dataesr/scientific-paragraphs-categorization huggingface dataset:
PREVIOUS WORK Copy-move forgery is one of the most classic attacks to tamper with an image. This attack consists of copying one or more areas in an image and move them elsewhere in the image. Detecting them can be really challenging, especially when the forged areas have been processed by several attacks like noise addition, JPEG compression, blurring, scaling or rotation or if it involves a smooth or small area. Since 2012, there exists common databases to evaluate the copy-move forgery detectors, such as the database FAU proposed by  Christlein et al. (2012)  which contains 48 forged images with realistic copy-move manipulation and where the average size is 3000 × 2300 pixels, and the database GRIP proposed by  Cozzolino et al. (2015a)  created in 2015 which contains 80 images of size 768 × 1024 pixels, where some very smooth areas are tampered.
Copy-move forgery detectors can be classified into two main categories, these are block-based and keypoint-based. In Section 2. 1 we detail previous block-based methods while in Section 2. 2 we present previous keypoint-based methods which have been published before 2015. Finally, in Section 2. 3 we present the most recent copy-move forgery detectors that we have used to compare with our proposed VFCMFD method using the GRIP and the FAU databases.
RESULTS AND DISCUSSION In this section, we present several experimental results obtained with our proposed VFCMFD method. First, in Section 4. 1, we present the databases used for our experimentation as well as metrics used to evaluate the performances of the method we propose. In Section 4. 2 we present a full example of our method and in Section 4. 3 we compare it with existing state of the art ones. In section 4. 4, we analyze the performance of our proposed VFCMFD method, first in terms of computation time and results obtained as a function of the threshold τ θ of the angle, and then in terms of robustness against noise addition and JPEG compression attacks.
Databases and Criteria Two databases designed for the copied-moved forgery detection have been used to perform our experimentation. The first one is the FAU database, which was created in 2012 by  Christlein et al. (2012). It includes 48 source images taken by different cameras. The aim of this database is to provide a common measurement for each copy-move forgery detector by providing large images with an average size of 3000 × 2300 pixels and realistic copy-move forgeries with tampered areas around 10% of the image size. This database provides different types of copy-move attacks which are 48 plain copy-move, where each image is tampered with a plain copy-move, rotation, where the tampered areas are rotated between 2 °and 10 °(with a step of 2 °) before being translated, scaling, where the tampered areas are scaled with different scale factors between 0.
91 and 1. 09 with a step of 0. 02, JPEG compression, where the tampered area undergoes JPEG compression with different quality factors (QF) between 100 and 20% with a step size of 10 before being pasted, noise addition, where the images are normalized and a zero-mean Gaussian noise is added with a standard deviation between 0. 02 and 0. 10 with step size 0. 02, combined transformations, where the images undergo several attacks from the previously mentioned ones, and finally multiple copies, where a square block of size 68 × 68 pixels are copied and pasted five times in each image. The second database used is the GRIP one which was created in 2015 by  Cozzolino et al. (2015a). Its creation was motivated by the need to set up a bank of images that would allow realistic and accurate forgeries to be performed in a reasonable amount of time to validate the different methods and to adjust the different parameters.
All 80 images provided have the same size of 1024 × 768 pixels. It consists of plain copy-move attacks with manipulation that are difficult to detect, such as forgeries made on very small areas (less than 1% of the image) or in very smooth areas. For our experiments we also worked on 10 images of a very large size provided by the DGA 1. Five of them have a size of around 2600-3000,× 2400-4000 pixels and the five others around 6000 × 4000 pixels. Images illustrated in Figure  4A , Figure  5A  and   We perform tests at image and pixel level. At image level the goal is to measure how well a method can make the distinction between tampered images and original ones. To test this, we took the original images from each database as well as the forged ones. The tests were carried out on 160 images from the GRIP database and on 96 images for the FAU database.
A Full Example As the objective is to process 4K images, in this section first we apply our algorithm to a large image (2640 × 2360 pixels), illustrated in Figure  4 , from the DGA database. In Figure  4A  the image representing a tree trunk is altered in the area of the tree bark. This image is a new challenge, because of the patterns formed by the tree bark and makes it much more difficult for CMFD algorithms to find forgeries. Figure  4  shows the results obtained after each step of our method until the convex hull is obtained. In Figure  4B , 117, 631 keypoints are detected and among all of these keypoints, 809 are matched (Figure  4C ). From these 809 matches, 803 are selected to generate 1 cluster as illustrated in Figure  4D , from which the two convex hulls are computed (Figure  4E ).
