dataesr/scientific-paragraphs-categorization huggingface dataset:
Specifically, 1-kb sequence upstream and downstream of TIPs detected in M82 by our SPLITREADER pipeline were extracted from the Heinz 1706 reference genome (version SL2. 5) and aligned using BLAT to the high-quality genome of M82. Consistent with the validation based on visual inspection, more than 70% of TIPs detected in M82 were also found in the reference M82 genome, with COPIA insertions showing the highest specificity (77%) (Supplementary Fig. 7 ). Furthermore, this estimated rate is similar to the one we obtained using the same pipeline to analyze numerous A. thaliana resequenced genome data and which we could validate experimentally using TE sequence capture  27, 58. Using this last dataset, we estimated that the false-negative rate of our SPLITREADER approach is about 20% overall, the highest sensitivity being achieved for TIPs belonging to the COPIA, MuDR, and CACTA families  58.
These rates of FP and FN are similar to those reported by others using multiple software developed to detect TIPs based on Illumina short reads  60. SNP calling and phylogenetic analyses. Illumina resequencing data were aligned to the tomato genome reference v. 2. 50 using Bowtie2 v. 2. 3. 2 with default parameters. The resulting alignment files were filtered to remove reads mapping to multiple locations using samtools with parameter -q 5, and to remove duplicated reads with Picard MarkDuplicates with default parameters (parameter REMOVE_ DUPLICATES = true). Finally, indels were realigned using GATK v4. 1. 8. 0 Rea-lignerTargetCreator and IndelRealigner successively with default parameters. Alignment files were used to call SNPs. For this, we ran GATK's UnifiedGenotyper with default parameters in all 602 accessions simultaneously.
We extracted SNPs at 8,760 positions genotyped in the SolCAP Infinium Chip SNP microarray as indicated in the tomato annotation (ITAG2. 4_solCAP. gff3). We obtained a final matrix of 1,812 SNPs after removing ambiguous SNPs and SNPs in high-linkage disequilibrium using PLINK v1. 90b6. 9 with parameters-mind 0. 1-geno 0. 1-indep 50 5 3. 5. A phylogenetic tree was estimated from the final matrix using the ape package in R v. 3. 4. 4 and the neighbor-joining method including S. pennellii LA0716 as an outgroup. The resulting tree was plotted using the ggtree package v. 1. 4. 11 in R v. 3. 4. 4. Tomato accessions in the tree were classified manually taking into account previously described classifications and their positions in the tree relative to known classifications of species and type.
TIP-based population differentiation. A principal component analysis (PCA) using 6906 TIPs was performed using the prcomp function from the stats package v. 3. 2. 3 in R v. 3. 4. 4. The first two eigenvectors were retained to create a twodimensional plot. Genomic localization of TIPs and genes. A circos plot was constructed to represent the chromosomal distributions of genes and TEs, as well as the mappability of Illumina short reads. The number of genes and TEs annotated in the reference genome, as well as TIPs for the six superfamilies (GYPSY, COPIA, LINE, MuDR, hAT, and CACTA) were calculated in 500-kb windows using bedtools. To determine mappability, we aligned Heinz 1706 short-read resequencing data (SRA: SRR1572628) on the reference genome (version SL2. 5). Mappability was defined as the fraction of uniquely mapped reads (MAPQ > = 10) in 10-kb windows.
Duplicated pairs were removed using picard MarkDuplicates. Counts over annotations (version ITAG2. 4) were normalized using DESeq2 61. To determine the transcriptomic impact of TIPs on nearby genes (located within 1 kb), the normalized transcript levels were compared between carriers and noncarrier accessions. Analysis was restricted to 1477 genes showing expression greater than 0 in at least one sample. Variation in full-length transcripts was calculated by comparing the ratio between the normalized number of reads that mapped downstream and upstream of a given TIP. This ratio was then compared between carriers and noncarrier accessions and binned by log2 fold changes (0. 5-1. 5], (1. 5-2. 5], [3. 5-4. 5], >4. 5. Linkage-disequilibrium analyses. For each TIP, we calculated the pairwise r 2 between the TIP and 300 SNPs located upstream and downstream, as well as the pairwise r 2 between all the 600 SNP-SNP polymorphic sites around the TIP using PLINK v2.
