dataesr/scientific-paragraphs-categorization huggingface dataset:
8 ± 4. 8 to 36. 0 ± 3. 0 min, Somno-Art Software results showed an underestimate of 6. 3 ± 4. 7 min. The Wilcoxon test results were not significantly different between Somno-Art Software and Av6. ICC between each scorer and Av6 was judged as excellent or good for all scorers on the full dataset. Differences in N3 sleep visual scoring for the full dataset ranged between À29. 9 ± 1. 9 to 19. 6 ± 3. 1 min. For this parameter, the Somno-Art Software mean was 1. 6 ± 3. 9 min and was within the interscorer range and was not significantly different from Av6. Similar results were observed for healthy, ISM and MDD subgroups. In the case of OSA patients, Somno-Art Software had the highest, but not significantly different mean score (12. 8 ± 1. 5 min; visual scorer range: À23. 1 ± 3. 5 min to 9. 9 ± 6.
2 min). The ICC score showed a moderate agreement between Somno-Art Software and Av6 on the full dataset, while the other scorers had good or excellent agreement. Somno-Art Software results overlapped with the visual scorer range for REM sleep on the full dataset and on the subgroups (full dataset Somno-Art Software difference of the means: 5. 2 ± 2. 6 min; visual scorer range: À20. 2 ± 2. 7 to 9. 6 ± 1. 7 min). The ICC agreement was judged as good between Somno-Art Software and Av6, while visual interscorers agreements were generally excellent except for VS4 that was moderate. Table  2  represents epoch-by-epoch percentage agreement for each scoring pair. For the full dataset, the lowest and highest agreement between visual scorers were between 78. 5% and 88. 7%, respectively. Somno-Art Software had an interscorer range between 69.
Discussion Inter-and intra-scorer variability between visual PSG scorers is a known bias in sleep analysis  [1, 2]. However, it remains the standard method for scoring sleep recordings. Automatic sleep scoring algorithms have the advantage of not having intra-scorer variability as they are totally reproducible. Most automatic algorithms are  validated against a single visual scorer, which may present a bias taking the interscorer variability into consideration. This paper aims at evaluating the Somno-Art Software, based on cardiac and body activity, against a pool of expert visual scorers. Somno-Art Software estimation of SE, W, N1þN2, N3 and REM sleep belonged to the interscorer range for the full dataset and the investigated subgroups (healthy, OSA, ISM and MDD). One exception concerned the OSA subgroup with a non-significant overestimation of N3 sleep of 12.
8 ± 10. 5 min, while the interscorer range was between À23. 1 ± 3. 5 to 9. 9 ± 6. 2 min. However, some visual scorers underestimated this sleep stage even more than Somno-Art Software overestimated it. Interestingly, full dataset N3 sleep showed an important interscorer variability (À29. 9 ± 1. 9 to 19. 6 ± 3. 1 min). Sleep stage N3 is known to present a high interrater variability between visual scorers. This is generally due to the complexity in characterizing slow waves (SW) duration and amplitude. In contrast, Somno-Art Software overestimated N3 sleep by less than 2 min on the full dataset (1. 6 ± 3. 9 min). Being an automatic algorithm, Somno-Art Software is consistent in its definition of SW and may therefore yield a more accurate and reproducible results. Somno-Art Software tended to overestimate SL in healthy and MDD sub-groups and significantly overestimate SL in ISM (4.
7 ± 1. 6 min; interscorer range: À3. 7 ± 3. 5 to 0. 6 ± 0. 8 min). However, an overestimation of less than 5 min on a mean SL of 44 min is not considered as clinically relevant. To note, long periods of wake-after-sleep-onset, which are present in ISM, are well detected with Somno-Art Software, as illustrated on Fig. 2  (best agreement of the ISM sub-group). On the full dataset, Somno-Art Software had good or excellent ICC scores for all sleep parameters analyzed except for N3 sleep (moderate score). However, ICC scores of SE, N3 and REM sleep were above the 1 reported in the literature for the validation of an automatic PSG scoring algorithm  [13]. 4-stages epoch-by-epoch agreement ranged between 78. 5 and 88. 7% within visual scorers and 69. 9 and 71. 2% for Somno-Art Software. On the healthy sub-group, visual scorers agreed between 68.
