche (ANR) , under grant  ANR-20-CE15-0020-01  (project " BATantiVIR "). L. E. and D. E. are supported by a grant from the joint program between the  CNRS  and the  University of Arizona. L. E. is further supported by the  CNRS  and by grants from the  French Research Agency on HIV and Emerging Infectious Diseases ANRS/MIE  (nos. ECTZ19143  and  ECTZ118944 ). D. P. is supported by the  CNRS , the  European Regional Development Fund (ERDF) , and the  ANR EBOFAC. S. J. is also supported by the  Fondation L'Oréal-Unesco "For Women In Science.
, C. V. , and  C. M. C. Luciferase  assays: S. J. , C. Z. , and C. P. Infectivity assays: C. Z. , C. D. L. M. M. , and  G. B. Study  coordination and supervision: L. E. and D. P. Further supervision: S. J. , S. R. , and N. C. E. Funding: S. J. , L. E. , D. E. , D. P. , A. C. , S. R. , and N. C. E. Writing-original draft: S. J. Writing-first editing: L. E. and D. P. Writing-review and editing: All the authors. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. All de novo PKR sequences were deposited in GenBank under accession numbers  OP006534  to OP006561. Dataset for Fig. 1 with alignments and phylogenetic trees is publicly available at https://figshare.
The NN consists of a supervised two-layer pattern recognition network  (Kothari and Oh, 1993)    Table  1. List of IASI wavenumbers selected for the training of the cloud detection neural network. IASI wavenumber (cm -1 ) 826. 00, 827. 50, 861. 50, 865. 50, 866. 25, 869. 25, 871. 25, 874. 75, 877. 00, 878. 50, 880. 00, 883. 75, 885. 75, 887. 25, 891. 25, 894. 25, 897. 75, 899. 75, 901. 50, 902. 50, 905. 00, 994. 50, 996. 25, 999. 50, 1001. 50, 1004. 75, 1006. 50, 1009. 75, 1011. 50, 1014. 50, 2145. 50, 2147. 25, 2150. 00, 2152. 50, 2153. 50, 2157. 25, 2158. 25, 2161. 75, 2164. 75, 2166. 75, 2169. 25, 2172. 75, 2174. 25, 2176. 25, 2177. 75  backpropagation. For the training, the dataset was divided randomly into three parts: (1) the training set (95 %), (2) a validation set (4 %) and a test set (1 %).
, 2014)  datasets. We also include a comparison with the Aqua/AIRS L2 (AIRS+AMSU) V7. 0 product, despite the different overpass time with IASI (01:30 and 13:30 LT; AIRS Project, 2019). A short description of each cloud product is provided in Table  2. The IASI-L2, the CIRS-LMD and the L1C-AVHRR are also briefly described in the introduction. The three CDRs are based on different retrieval approaches (see Table  2  and references therein). In addition to a cloud mask, they also include a full cloud characterization. The data are provided at a resampled resolution of 0. 05 • × 0. 05 • grid for the CLARA-A2. 1 and the ESA Cloud_cci and at 0. 1 • × 0. 1 • for PATMOS-x. The AIRS-AMSU L2 product integrates cloud top level information and the cloud effective fraction. The intercomparison is performed over the 2016 data, except for the IASI-L2 product, which is derived from 2020 and the AIRS-AMSU products from 2015 (as AMSU failed in September 2016).
It also differentiates cloud from dust plumes fairly well. While the agreement with the IASI-L2 cloud product was generally very good, with a correspondence of about 87 % overall, differences have been reported over some regions, especially above seas over the subtropical gyres and in the southern Pacific and the southern Atlantic, but without being able to yield a better performance for either product. The agreement with other existing products was generally good in the main cloud regimes, but with sometimes large differences in the mean cloud amount (up to 10 % on average), especially with the three AVHRR cloud climate data records and with the CIRS-LMD IASI cloud retrieval algorithm suite. Given its good performances, the IASI NN cloud product is planned to be implemented in the near future in the Artificial Neural Network for IASI (ANNI)  (Franco et al.
Acknowledgements. IASI has been developed and built under the responsibility of the  Centre National d'Études spatiales (CNES, France). It is flown on board the Metop satellites as part of the  EU-METSAT Polar System. The IASI L1C data are received through the EUMETCast near-real-time data distribution service. Simon Whitburn  is grateful to the  ERC  for funding his research work. Lieven Clarisse  is a research associate ( Chercheur Qualifié ) supported by the  Belgian F. R. S. -FNRS. Cathy Clerbaux is grateful to  CNES for scientific collaboration  and financial support. We acknowledge the use of imagery from the  NASA  Worldview application (https://worldview. earthdata. nasa. gov),part of the NASA Earth Observing System Data and Information System (EOSDIS). Financial support. This research has been supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no.
