dataesr/scientific-paragraphs-categorization huggingface dataset:
In multiple computer vision tasks, e. g. , object detection  [41] ,  [55] , it is common to use a background class in addition to the known classes to classify. This leads to a better separation of the classification space and a more discriminative classifier. While this seems to be a reasonable and straightforward approach, for OOD detection, it is likely to suffer from negative dataset bias  [59]  and thus not generalize to other background objects not seen during training. In our approach, we also use a part of the classes as background when training the individual classifiers, however the overlap of their decision boundaries coupled with the AVA model, better distinguishes in-from out-of-distributions samples. Anomaly detection by reconstruction. Anomalies can be detected by training an autoencoder  [2] ,  [11]  or generative model  [39] ,  [57]  on in-distribution data and use the quality of the reconstruction as a proxy OOD, as the autoencoder is unlikely to decode accurately patterns not seen during training.
In contrast, OVA fusion uses a linearly increasing number of base classifiers, and relies in most works on a Winner-Takes-All class assignment based on the maximum class response. To the best of our knowledge, these ensembling methods have not been used for estimating the epistemic uncertainty of DNNs. One-vs-all formulations have also been studied in a more recent publication  [52]  where an ensemble of one-vs-all DNNs is trained, with a new distancebased loss that can encode the distance of a point from the training manifold, maximizing the binary log-likelihood for the positive class and minimizing it for the negative classes. Despite the interesting results on image classification tasks, this approach does not seem scalable for computer vision tasks such as semantic segmentation. Deep OOD detection.
A recent line of approaches addresses OOD detection through DNNs specific heuristics. Hendrycks and Gimpel  [21]  established a standard baseline for OOD detection relying on the Maximum Class Probability from softmax. In  [12]  a confidence branch is attached to a classification network, which is trained to predict OOD samples, while ODIN  [38]  learns a temperature scaling for softmax values and adversarial perturbation to better distinguish OOD data. Lee et al. [37]  get a class conditional Gaussian distribution with respect to features that they tune on a dataset with OOD data and in-distribution data. Lambert et al. [33]  attenuate uncertainty by training on a large composite dataset leading to a more robust DNN. Zendel et al. [64]  propose a semantic segmentation dataset for checking the confidence score of DNNs.
The authors of  [3]  train a DNN to predict OOD confidence score. Lee et al. [36]  train a GAN along with the classifier to produce near-distribution examples and enforce lower classifier confidence on GAN samples. Malinin and Gales  [45]  use Dirichlet networks to build a distribution over the prediction distributions for OOD detection. Most of these methods rely on a OOD dataset during training and are likely to specialize on specific anomalies from these data  [22]. In contrast, in our approach we do not require OOD examples during training, as we leverage the multiple one-versus-all classifiers.
Hence we must deal with an overconfident DNN that cannot handle unknown data. Our solution is to use a DNN for learning to classify each class vs all the other classes (OVA training); thanks to this training procedure, each DNN will learn to discriminate every class as well as their boundaries: this permits classifiers that it knows but also that it does not know. The OVA strategy is conceptually simple, since at its core it involves training a binary classification DNN. One classifier FIGURE  2 : From AVA and OVA to OVNNI process in the case we deal with a database composed of just three classes. is trained for each class, and prediction is then performed by running the obtained binary classifiers on the testing sample and choosing the prediction with the highest confidence score. Yet, the multiple classifiers involved will learn multiple probabilistic predictions, denoted by P (Y j = 1 | X = x i , ω j ) with Y j a binary random variable for each class j.
The first point assures that we know what is the maximum and minimum of confidence. The second point is to ensure to detect OOD data, which is crucial since it provides information on the reliability of the DNN on one data. The third point is linked to the calibration  [18] , which is crucial to rely on the model predictions. The last point concerns the fact that we want to reduce the uncertainty when increasing the dataset. We use as a measure of confidence for OVNNI the probability max j∈  [1,nlabel]  {p j (x i )}. This measure, bounded by 0 and 1, tells us how much we can rely on the DNN prediction and to which extent it can be used to model the epistemic uncertainty. Indeed in most approaches, the maximum class probability (MCP)  [21]  is used as a simple baseline to model uncertainty.