9 and 86. 9%, while Somno-Art Software overlapped this range with an interscorer range of 62. 0e71. 4%. Fig. 2  illustrates Somno-Art Software's tendency to fragment less sleep than the visual scorers, a characteristic which could explain the lower epoch-by-epoch agreement observed for Somno-Art Software. On the other hand, reduced fragmentation increases intra-scorer reliability which is of interest for longitudinal studies. It is to note that Fig. 2  displays some misclassifications between N3 and REM sleep with Somno-Art Software (Fig. 2 , worst agreement of ISM and MDD patients). However, as reported in previous validation studies of Somno-Art Software, this kind of misclassification is rare: REM sleep to N3 sleep misclassification was lower than 1. 5%, while N3 sleep to REM sleep was lower than 2% on a dataset of 458 recording nights with healthy and pathological sleep profiles  [7, 8].
This paper shows good inter-scorer reliability of Somno-Art Software compared to PSG visual scorers despite the use of different raw signals (cardiac and wrist activity vs PSG leads). Unlike visual scoring, Somno-Art Software has the advantage to have perfect intra-scorer reliability and can therefore be of interest in studies with repetitive measures, where a drift in the scoring can become critical (i. e. pharmacology, research, treatment follow-up)  [14]. Moreover, the analysis of sleep through Somno-Art Software takes less than 1 min while a visual scorer needs several hours depending on the complexity of the night. In conclusion, Somno-Art Software performance demonstrated reliable scoring over various scoring centers and is in the PSG inter-Table  2  Overall percentage agreement (W/N1þN2/N3/REM) between each pair of scorer (VS1-VS5: visual scorers 1e5; SA: Somno-Art Software).
Acknowledgments We would like to thank the sleep experts of the 5 sleep centers ( St Luke's Hospital, Chesterfield, USA; Concordia University, Montr eal, Canada; The Siesta Group, Vienna, Austria; CIRCS research center, Strasbourg, France; PPRS, Colmar, France ) for having scored the full dataset. We would like to thank  Alain Muzet ,  Bruno Muller ,  Julien Barascud  and  R egis Lengell  e for their help in developing the Somno-Art Software algorithm. Some studies used data obtained from clinical trials sponsored by  Janssen and Minerva Neurosciences, Inc. The interpretation and reporting of research using these data are solely the responsibility of the authors and does not necessarily represent the official views of Janssen and Minerva.
Hernandez-Ortega  [24]  pro-posed a deepfake detection framework based on physiological measurement, namely heart rate using remote photoplethysmography (rPPG). Trinh et al. [50]  utilized dynamic representations (i. e. , prototypes) to explain deepfake temporal artifacts. Sun et al. [45]  attempted to generalize forgery face detection by proposing a framework based on meta-learning. Tolosana et al. [49]  revisited first and second DeepFake generations w. r. t. facial regions and fake detection performance. We show in this work that such algorithms are indeed challenged, if confronted with manipulation techniques outside of the training data. Rössler et al. [40]  presented a comparison of existing handcrafted, as well as deep neural networks (DNNs), which analyzed the FaceForensics++ dataset and proceeded to detect adversarial examples in an image-based manner.
97% and 95. 73% (XceptionNet). 3. LQ: Intuitively low quality compressed data had the lowest detection rates with 55. 98% to 81% (XceptionNet). We here focus on the LQ-compression as the most challenging setting. We note that reported detection rates pertained to the analysis of a facial area with the dimension 1. 3 times the cropped face. Analyzing the full frame obtained lower accuracy. A challenge, not being addressed by Rössler et al. has to do with the generalization of such methods. When detection methods, as the presented ones are confronted with adversarial attacks, outside of the training set, such networks are challenged. This has to do with the third scenario of interst in this chapter.
Attention Mechanisms Attention mechanisms are designed to identify and focus on salient information, which can facilitate improved decisions. Deepfake videos are acquired in uncontrolled conditions and can include a number of artificially created objects in the background (e. g. , news-banners). We hypothesize that attention mechanisms are instrumental in facilitating improved classification accuracy of a deepfake detector by enabling the model to focus on discriminative information. Additionally, visualization of attention maps are beneficial in interpretation of the taken decision. The understanding about attention can be derived from Naradaya-Watson's regression model  [37, 62]. Given the paired training data {( 1 , 1 ), ( 2 , 2 ). ( , )}, for a given test example , a regression model predicts the target value as ˆ = =1 ( , ) (1) i.