Code availability. The analysis codes can be made available upon request to the corresponding author. The MATLAB function containing all the training variables is provided in the Supplement along with an example code showing how to run the network. Data availability. The daily IASI NN outputs will be made freely available for all users through the IASI-FT website: https://iasi-ft. eu/ (last access: 5 September 2022; Metop-A: https://doi. org/10. 5194/amt-15-6653-2022 Atmos. Meas. Tech. , 15, 6653-6668, 2022.
2020 , Bai et al. 2019 , Kassounian et al. 2019 , Fabbro et al. 2018 , Gill et al. 2018 , Li et al. 2017 , Gebran et al. 2016 , Paletou et al. 2015a, b). DL is an ML method based on deep artificial neural networks (ANN) that does not usually require a specific statistical algorithm to predict a solution but it is rather learned by experience and thus require a very large dataset  (Zhu et al. 2016)  for training in order to perform properly. An overview of the automated techniques used in stellar parameter determination can be found in the study of  Kassounian et al. (2019). We will mention some of the recent studies that involve ML/DL. The increase of the computational power and the large availability of predefined optimized ML packages (in e. g. Python, C++, and R) have allowed astronomers to shift from classical techniques to ML when using large data.
(2020)  introduced a DL method, SPCANet, which derivedT eff and g log and 13 chemical abundances for LAMOST Medium-Resolution Survey data. These authors found abundance precision up to 0. 19 dex for spectra with a signal-to-noise ratio (SNR) down to ~10. The results of SPCANet are consistent with those from other surveys, such as APOGEE, GALAH, and RAVE, and are also validated with the previous literature values including clusters and field stars. Guiglion et al. (2020)  derived the atmospheric parameters and abundances of different species for 420165 RAVE spectra. They showed that CNN-based methods provide a powerful way to combine spectroscopic, photometric, and astrometric data without the need to apply any priors in the form of stellar evolutionary models. More recently, Landa and Reuveni (2021) introduced a multi-layer CNN to forecast solar flare events probability occurrence of M and X classes.
Training spectra Our learning or training databases (TDB) are constructed from synthetic spectra for stars having effective temperature between 7,000 and 10,000 K, and the wavelength range of 4,450-5,000 Å. This range was selected because it is in the visible domain and contains metallic and Balmer lines sensitive to all stellar parameters (T eff , g log , [M/H], v i sin e ), especially for the spectra types selected in this work. This region is also insensitive to microturbulent velocity which was adopted to be = / ξ 2 km s t based on the work of  Gebran et al. (2016 Gebran et al. ( , 2014)). Surface gravity, g log , is selected to be in the range of 2. 0-5. 0 dex. Pro- jected rotational velocity, v i sin e , is calculated between 0 and 300 km s 1. The metallicity, [M/H], is in the range of -1.
We have used the new opacity distribution function in the calculations  (Castelli and Kurucz 2003)  as well as a mixing length parameter of 0. 5 for ≤ ≤ T 7,000 K 8,500 K eff , and 1. 25 for ≤ T 7,000 K eff  (Smalley 2004 ). We have used  Hubeny and Lanz (1992)  SYNSPEC48 synthetic spectra code to calculate all normalized spectra. The adopted line lists are detailed in the study of  Gebran et al. (2016). This list is mainly compiled using the data from Kurucz gfhyperall. dat², VALD³, and the NIST⁴ databases. Finally, the resolving power is simulated to = R 60,000. This value falls in the range between low and high resolution spectrographs. The technique that will be shown in the next sections can be used for any resolution. The construction and the size of the TDB will be discussed in Section 5.
The use of synthetic spectra in ML to constrain the stellar parameters has shown to suffer from the so-called synthetic gap  (Fabbro et al. 2018 , Passegger et al. 2020). This gap refers to the differences in feature distributions between synthetic and observed data. We have decided to limit our work to synthetic data for two reasons: first we would like to remove the hassle of the data preparation steps (data reduction, flux calibration, flux normalization, radial velocity correction, and so on), and second because our intention is to find the strategy and technique that should be adopted in ML for deriving stellar parameters. We are working on a future paper that deals with the architecture of the network as well as the choice of the kernel sizes and the number of neurons. Combining the best strategy to constrain the hyperparameters (this manuscript) as well as the most optimal architecture (future studies) will allow us to use a combination of synthetic and observational data in our training database.