Finally, Figure  4F  shows a comparison between the mask we obtain with our method and the ground truth mask. The pixels in yellow correspond to the true positives, while the pixels in red are the false positives and the pixels in green, the false negatives. We can observe that our method correctly detects and locates the tampered areas and does not produce many false positives. Figure  5  illustrates two other examples applied to images from the DGA database. The first image illustrated Figure  5A , of size 2640 × 3960 pixels, shows a dark scene with several similar coasters. One of these coasters has been copied and moved while the others remain unchanged. The main difficulty is not to confuse the original coasters by making false positives. Our proposed algorithm successfully detects and locates the two areas involved in the forgery (Dice index = 0.
Comparison on Challenging Images In this part we analyze the results obtained on copied-moved images from the GRIP and FAU databases which have been tested by  Wang et al. (2019). These images are specially challenging because the TP_C01_023 forgery occurs in smooth regions, the TP_C01_005 and TP_C01_30 occur in high-brightness smooth regions, the TP_C01_024 and the TP_C01_049 images (all four from the GRIP database) occurs in images with similar patterns and the image Bricks (from the FAU database) which occurs in multiple regions. Our method is compared with the previous methods of  (Cozzolino et al. , 2015a; Pun et al. , 2015; Zandi et al. , 2016)  and  (Wang et al. , 2019). First, Table  2  presents the results obtained with our method for these challenging images compared to the methods  (Cozzolino et al.
, 2016)  and  (Wang et al. , 2019)  methods on challenging images. The values in bold are the F1 scores obtained using our method on the different databases (6 difficult images, FAU and GRIP) tampered areas are small and occur in a high-brightness, smooth region. Our method obtains similar results than those of  (Wang et al. , 2019)  and better results than  (Cozzolino et al. , 2015a; Pun et al. , 2015)  and  (Zandi et al. , 2016). Images TP_C01_024 and TP_C01_049 contain forged areas that are very similar to genuine areas. The CMFD methods can generate false positives and therefore has a low precision. Our method provides good results due to fast feature g2NN matching process and the DBSCAN filtering step. Our method even succeeds in obtaining better results than those obtained by  (Wang et al.
Comparison With the Entire FAU Database In this part we compare our method with previous ones on the entire FAU database. To do this, we compare our method with the previous methods of  (Huang et al. , 2008; Amerini et al. , 2011; Shivakumar and Baboo, 2011; Li et al. , 2015; Pun et al. , 2015; Zandi et al. , 2016; Li and Zhou, 2019; Mei et al. , 2019; Chen et al. , 2020; Diwan et al. , 2019)  and  (Lyu et al. , 2021). Table  3  presents the results obtained in terms of F1-score at pixel level. At pixel level, our method is very efficient with an average F1score of 0. 8963. The scores obtained are better than those of the methods  (Huang et al. , 2008; Shivakumar and Baboo, 2011; Li et al. , 2015; Zandi et al. , 2016; Lyu et al. , 2021)  with F1-scores between 0. 6354 and 0. 8607, and is comparable to the methods of  (Amerini et al.
Comparison on the Entire GRIP Database In this part we compare our method with the method of  (Bravo-Solorio and Nandi, 2009; Amerini et al. , 2011; Cozzolino et al. , 2015a; Li et al. , 2015; Silva et al. , 2015; Zandi et al. , 2016; Diwan et al. , 2019; Li and Zhou, 2019; Meena and Tyagi, 2020; Tahaoglu et al. , 2021; Chen et al. , 2020)  and  (Gupta and Singh, 2021)  on the entire GRIP database. Table  4  presents the results obtained in terms of F1-score at pixel levels. Our method obtains an average F1-score of 0. 9606 TABLE 3 | Comparison, in terms of F1-score at pixel level, obtained by our method with the methods  (Huang et al. , 2008; Amerini et al. , 2011; Shivakumar and Baboo, 2011; Li et al. , 2015; Pun et al. , 2015; Zandi et al. , 2016; Li and Zhou, 2019; Mei et al. , 2019; Chen et al.