(4) Excitation Operation exploits information acquired through squeeze operation to model dependency among channels through gating with sigmoid activation. Formally, squeeze operation is defined the following. = ( , 1 , 2 ) = ( 2 ( 1 )), (5) where 1 ∈ × , 2 ∈ ×. In this context denotes the modulation weights per channel and denotes ReLU. The recalibrated feature is then computed as ˜ = ( , ) = ˜ = [ ˜ 1 , ˜ 2 ,. ˜ ]. (6) We proceed to discuss the dataset.
Dataset The FaceForensics++ dataset  [40]  comprises of 1000 talking subjects, represented in 1000 real videos. Further, based on these 1000 real videos, 4×1000 adversarial examples have been generated by following four manipulation schemes. 1. Face-swap represents a graphic approach transferring a full face region from a source video to a target video. Using facial landmarks, a 3D template model employs blend-shapes to fit the transferred face. FaceSwap. 2. Deepfakes has become the synonym for all face manipulations of all kind, it origins to FakeApp and faceswap github. 3. Face2face  [48]  is a facial reenactment system that transfers the expressions of a source video to a target video, while maintaining the identity of the target person. Based on an identity reconstruction, the whole video is being tracked to compute per frame the expression, rigid pose, and lighting parameters.
Given the binary classification problem in this work, we replace the prediction layer in all networks by a single neuron layer, which outputs one scalar value. All three networks have been pre-trained on the large-scale human action dataset Kinetics-400. We inherit the weights in the neural network models and further fine-tune the networks on the FaceForensics++ dataset in all our experiments. We detect and crop the face region based on facial landmarks, which we detect in each frame using the method from Bulat and Tzimiropoulos  [9]. Next, we enlarge the detected region by a factor of 1. 3, in order to include pixels around the face region.
Experiments We conduct experiments on the manipulation techniques listed above with the algorithms I3D, 3D ResNet and 3D ResNext aiming at training and detecting (a) all manipulation techniques, (b) each manipulation technique separately, as well as (c) cross-manipulation techniques. Towards this, we split train, test and validation sets according to the protocol provided in the FaceForensics++ dataset. We use PyTorch to implement our models. The three entire networks are trained end-to-end on 4 NVIDIA V100 GPUs. We set the learning rates to 1 -3. For training, I3D accepts videos of 64 frames with spatial dimension 224 × 224 as input. The size of input of 3D ResNet and 3D ResNeXt are 16 frames of spatial resolution 112 × 112. For testing, we split each video into short trunks, each of temporal size of 250 frames.
The final score assigned to each test video is the average value of the scores of all trunks. We also investigate the impact of two attention mechanisms on 3D ResNet, namely Squeeze-Excitation blocks and Non-local blocks. In the case of the 3D ResNet with the Squeeze-Excitation (SE) blocks, the network is trained from scratch as the SE blocks are incorporated in the bottleneck modules themselves. Despite this addition not performing at par with the original 3D ResNet pre-trained on Kinetics, training is more stable and obtains superior results compared to a 3D ResNet that is trained on the dataset from scratch. Based on the limitations and advantages we observe for the 3D ResNet, we also investigate the impact of using the non-local block in the 3D ResNeXt, which outperform the other 3D architectures in most cases after this modification.
Effect of Attention in 3D ResNets We here analyze the correlation matrices between two layers (at the same depth) for all the three variants of the 3D ResNet -the original 3D ResNet, the 3D ResNet with squeeze-excitation and the 3D ResNet with non-local block (refer Figure  6 ). The high correlation observed in distinct patches in Figure  6  Both attention mechanisms, squeeze-excitation and non local block increase the number of parameters in the 3D ResNet by around 10%, however when trained and tested on the whole dataset, we observe an improvement of 2% in the true classification rate in case of the model with non-local block (Table  2 ). We note that the 3D Resnet with SE attention could not be initialized with pre-trained Kinetics weights, so for a fair comparison, a 3D ResNet trained on the dataset from scratch was considered.