Data preparation The TDB contains N spectra spectra that span the wave- length range of 4,450-5,000 Å. Having a wavelength step of 0. 05 Å, this results in = N 10,800 λ flux points per spectrum. The TDB can then be represented by a. A colour map of a subsample of M is displayed in Figure  1. Although the syn- thetic spectra are normalized, some wavelength points could have fluxes larger than unity. This is due to the noise that is incorporated during the so-called data augmentation procedure, which will be explained in Section 4. 1. 1. Training a CNN using the M matrix is time con- suming, especially if one should use a larger wavelength range or a higher resolution. For that reason, we have applied a dimensionality reduction technique, i. e. principal component analysis (PCA), in order to reduce the size of the training TDB as well as the size of the validation, test, and noisy synthetic data.
Although this step is optional, we recommend its use whenever the data can be represented by a small number of coefficients. The PCA can reduce the size of each spectrum from N λ to n k. The choice of n k depends on the many parameters, the size of the database, the wavelength range, and the shape of the spectra lines. As a first step, we need to find the principal components, and to do so, we proceed as follows. The matrix M is averaged along the N spectra -axis and the result is stored in a vector M ¯. Then, we calculate the eigenvectors ( ) λ e k of the variance-covariance matrix C defined as ( ) ( ) = - ⋅ - C M M M M ¯¯, T (1) where the superscript "T" stands for the transpose operator. C has a dimension of × N N λ λ. Sorting the eigenvectors of the variance-covariance matrix in decreasing magnitude will result in the "principal components.
" Each spectrum of M is then projected on these principal com- ponents in order to find its corresponding coefficient p jk defined as ( ) = - ⋅e p M M ¯. jk j k (2) The choice of the number of coefficient is regulated by the reconstructed error as detailed in the study of  Paletou et al. (2015a) : ( ) ⎛ ⎝ ⎜ | | ⎞ ⎠ ⎟ = + ∑ - = e E k M p M M ¯. k k jk k j j max 1 max (3) We have opted to a value for n k that reduces the mean reconstructed error to a value of <0. 5%. As an example, using a database of 25,000 spectra with stellar parameters ranging randomly between the values in Table  1  requires less than seven coefficients to reach an accuracy <1%, and a value of Applying the same procedure to all our TDB and taking the maximum value to be used for all, we have adopted a constant value for = n 50 k.
This value takes into account all the databases that will be dealt with in this work, especially that some will be data augmented as will be explained in Section 4. 1. 1. This means that instead of training a matrix having a dimension of × N N λ spectra , we are using one with dimension of × N n k spectra , with ≪ n N k λ. In that case, our new data consist of a matrix containing the coefficients that are calculated by projecting the spectra on the n k eigenvectors. This projection procedure over the principal components is then applied to the validation, test, and noisy spectra datasets. , the spectra can be reconstructed with more than 99. 5% accuracy.
DL: ANN This section begins with a brief description of supervised⁵ learning. Given a data set ( ) X Y , , the goal is to find a function f such that ( ) f X is as "close" as possible to Y. For example, Y could be the effective temperature or sur- face gravity and X the corresponding spectra. This "clo- seness" is typically measured by defining a loss function ( ( ) ) L f X Y , that measures the difference between the predicted and actual values. Therefore, the goal of the learning process is to find f that minimizes L for a given dataset ( ) X Y ,. Ultimately, the success of any learning method is assessed by how well it generalizes. In other words, once the optimal f is found for the training set ( ) X Y , , and given another data set ( ) U V , , how close is ( ) f U to V. One of the most successful methods in tackling this kind of problem is ANN, a subset of ML.
After the network architecture is chosen, the weights are initialized, then a variant of gradient descent is applied to the training data. Gradient descent changes the parameters iteratively, at a certain rate proportional to their gradient, until the loss value is sufficiently small  (Ruder 2016). The proportionality constant is called the learning rate. While this process is wellknown, there is to date no clear prescription for the choice of different components. The main difficulty arises from the fact that the loss function contains multiple minima with different generalization properties. In other words, not all minima of the loss function are equal in terms of generalization. Which minimum is reached at the end of training phase depends on the initial values chosen for the weights, the optimization algorithm used, including the learning rate and the training dataset  (Zhang et al.
CNN A CNN is a multi-layer network where at least one of the layers is a convolution layer  LeCun (1989). As the name suggests, the output of a convolution layer is the result of a convolution operation, rather than matrix multiplication, as in feedforward layers, on its input. Typically, this convolution operation is performed via a set of filters. CNNs have been very successful in image recognition tasks  (Yim et al. 2015). Most commonly, CNNs are used in conjunction with pooling layers. In this work, since the input to the CNN has been already processed with PCA to reduce the dimension of the training database, we decided to omit pooling layers in our work. Even though CNNs have been mostly used for processing image data, which can be viewed as 2D grid data, they can also be used for 1D data as well.