We then contrasted the percentage of TIP-SNPs and SNP-SNP comparisons that are in high LD (r 2 > 0. 4). Similar results were obtained when using r 2 > 0. 2 as a threshold to define polymorphisms with high LD (Supplementary Fig. 8 ). Genome-wide association studies. Phenotypic information for 17 important agronomic traits in tomato, including determinate or indeterminate growth, simple and compound inflorescences, leaf morphology, fruit color, shape, and taste for more than 150 accessions was retrieved by web data extraction. To this end, we performed a systematic web data scraping using Google search engine (googler, https://github. com/jarun/googler) followed by text pattern matching. We noted that the World Tomato Society webpage (https://worldtomatosociety. com/) compiles, in a consistent manner, phenotypic information for a large number of varieties commercialized by numerous seed banks.
We thus focused our web data scraping on this webpage. Phenotypes were transformed either in boolean (1 or 0) or quantitative variables (Supplementary Data 2-17). For fruit color phenotype, accessions with red, purple-black, or pink fruits were considered as high lycopenecontaining, and those with green, white, yellow, or orange fruits were considered as low lycopene-containing fruits and codified as 1 or 0, respectively. Metabolomic and volatile quantitative data from ripe fruits of 397 accessions were obtained from refs. 25, 35. SNPs and TIPs with MAF < 1% or more than 20% of missing data were excluded. SNP-GWAS was restricted to biallelic SNPs and LD-pruned using PLINK v1. 90b6. 9  62 option-indep-pairwise 50 5 0. 2. GWAS was performed using linear mixed models (LMM) encoded in the software EMMAX  63.
SNP-based Kinship matrix was calculated (emmax-kin-intel64 -v -d 10) and included in the models as a random effect to control population structure and minimize false positives. Manhattan and qq plots for genome-wide association studies were performed using qqman package v. 0. 1. 4  64. r 2 between the leading associated variant and all other associated variants in Fig. 3g  was calculated using PLINK v1. 90b6. 9  62 nd represented by color code. Effect sizes of associations presented in Fig. 4c  correspond to the beta values of the leading variant from each associated locus identified by the LMM. Given the lower sensitivity and specificity of TIP calling compared with SNPs, which could affect GWAS results, we inspected visually all associated TIPs, and we removed or corrected false-positive TIPs and negative insertion calls, respectively.
Following these corrections, TIP-GWAS was performed again, and only associations with manually curated, high-confidence, TIPs were retained. In addition, the presence/absence of the two key TE insertions studied in detail (i. e. , insertions within PSY1 and PPEAT) was assessed by PCR using genomic DNA extracted from 22 tomato accessions, and their status was confirmed in each case (Supplementary Fig. 6 ). Finally, we tested the robustness of our TIP-GWAS approach by randomizing 1000 times the set of carriers and noncarrier accessions, and running GWAS for the two traits with validated associations (i. e. , fruit color and potato leaf)  28, 41. In both cases, the number of permuted datasets with associations was below the family-wise significance threshold (p < 0. 001 and p < 0. 038, respectively; Supplementary Fig.
Samples were multiplexed in equimolar quantities to obtain 20 fmol of cDNA, and the rapid adapter ligation step was performed. Multiplexed library was loaded on an R9. 4. 1 flowcell (ONT) according to the manufacturer's instructions. A standard 72-h sequencing was performed on a MinION MkIB instrument. MinKNOW software (version 19. 12. 5) was used for sequence calling. Long-reads were mapped on the tomato reference genome (SL2. 5) using mini-map2 67 V2. 11-r797 and visualized with IGV.