Conclusions In this work we compared three state of the art video based CNN methods in detecting four deepfake-manipulation-techniques. The three tested methods included 3D ResNet, 3D ResNeXt and I3D, which we adapted from action recognition. In addition, we tested two attention mechanisms. Despite the pre-training of mentioned methods on the action recognition dataset Kinetics-400, the methods generalized very well to deepfake detection. Experimental results showed that 3D/video CNNs outperformed or performed at least similarly to image-based detection algorithms. In addition, we observed that the incorporation of attention mechanisms in 3D CNNs improved related detection accuracy and were beneficial in placing focus of the models on areas of maximum manipulation in the forged videos. Further, we noted a significant decrease in detection rates in the scenario, when we detected a manipulation technique not represented in the training set.
LES SIMULATION ON A 3-D VENTILATED ROOM Finally, the CPC scheme is tested on a more complex validation case, the MINIBAT 66 configuration, whose 3-D flow is driven by a turbulent axisymmetrical jet. The numerical results were compared with the experimental data  67. Even if the 3. 1 × 3. 1 × 2. 5 system geometry (shown in Fig. 21 ) appears simple, several studies have shown the difficulty in predicting the flow mean and instantaneous quantities  68 69. Two RANS simulations, using theand approaches were compared to a LES simulation. A radial interpolation on the velocity, temperature and turbulent kinetic energy at the inlet faces based on the experimental data is performed. The mean inlet velocity and temperature are = 2. 96. -  and = 294. 95. The resulting 2-D inlet interpolated velocity field is shown in Figure  22 (a).
AI based software for lung and lung lesion segmentation All 349 chest CTs were analyzed using the CT Pulmo Auto Results software in the IntelliSpace Portal 11 platform (Philips Healthcare). This is a fully automatic image analysis application able to identify consolidations and ground-glass opacities (GGO) within the lungs, supporting the management of adult patients with suspected or diagnosed COVID-19 pneumonia. An example of the segmentation with quantitative results is shown in Fig. 1. The automatic processing is performed in three cascaded steps as illustrated in Fig. 2. The left and right lungs are first segmented with exclusion of the main airways including trachea, stem, lobar bronchi, and the main vessels. COVID-19 related lesions are then segmented within the pre-segmented lungs with exclusion of the large bronchi and vessels.
Finally, the detected lung lesions are classified voxelwise as GGO or consolidation. Segmentations and classification steps are achieved using deep learning algorithms  [37, 38]  trained on internal and public datasets  [39, 40]  for which lungs, consolidation and GGO were annotated. The training dataset included both contrast and non-contrast enhanced studies in order for the algorithm to be robust to different protocols. To evaluate the performance of the CT Pulmo Auto Results software on the 349 cases of our study, two expert radiologists with more than 20 years of experience performed in consensus a subjective evaluation on 37 randomly selected cases out of 349 patients (approximately 10% of the cohort). Quality of lung and lesion segmentation and lesion classification was rated using five and two levels score respectively described in Table  1.
Data analysis and prognostic score To measure the additional value of the automatically extracted CT lung information in predicting death, the main outcome criterion, we performed a two-phase analysis. To select the best CT criterion, we first assessed the performance of each CT-scan related criterion in predicting death. Once the best CT criterion was identified, we evaluated the benefit on the predictive performance of incorporating this CT criterion to additional clinical factors. During the first phase, data from all individuals of the study population (n=349) were used. ROC curves  [41]  were built for each of the following CT-scan criteria: GGO volume/ Total Lung Volume ratio (GGOV/TLV), consolidation volume/Total Lung Volume ratio (CV/ TLV), lesion volume (GGO + consolidation)/Total Lung Volume ratio (LV/TLV), GGO volume/Lesion Volume ratio (GGOV/LV).
AUC values were estimated, along with the associated 95% Confidence Intervals (CI) using the Delong method  [42]. The CT-scan criterion with the highest AUC value was selected and used in the second phase of the analysis. For this second phase, only individuals with complete data (i. e. , with no missing information; n=346) were analyzed. In step 1, a clinical prognostic model for death prediction was developed. The following clinical factors were selected based on prior knowledge about factors associated with the severity of the COVID disease and on clinicians' expertise: age, sex, obesity, diabetes, hypertension, chronic respiratory disease, dyspnoea, ENT symptoms, chest pain and asymptomatic. A logistic regression model including all the clinical factors listed previously was built and is referred as "Clinical" model in the next step.