To better understand the behavior of our DNNs when facing strong shifts in the input data distribution we propose to evaluate the Corrupted Accuracy (cA), and Corrupted Expected Calibration Error (cE) for CIFAR10  [29]. For this scenario, we use CIFAR-10-C  [66] , where the author generated various noise with different levels of intensity. OOD classification with MNIST  [35]. Concerning the classification, we used in a first experiment MNIST  [35]  which is a dataset composed of digit images as training dataset and NotMnist  [1]  which contains letter images as OOD dataset. We first trained a classifier to learn to recognize the images of digits then tested it on the test set of MNIST and NotMnist hoping that the classifier would distinguish digits form letters. The DNN used for this experiment is fully connected and composed of 3 layers as in  [15] ,  [32].
(MNIST rows). OOD classification with CIFAR10  [29]. W also trained a network on CIFAR10 composed of classes airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships and trucks. We have considered as OOD SVHN dataset  [50]. Many papers  [44]  train on CIFAR10 and test on the test set of CIFAR10 with noise or on STL-10  [9]. It turns out that the first test aims more at measuring random uncertainty and the second one the capability to adapt to the domain. We rather have preferred to consider as an OOD dataset SVHN which is a color image dataset of digits, that guarantees that the OOD data really comes from a distribution different from that of CIFAR10. The DNN we used on this experiment is Resnet50  [19] , which has the advantage of being popular in the community. Results are shown in Tables  1  and 2  (CIFAR10 rows).
One can see in Table  3  our results under dataset shift. Our approach has state-of-the-   art results in terms of accuracy and almost regarding the ECE. Figure  4  shows that OVA is not adapted to aleatoric uncertainty while OVNNI maintains good performances. Hence, we have shown that our approach is resistant to aleatoric uncertainty. OOD Segmentation with Camvid  [8]. We used Camvid, a dataset conventionally used in works dealing with segmentation or uncertainty theory and deep learning  [10] ,  [15] ,  [25]. This is dataset is an "easy" dataset but allows you to quickly validate results. To test the ability of OVNNI to detect OOD pixels, we trained on all Camvid classes except 3 classes (pedestrian, bicycle, and car), that we deleted, by marking the corresponding pixels as unlabeled. These three classes correspond to OOD classes.
Thus this experimental protocol proposed by  [15]  makes it possible to validate that the trained DNN will detect the pixels on which it has not been trained as OOD. The DNN for this experiment is Enet  [54]. Results are shown in Tables  1  and 2  (Camvid rows). OOD Segmentation with StreetHazards  [20]. StreetHazards is a large-scale dataset that contains different sets of synthetic images of street scenes. More precisely, this dataset is composed of 5125 images for training and 1500 test images. The training dataset contains 13 classes and the test dataset is composed of the 13 training classes and 250 OOD classes, making it possible to test the robustness of the algorithms with all possible scenarios. For this experiment we used PSPnet  [65]  with the experimental protocol in  [20]. The   1  and 2  (StreetHazards rows).
OOD Segmentation with BDD Anomaly  [20]. BDD Anomaly dataset is a subset of BDD dataset, composed of 6688 street scenes for the training set and 361 for the testing set. The training set contains 17 classes, and the test dataset is composed of the 17 training classes and 2 OOD classes. For this experiment we used PSPnet  [65]  with the experimental protocol in  [20] ]. The architecture used for the PSPnet is ResNet50. Results are shown in Tables  1  and 2  (BDD Anomaly rows).
B. VISUALIZING OVA AND AVA EMBEDDING In this subsection, we perform two experiments to determine the behavior of the representations learned by the DNN with the different techniques. For both experiments we train a simple DNN composed of 3 hidden layers followed by a batch normalization on MNIST dataset  [35]. In the first experiment, we have considered as training data only the images with the digits '0','1' and '2' images (the 3 first classes). Then we perform inference on the official test set composed of images with these classes and the OOD images which are composed of other classes. We represent in Figure  7  the softmax of a classical AVA training, a deep ensemble training and the OVNNI training. We can see that in contrast to other techniques, OVNNI results do not necessarily belong to the 2-dimensional simplex.