, 2020; Lyu et al. , 2021)  and  (Diwan et al. , 2019)  on the entire FAU database. The values in bold are the F1 scores obtained using our method on the different databases (6 difficult images, FAU and GRIP)   -Solorio and Nandi, 2009; Amerini et al. , 2011; Cozzolino et al. , 2015a; Li et al. , 2015; Silva et al. , 2015; Zandi et al. , 2016; Diwan et al. , 2019; Li and Zhou, 2019; Meena and Tyagi, 2020; Chen et al. , 2020; Tahaoglu et al. , 2021)  and  (Gupta and Singh, 2021)  on the entire GRIP database.
Methods Methods Pixel F1-score  Li et al. (2015)  0. 2774  Silva et al. (2015)  0. 6662  Zandi et al. (2016)  0. 6444  Bravo-Solorio and Nandi (2009)  0. 8482  Cozzolino et al. (2015a)  0. 9299  Tahaoglu et al. (2021)  0. 9300  Li and Zhou, (2019)  0. 9466  Diwan et al. (2019)  0. 9469  Amerini et al. (2011)  0. 9538  Meena and Tyagi (2020)  0. 9581 Proposed method 0. 9606  Gupta and Singh, (2021)  0. 9748  Chen et al. (2020)  0. 9972 The values in bold are the F1 scores obtained using our method on the different databases (6 difficult images, FAU and GRIP) FIGURE 8 | Distribution of the matched keypoints according to their differences in angles and norms, with τ SURF = 0: In green, the keypoints that are matched in the same way with a standard g2NN and with our approach; In blue, the keypoints that are matched with a standard g2NN, but not with our approach; In yellow, the new keypoints that are matched with our approach, with τ θ = 4 °, but not with a standard g2NN.
Frontiers in Signal Processing | www. frontiersin. org size of around 2600-3000 × 2400-4000 pixels and DGA_2 which is composed of five images of a size around 6000 × 4000 pixels. As we can see in Figure  7 , the computation time increases drastically when using the standard g2NN matching algorithm  (Amerini et al. , 2011)  with large images. In particular, for very large images from the DGA_2 database with more than 24 million of pixels and more than 500,000 keypoints extracted, the average computation time exceeds 140,000 s corresponding to 38. 9 h (more than 1. 5 days). With our method the necessary computation time for such an image is on average 5830 s which is around 1. 6 h. The gain in time is mainly due to the matching phase. In order to evaluate the performance of our approach, Table  5  presents the average times for each phase on an image from the FAU database.
Analysis of the Threshold τ θ of the Angle Even if our VFCMFD method strongly reduces the computation time, the results obtained are comparable to those of other state of the art methods as presented in Section 4. 3. Indeed, the main novelty of our fast matching algorithm is the reduction of the number of comparisons made between different keypoints. Our VFCMFD method uses a threshold to select the keypoints that are most likely to match. The higher the threshold, the more comparisons are needed. In Figure  8 , we compare the matches obtained by a standard g2NN algorithm with our proposed fast matching algorithm on the entire database GRIP  (Cozzolino et al. , 2015a). Then, for all matched keypoints, we compute the difference between the norm of their descriptors and the difference between their angles.
We can observe in Figure  8  that the majority of the matches are close to (0, 0). This means that the matches are usually made between keypoints whose orientations are very similar. Consequently, instead of comparing a keypoint to all the other keypoints, it is very interesting to compare it to the keypoints of angle lower than the threshold τ θ. As presented in Table  1 , for our experiments using our fast g2NN method we have used τ SURF = 0 and τ θ = 4 °. As we can observe, our proposed VFCMFD method correctly matches the majority of keypoints, but a lot of other matches appear as well. Table  6  presents the percentages of the same, the false and the new matches between our method and the standard g2NN, respectively for the GRIP database and the FAU database. The first row "Same Matches" represents the keypoints that have been matched by the standard g2NN method and are still matched by the new method, the second row "False Matches" represents the keypoints that are mapped by the standard g2NN method and that are mapped to another keypoint with our proposed fast g2NN while the last row "New Matches" represents the keypoints that have not been matched by the standard g2NN method and that are matched with our proposed fast g2NN method.