The same analysis strategy was carried out for the secondary outcome of our study corresponding to the admission in an Intensive Care Unit (ICU): the first phase to identify the CT scan criterion and to perform the univariate ROC-curve analyses and all 5 steps of the second phase All analyses were performed using R software version 4. 0. 2 [46]. ROC analyses and logistic regressions were performed using the pROC package  [47]  and the mgcv package  [48] , respectively.
This may also explain the relatively low performance of the algorithm in lesions classification. Secondly, the evolution of the disease from GGO to consolidation is not linear. Indeed, in certain cases, the evolution of the disease is fast presenting a progression of the lesions directly from the early stage (GGO) to the late phase called fibrosis, skipping the consolidation phase  [56, 57]. The major strength of our study concerns the use of the CT Pulmo Auto Results software, a commercial AI tool released for clinical use, accessible via the Philips IntelliSpace Portal Platform. Having AI tools integrated into the clinical workflow is indeed of major importance, as specified in the CLAIM (Checklist for Artificial Intelligence in Medical Imaging, Mongan et al  [58]  item 39). This is not the case for several other studies  [59] [60] [61] [62]  that used AI lung segmentation tools difficult to access in daily hospital practice.
8 They also had the option to go directly to the end of the survey without supporting any of the two causes. Participants were then debriefed and went to the end of the survey. The experiment took place in December 2019. Overall, 556 participants living in the UK and speaking English as first language completed the survey. We removed 18 participants who failed the attention check. We also excluded from the sample 6 participants who did not comply with the experimental instructions, i. e. , who did not recall an immoral behavior (2 from the interpersonal recall condition, 4 from the environmental recall condition). Hence, our final sample size comprises 532 participants. On average, participants took 6 min to complete the survey. Once data collection was completed, 10% of participants were randomly drawn to receive the payment bonus.
5). Equal protein amounts per cell type (Table  S2 ) were reduced, alkylated and digested by trypsin (Thermo Pierce) using S-Trap (Protifi) as described  [2]. Peptide labeling by 10-plex tandem mass tag (TMT, Thermo Fisher Scientific) was done in three separate TMT sets based on cell type while preserving the same isobaric label for a given individual across the sets (Figure  1A , Table  S2 ). Each cell type-specific TMT set was fractionated by basic reversed-phase HPLC and the fractions were analyzed by Orbitrap Fusion Lumos Tribrid mass spectrometer hyphenated to Easy nanoLC 1200 liquid chromatograph (Thermo Fisher Scientific)  [3, 4]  using data-dependent multi-notch acquisition  [5]. Cell-specific proteomic datasets were processed by Proteome Discoverer ver. 2. 4 (Thermo Fisher Scientific) using Mascot search engine (Matrix Science) and Homo sapiens Swissprot database.
Spectral matches were validated by Percolator accepting hits at a false discovery rate (FDR) < 0. 01 and the quantification of TMT reporters was done from MS3 spectra of unique peptides. Proteins identified as contaminants were removed prior to any interpretation of data. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http:// proteomecentral. proteomexchange. org) via the PRIDE partner repository  [6]  with the dataset identifier PXD036591. Further details regarding methods can be found in the Supporting Information. Of the 6264 total unique proteins identified, 4949 proteins were quantified in at least one of the three sorted cell types by TMT-based workflow in Proteome Discoverer (Figure  1B , Table  S3 ). To further check cell-specific features in data, we surveyed cell markers published in the Human Protein Atlas  [7, 8]  (in the Total Elevated category, Supporting Information) and examined their MS1 abundance across the three cell-specific pooled samples using label-free quantification performed in separate MaxQuant search (Figure  1C , Table  S4 ).
As expected, the majority of the immune cell (B cell, T cell, granulocyte and macrophage) markers showed the highest relative expression in the CD45 dataset. Similarly, the relative expression for many of the intestinal epithelial cell (enterocyte) markers was most pronounced in the EpCAM dataset, while fibroblast and smooth muscle cell markers were upregulated in the stromal cell dataset. These results confirm the phenotype of the sorted cells (Figure  S1B ) and support that double negative cells represent stromal cells such as fibroblasts and smooth muscle cells. We next aimed to identify functional groups of proteins in each cell type that are characteristic of CD. For this purpose, we choose Weighted Gene Correlational Network Analysis (WGCNA)  [9] , which is a bioinformatic method that allows the identification of clusters of co-expressed proteins (called modules) within large datasets.