In addition, OVNNI brings the OOD data far away from the simplex vertices which highlights its potential to detect OOD data. In the second experiment, we performed a classical AVA training, and we also performed the OVA training. Hence for the OVA training, we have 10 DNNs (since the dataset has 10 classes which are the 10 digits). The OOD class is composed of images of the NotMNIST dataset  [1]. Hence, we apply the DNNs on this test dataset and on the AVA case, we collect for each data the feature space of the DNN just before the classification of each data. In the OVA case, we collect the same feature space but for the DNN of the predicted class. We reduce the dimension of each of these feature spaces using T-SNE  [43]  and Principal Component Analysis (PCA)  [62]  and we plot the results in Figure  8.
The calibration plot is defined on  [18]  and is constructed by taking bins of data based on their confidence score. Then on each bin, we evaluate the accuracy, as it should ideally be comparable to the confidence score. These curves show once again the good performance of OVNNI in terms of calibration. On Camvid we note that OVNNI improves the results the state of the art by up to 77% with regard to the metrics proposed by Corbière et al. [10]    The first column is the input image, the second is the ground truth, the third is prediction and the last is the confidence score of OVNNI. For comparison, we add the MCP confidence score in the fourth column. We can see that OVNNI has a low score on the chair, the seat, the rocket and the spider which correspond to the OOD classes. by a least 2.
E. LIMITATIONS OF THE APPROACH AND PERSPECTIVES The OVA strategy is conceptually simple and straightforward to adopt and implement in most cases. The main limitation of OVA is related to cases with numerous classes. For popular datasets and tasks involving up to 10-15 classes the computational cost is on par with ensembling approaches, for which this is a typical size for the ensemble  [32]. However beyond this number of classes, the approach is less appealing due to the increasing training cost. We note that in several practical settings, e. g. , perception for driving assistance  [82] -  [85] , the number of classes is often low, i. e. , less than 10, (in order to avoid ambiguity and class imbalance, frequent drawbacks of high granularity datasets. For tasks that do not allow low number of classes, we indicate a few potential strategies to render OVNNI more feasible for such cases.
we can consider meta-classes that group multiple classes from the training set according to visual or semantic relatedness. The taxonomy of a number of datasets is derived from an ontology (usually hierarchical), e. g. , Imagenet  [76] , and we can use this criterium for grouping classes. Alternatively, such meta-classes can be learned  [77] ,  [78] , as considered in other related approaches, e. g. , Error Correcting Output Codes rely on OVA and even on OVO classifiers  [79] ,  [80]. In this work, we did not need to adopt such strategies were not necessary as the computational cost is tractable on the considered datasets. However we plan to explore them in future works.
V. CONCLUSIONS In this work, we presented an approach based on One versus all training and mixed with a modern approach based on deep learning. We show that the combination of these approaches reaches states of the art performance on all segmentation experiments. Regarding classification tasks, OVNNI exhibits the best calibration performance. Concurrent approaches suffer from a lack of performance in calibration in most datasets, hence the scores that they provide are overconfident, potentially leading to dangerous scenarios in critical applications. In addition to the reported performance, our approach.
We propose an effective non-Bayesian technique for uncertainty quantification in OOD data classification, that reaches state of the art results on calibration and on OOD data detection on a variety of datasets, and on all typical metrics. (2) We conduct extensive evaluation experiments on multiple computer vision tasks (image classification, semantic segmentation) and datasets (MNIST [35]/Not MNIST [1], CIFAR-10 [29]/SVHN [50],.
( 1 ) 1 We propose an effective non-Bayesian technique for uncertainty quantification in OOD data classification, that reaches state of the art results on calibration and on OOD data detection on a variety of datasets, and on all typical metrics. (2) We conduct extensive evaluation experiments on multiple computer vision tasks (image classification, semantic segmentation) and datasets (MNIST [35]/Not MNIST [1], CIFAR-10 [29]/SVHN [50],.
Cognitive assessment on MindCrowd consists of a simple visual reaction time (svRT) and a pairedassociates learning (PAL) task. svRT and PAL are quick and sensitive tests of processing speed and associative episodic memory function, respectively. Past work has shown these cognitive functions are affected in the earliest stages of cognitive impairment and Alzheimer's Disease (AD), but also reflect the general decline in cognitive performance in healthy aging. The ability to capture data from hundreds of thousands of participants at a relatively low cost and without time-consuming and intensive in-person visits has allowed the team behind MindCrowd to investigate the impact of diverse family, medical history, and genetic factors on cognitive performance across the adult lifespan  [29, 32] , and to identify potential cases of previously undiagnosed cases of cognitive impairment and dementia  [31].