Acknowledgements We thank members of the  Colot group  and especially  Pierre Baduel  for discussions and critical reading of the paper. We thank  Zachary Lippman  for sharing his high-quality genome assembly of M82 before publication. We also thank the  World Tomato Society  for making available phenotypic information of tomato cultivars. Support was from the  Agence National de la Recherche  ( ANR-17-tomaTE  to V. C. and J. J. G. ), the  Centre National de la Recherche Scientifique  ( MOMENTUM program  to L. Q. ), and  France Génomique  national infrastructure, funded as part of the  Investissements d'Avenir program  managed by the  Agence Nationale de la Recherche  ( ANR-10-INBS-09 ).
In the last decade, the use of convolutional neural networks (CNN) has spread in the image forensic community [ 7 7 _ T D $ D I F F ] [14,5,  [15] [16] [17] [18] [19]. These algorithms have focused on training the CNN to determine the best features to classify camera models. One advantage of using CNN is the features are extracted directly from the image dataset. The principal advantage of those CNN based approaches is they are capable of learning classification features directly from image data, however, interpreting the extracted camera identification model is very difficult. Another drawback of using CNN is the model is dedicated to a specific training dataset. However, image modifications like geometric distortions (e. g. resizing, compression, filtering) or content manipulations are unpredictable.
Convolutional neural networks (CNN) CNN have become a widespread tool to address forgery image detection tasks. They have achieved state-of-the-art performance in a large variety of computer vision and image processing applications to solve detection and classification problems. In the late 1980s, LeCun et al. [30]  are the first to train a CNN to recognize handwritten letters. This CNN ancestor became mature in 1998 and its superior classification power has been demonstrated over the MNIST handwritten number database  [31]. Afterwards, CNN have been used in many applications during the 90s with good performance on fingerprint recognition in 1993s by Baldi et al. [32]. But soon they quickly became obsolete with the emergence of new methods such as Support Vector Machines (SVMs) and Bayesian Networks (BNs)  [33].
In 2012, CNN made a come back and became a reference in the literature of image classification techniques. This phenomenon was possible due to two main factors. First, high-performance computing systems including GPUs became affordable and available for all. Thus, training CNN with a small dataset was now possible even on a laptop computer. The second important factor of this success was the huge number of annotated images available on the internet and in particular with the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) won by AlexNet  [34]. Since then, this ImageNet challenge became a showdown benchmark for big companies like Google, Microsoft, IBM, and many deep learning models like GoogLeNet  [35]  or ResNet  [36]  are now available and are commonly used to extract deep features feeding an application-specific classification layer.
In the last few years, Convolutional neural networks have also become a very widespread tool to address forgery image detection tasks  [14, 12, 5, 37, 38, 16 ]. Bayar et al. [39]  have proposed a new form of convolutional layer. This layer has been specifically designed to detect the features that have undergone some manipulations. Their proposed framework is trained on a large dataset of unaltered images plus datasets with different manipulations like noising, blurring or resizing. Bulk et al. [40]  have shown the detection tampered regions could be made by combining features provided by CNN and LSTM networks (Long Short Term memory). Their approach did not take into account the possibility of possible manipulations which may happen after the tampering. Bayar et al. [41]  have studied the effect of different CNN architectures for forensic purpose.
Image pre-processing In this part, we detail the selected pre-processing steps needed in our framework (see Fig. 2 ). CNN require large amounts of training data. Preprocessing techniques augment the existing dataset with transformed versions of the existing images. This is done to expose the neural network to a wide variety of data. Scaling and compression are transformation examples among others. In the next subsections, we give details about each component used for image pre-processing. Image transformations: The quality and quantity of the training dataset is a crucial step to achieve high accuracy when using a deep learning model in the framework. One of our research objectives is to apply our image forgery detection approach on online shared images. Thus, our training set has to go through transformations that should be similar to the common ones used on the Internet, e.
Experiments This section presents our exhaustive experiment results. After detailing the experiment setup including chosen datasets and evaluation criteria, we will propose a preliminary study highlighting the importance of compression as an image manipulation technique. It is a summary of our work done in  [20]. Then, we will detail the performance of our framework for camera model identification. We will also analyse our proposed model for a better understanding of CNN and finally, we will study the influence of generalization for the network layers. By extracting 32 patches by image, a dataset of more than 254,000 patches is obtained and used as the training dataset. Each patch is labeled with the true camera model. This process is repeated on two other datasets: a validation dataset (DV) containing more than 43,000 patches and an evaluation dataset (DE) with more than 170,000 patches.