We can notice that with our proposed VFCMFD method, most of the matches taking place in clusters are keypoints that are matched by a standard method. But more than 30% of the matches outside clusters are new matches. These new matches can be explained by the ratio used when applying the g2NN. For the GRIP database, in Table  6 , we can note that 0 false matches were detected. Figure  9  illustrates the F1-score obtained and the computation time as a function of the angle threshold used for our proposed approach. We can observe that after a threshold of 4 °, while the computation time continues to increase, the F1-score hardly changes. This analysis enables us to conclude that the approach we propose is very efficient in terms of F1-score and allows us to drastically reduce the computation time.
Performance Analysis of Robustness Against Noise Addition and JPEG Compression Attacks In this section, we study the robustness of our VFCMFD method on images that underwent JPEG compression or Gaussian noise attacks. All the tests were performed on both the entire FAU database  (Christlein et al. , 2012)  and the entire GRIP database  Cozzolino et al. (2015a). The performance of the proposed method is illustrated in detail on two images from the FAU database. The Red_tower image, Figure  10 , is used to illustrate the robustness against JPEG attacks, while the Mask image, Figure  11 , is used for the noise addition analysis. For each figure, we present a set of three images provided by the FAU database  (Christlein et al. , 2012) : the original images, Figure  10A  and Figure  11A , the tampered images, Figure  10B  and Figure  11B , and the binary masks locating the tampered areas in white, Figure  10C  and Figure 11C.
Regarding the F1-score obtained, even if they decrease when the standard deviation of the Gaussian noise increases, we obtain very good performances whatever the standard deviation, as shown in Figure  13. Indeed, all F1-scores are higher than 0. 65, and for a Gaussian noise with a standard deviation lower than 0. 04, the F1-score remains higher than 0. 78. Regarding the evolution of the F1-score obtained on the Mask image as a function of the standard deviation of the added Gaussian noise, up to a standard deviation of 0. 06 the F1-score is higher than 0. 6. We notice a clear decrease when the standard deviation reaches 0. 1 due to the fact the noise becomes too important and the copied and moved areas do not look similar anymore. Figure  14A  and Figure  14B  show the evolution of the average F1-score obtained on the entire FAU database.
While Figure  14A  illustrates this evolution as a function of the JPEG QF used during compression, Figure  14B  illustrates this evolution as a function of the standard deviation of Gaussian noise. We can see that the average F1-scores obtained for both graphs decrease almost in a linearly way. A slightly faster decrease can be observed for the JPEG compression from a QF of 100% and between QF 30% and 20% (Figure  14A ). The decrease is stronger in the case of the addition of Gaussian noise, Figure  14B. Regarding the GRIP database, Figure  14C  illustrates the evolution of the F1-score as a function of the JPEG QF used during compression, and Figure  14D  illustrates this evolution as a function of the standard deviation of Gaussian noise. We can see that the average F1-scores obtained for the two graphs decrease a little more strongly than for the FAU database, although this remains relatively linear.
C. Contact surface selection The contact planner solves the combinatorial problem of selecting the contact surfaces relevant for the motion while enforcing the kinematic constraints for a reduced model of the robot. This problem can be formulated as a Mixed-Integer Program (MIP)  [12] , solved using a variation of the SL1M open-source framework  [15] ,  [16] , with the difference that quasi-static balance is not verified in our implementation. Planner inputs and outputs: The inputs are: • The current state of the robot; • A 3D velocity reference for base (X/Y translation, yaw); • A list of predicted positions/orientations for the base over the horizon, at the time of contact creation; • A list of potential contact surfaces to step on for each of the contacts created in the horizon The computation of these inputs is detailed in Section III.
However, in line with the immunofluorescence data (Fig 1B ), the labeling frequency was significantly reduced in the lumbar region of the skin where only 55. 6% ± 8. 1% (n = 5) of LECs were Tomato + (Fig  1D ). Three independent litters were analyzed to exclude inter-litter variations in the stage of embryos that may affect Cdh5-CreER T2 -mediated recombination efficiency depending on the developmental state of the vasculature. Analysis of untreated litters of E14. 5 R26-tdTom;Cdh5-CreER T2 embryos did not show significant tamoxifen-independent recombination in BECs (0. 9% ± 0. 5% [n = 4]) or LECs (0. 7% ± 0. 3% [n = 4]). Compared with embryos treated at E9. 5, those treated at E10. 5 showed a similar high recombination frequency of BECs (95-96%) and LECs of the cervical region (79% at E9.