Note that the number of images per camera class is not equal. For example, there is a minimum of 580 images and a maximum of 1200 images per camera class, resulting in an unbalanced classification problem. [ 7 4 _ T D $ D I F F ] -Forgery detection: To evaluate the forgery detection algorithm, we used 2 image datasets. These two separate sets of altered data represent a set of [ 1 1 0 _ T D $ D I F F ] "Known" data from DE dataset and an [ 1 1 1 _ T D $ D I F F ] "Unknown" dataset which contains images from another 8 camera models not included in the CNN training phase. The objective is to study the differences in performance when using [ 1 1 2 _ T D $ D I F F ] "Known" and "Unknown" camera models. Both sets each contain 500 pristine images and 500 tampered images, making a total of 1000 images per set, generated following the process given in  [37].
is JPEG compression reduces discriminative anomalies between block pairs, deleting camera fingerprints for CNN patch-based approaches. Those results confirm training a CNN with a specific compression quality factor provides better performance but only for images compressed with this specific QF. Consequently, the performance of our proposed approach is globally higher on the mixed test image dataset (with the CNNm Model). Fig. 4 (b) shows the confusion matrix of this CNNm model on the uncompressed test dataset (DE). This matrix shows a good classification of the models of cameras with small errors on the models of a same brand. Indeed, models of a same brand may contain similar components and processing tools which result in similar model fingerprints. To conclude on this first set of experiments, we obtain a better accuracy from the CNNm model.
Network layer generalization In this section, we try to determine the existence of a network layer generalization between the different networks we used. To do so, we build exhaustive experiments which consist of using the trained models we already have, and fine-tuning them with another dataset. This is done by freezing first layers of a trained model and restart the training from this model with another quality factor compression dataset. We test this scheme for the first 4 convolutional layers, freezing between one and four first convolutional layers, named FL1 to FL4 respectively. We denoted for example CNN80 FL2 100 the new trained model from the initial CNN80 model with the 2 first layers frozen and fine-tuned with the dataset D 100. We first observe on all figures of Fig. 7   However, when we test those fine-tuned models on other QF datasets, we clearly see higher results meaning that freezing first layers has a small effect for a specific dataset if fine-tuned on it but has more effect on other QF datasets.
Another observation is that the more frozen layers we have, the better are the performance on the other dataset which is logical as more trained parameters come from the other QF datasets. models perform better, but for higher QF, they degrade. It shows the influence of the initial trained model. In conclusion, results obtained from our exhaustive experiments are mainly positive for the mixed model CNNm. All other fine-tuned models (CNN70, CNN80, CNN90) also yield better classification scores on compressed images. However, the original model still performs a bit better on uncompressed images. Those results demonstrate the advantages of using a generalization approach to improve the robustness of the framework while reducing the computational cost of the training steps of the CNN: first layers are trained only once, and last layers, with fewer parameters, are trained on specific datasets.
Further analysis with state-of-the-art models For comparison purpose, we have decided to include recent and weel known deep learning models such as ResNet-152  [36] , DenseNet  [54] , and VGG19  [55]. These recent networks make it possible to train up to hundreds or even thousands of layers and still have allowed boosting the performance of many computer vision applications and classification. The first version of those models have been published between 2014 and 2017. However, those models are updated every year with more layers and new pretrained parameters. Note that, for this experiment, we only use the original dataset without compression factor. The promising results of the pre-trained convolutional networks led us to apply it to the above-mentioned networks to adapt to our camera classification application in order to be able to make this comparison.
The pre-trained models of ResNet, DenseNet, and VGG-19 have millions of parameters trained from the ImageNet dataset with high accuracies. VGG19 model proposed in 2014, contains 19 trainable layers. ResNet model contains 152 layers and DenseNet contains 201 layers. We use the last trained convolution layer with a personalized classification part including a two layers fully-connected classifier adapted to our application. Table  1  shows the performance of our model compared to transfer learning based approach from very well known models. The low performance [ 1 2 3 _ T D $ D I F F ] (35% test accuracy) we obtained with object classificationtargeted models (Resnet, DenseNet and VGG-19) on Dresden ground truth highlight the fact that its classes are unlikely to be identified by specific object features (as opposed to camera model features).