Acknowledgements We thank  Ralf Adams  ( Max Planck Institute for Molecular Biomedicine, Münster ) for the Cdh5-CreER T2 mice. We also thank the  BioVis facility (Uppsala University, Sweden ) for flow cytometer usage and support and  Sofie Lunell Segerqvist ,  Sofie Sjöberg , and  Aissatu Mami Camara  for technical assistance. This work was supported by grants from  Knut and Alice Wallenberg Foundation  ( 2018. 0218 ), the  Swedish Research Council  ( 2020-02692 ), the  G öran Gustafsson foundation , and the  Swedish Cancer Society  ( 19 0220 Pj ), all to T M äkinen.
Study design and population Between April 2021 and February 2022, we enrolled 168 subjects presenting to the University Hospital of Basel in a prospective single-centre diagnostic study. Patients included were scheduled for catheter ablation procedures, electric cardioversions or pacemaker or implantable cardioverter defibrillator implantation. Patients had to be aged > 18 years to participate in this study. Participants with paced ventricular rhythm, missing recordings because of logistical or technical issues and patients with heart rhythm change between the 12-lead electrocardiogram and corresponding single-lead electrocardiograms were excluded. The study was approved by the local ethics committee, pre-registered (Clinical-Trial. gov identifier: NCT04809922) and carried out according to the principles of the Declaration of Helsinki.
These DNNs were trained and validated using a dataset of more than 1 million electrocardiograms from an anonymized dataset that had previously been adjudicated by physicians or certified electrocardiogram technicians. The dataset contains 12lead electrocardiograms and Holter electrocardiograms, and the DNN supports, by design, the analysis of a single lead of these electrocardiograms. These electrocardiograms were acquired from North American, European and Asian independent diagnostic testing facilities, hospitals or public datasets. Stochastic descent, early stopping and dropout were used during training to avoid overfitting. The Keras framework with TensorFlow backend (Google; Mountain View, CA, USA) on K-80 (NVIDIA) graphics processing units were used to implement and train the DNNs. The trained DNN reached 96% sensitivity and 99% specificity for the detection of AF or atrial flutter  [12].
This poses a significant challenge for the medical community, as these algorithms are proprietary, and highly variable among companies. Unfortunately, detailed information on how each of these algorithms adjudicates AF diagnoses is not available, nor is it likely to be shared between companies, because of vested commercial interests. Thus, most manufacturers protect their raw data and their algorithms. In this context, a device-agnostic DNN-based algorithm provides two key advantages. First, it guarantees an identical adjudication of AF diagnoses across devices; hence the importance of devicespecific considerations is significantly reduced when the physician assesses the output of the algorithm. Second, it has the advantage to generalize across smart devices, which reflects that the size and diversity of the training dataset were key and appropriate to develop the algorithm.
With more than 15 years of continuous and consistent measurements, the Infrared Atmospheric Sounding Interferometer (IASI) radiance dataset is becoming a reference climate data record. To be exploited to its full potential, it requires a cloud filter that is accurate, unbiased over the full IASI life span and strict enough to be used in satellite data retrieval schemes. Here, we present a new cloud detection algorithm which combines (1) a high sensitivity, (2) a good consistency over the whole IASI time series and between the different copies of the instrument flying on board the suite of Metop satellites, and (3) simplicity in its parametrization. The method is based on a supervised neural network (NN) and relies, as input parameters, on the IASI radiance measurements only. The robustness of the cloud mask over time is ensured in particular by avoiding the IASI channels that are influenced by CO 2 , N 2 O, CH 4 , CFC-11 and CFC-12 absorption lines and those corresponding to the ν 2 H 2 O absorption band.
As a reference dataset for the training, version 6. 5 of the operational IASI Level 2 (L2) cloud product is used. We provide different illustrations of the NN cloud product, including comparisons with other existing products. We find very good agreement overall with version 6. 5 of the operational IASI L2 with an identical mean annual cloud amount and a pixel-by-pixel correspondence of about 87 %. The comparison with the other cloud products shows a good correspondence in the main cloud regimes but with sometimes large differences in the mean cloud amount (up to 10 %) due to the specificities of each of the different prod-ucts. We also show the good capability of the NN product to differentiate clouds from dust plumes.