Image forgery detection In this section, we analyse the JPEG compression effect on the performance of an image forgery detection approach. Note that, in this section, we have not taken into account the pre-trained models used in previous section as their performance was not good enough. As previously described, the [ 1 2 6 _ T D $ D I F F ] "Original" datasets have undergone a compression manipulation with increasing quality factors (QF of [ 1 2 7 _ T D $ D I F F ] 70%, 80% and 90%). The ROC and AUC for our framework only are shown in Fig. 8. Those figures present the evolution of the mixed model CNNm with respect to different QF and they clearly show for forgery detection too, the quality of the image has a great impact on the accuracy with the camera model [ 1 2 8 _ T D $ D I F F ] "Known" or "Unknown" datasets.
Conclusion In this article, we have proposed a robust framework for camera identification model and image forgery detection. The proposed approach may include any kind of common manipulations frequently used when sharing images on the internet. We were interested in compression, which is one of the most common manipulations on images. First results on different compression quality factors have shown that training with compressed and uncompressed images is of great importance to get better performances than recent literature approaches. Results have proven the performance of the latter strategy which is a compromise between a fully trained model on a specific dataset and on a mixture of different quality factor datasets. Then, to improve the low CNN interpretability (mainly due to a large number of parameters), we have proposed a qualitative and statistical visualization analysis of the internal features.
This analysis concludes on the great similarity between first convolutional layer weights of different models leading to the generalization of first layers of those networks with a fine-tuning strategy to a specific dataset. We have also tested a transfer learning-based approach from very well known models (ResNet, VGG-19, and DenseNet). We have highlighted that those models do not allow us to identify the signature of the camera on the images. Finally, we have studied the influence of our different proposal on an image forgery detection application. We have observed promising results showing the robustness of our framework. The perspective of this work includes data augmentation using generative adversarial networks to improve the overall performance over multiple image manipulations.
Patch extraction: The second step of our framework divides the images of the dataset into 64 [ 8 3 _ T D $ D I F F ] Â 64 pixel patches. Indeed, the choice of using small images instead of full-resolution images can be explained by the following arguments: (i) it reduces the space size to improve the camera model representation; (ii) the efficiency of algorithms used in the framework and (iii) the data quantity to better train the deep learning model. Moreover, state-of-the-art approaches for camera model classification have shown better performance using small image patches according to[5]. As the quality of the input data is important to train the model, we filter the patches which contains overly dark or saturated regions. Indeed, the number of those specific patches is too high and disturb the training process.
[ 1 0 7 _ T D $ D I F F ] Section 4. 3 allows us to improve the robustness and accuracy of our overall framework. Moreover, we detailed our exhaustive experiments in [ 1 0 8 _ T D $ D I F F ] Section 4. 4 showing the generalization of the first CNN layers. That is to say by freezing the first layers of a pre-trained model before training anew this model. The main idea of testing the generalization is to know the possibility to mix the training sets into only one for first layers and in a second step, fine tune only with a specific dataset the last layers of the network. This scheme allows to reduce the training time and to improve the robustness of the framework.
Experiment setup [ 7 4 _ T D $ D I F F ] Datasets description: [ 7 2 _ T D $ D I F F ] -CMI: Dresden dataset [50] is a publicly available dataset suitable for image source attribution problems. Dresden contains more than 13,000 images of 18 different camera models. For each precise scene, several cameras were arranged in approximately the same position and took almost the same picture. This means that each scene is represented by several (or all) existing cameras. The database is composed of color images of very high quality pictures (around 4000 [ 1 0 9 _ T D $ D I F F ] Â 3000 pixels). Note that we selected only natural JPEG photos from camera models with more than one instance. For the purposes of learning, we divided the dataset into separate training, validation, and evaluation sets denoted DT, DV, and DE respectively.