Analysis Following data collection, an initial exploratory voxel-based analysis was performed to identify potential associations between the collected cognitive measures (word-pair association score and reaction time) and brain gray matter density. Following nonlinear alignment to the study template, correction for the effects of the warping on the voxel-wise density measures, and subtle blurring with a 4mm Gaussian kernel, a general linear model was fit at each voxel that modeled PAL as a function of local gray matter density (GM), subject age, and biological sex, i. e. ,  [1]  Recognizing prior demonstrated associations between education level and metrics of brain morphometry in healthy and cognitively impaired individuals  [42, 43] , we extended our exploratory analyses to include reported education level as an additional model variable,  [2]  Education was scored on a 5-point scale ranging from 1 (high school diploma) to 5 (graduate or professional degree).
PA L i = β 0 + β 1 Age i + β 2 Sex i + β 3 GM i PA L i = β 0 + β 1 Age i + β 2 Sex i + β 3 GM i + β 4 Educat ion At tain m ent i. Analysis was performed using the Randomise tool of the FMRIB Software Library (FSL)  [44]. Threshold-free cluster enhancement (TFCE)  [45]  was used to control for the multiple voxel-wise comparisons. Based on the outcomes of our exploratory analyses, hypothesis-based analysis was performed in which we examined associations between deep brain gray matter structure volumes (including right and left hemisphere hippocampus, amygdala, caudate nucleus, thalamus, globus palliudus, and putamen) and PAL scores. To calculate the deep gray matter structure volumes, the Oxford-Harvard subcortical atlas provided as part of FSL and registered to MNI space  [46]  was aligned to each participant's image using the inverse of their individual->study template and study template->MNI template transformations.
These regions have known functional associations with language processing, memory, and learning. While the precentral gyrus is not directly related to these neurological functions, it is involved in movement and may reflect individual differences in tablet use or ability during the MindCrowd assessment. These results were confirmed in follow-up regional analysis, which identified significant brain-PAL associations in bilateral hippocampus, amygdala, thalamus, and caudate, controlling for subject age, sex, educational attainment, and total brain volume. Although the putamen associations did not survive FWE correction (with corrected p values of approximately ≃ 0. 05) they do show a strong trend towards significance. These results carry important implications for future opportunities and directions in neuroscience research.
In this perspective, the mode of transmission of the SARS-Cov-2 virus, which relies on droplets transported and dispersed though the atmospheric environment and indoor spaces, at distances from less than one meter to a few meters (and perhaps more), opens up possibilities for using computational fluid dynamics (CFD). Generally speaking, CFD enables choices to be made regarding efficient designs and developments in order to reach desired results and to prevent or limit the adverse effects of critical situations. Therefore, CFD could find a specific breakthrough application in response to the propagation of infectious agents in enclosed spaces. This is precisely the objective of this paper, which aims to demonstrate the relevance of physical modelling and numerical simulation using tried and tested CFD computer software to evaluate the transport and dispersion of drops and droplets carrying the SARS-CoV-2 virus in human secretions and, at a later stage, to evaluate the health consequences of the virus.
The authors solved the equations of fluid dynamics in the RANS formalism with the k-omega turbulence model. The dispersion of the drops was evaluated by a Lagrangian method, taking into account the phase changes of the drops and their interactions. The simulations were carried out with the Open FOAM software. The authors showed that the drops were still suspended 6 m beyond their point of origin when carried by the strongest flow. In another study, Dbouk and Drikakis 26 applied the same turbulence modelling of the flow and dispersion of drops emitted by a simplistic humanoid manikin in an elevator cabin. As expected, the distribution of the drops strongly depended on how the elevator cabin was ventilated. All these studies are informative because they account for the evaporation of liquid particles.
Unfortunately, the geometry and the ventilation system considered in this study seem to be very specific, and it is unclear if the patients were actually modelled. Wang et al. 31  also dealt with the optimisation of air distribution in a hospital ward for minimising cross-infection among patients. Simulations were performed with ANSYS FLUENT software using a realisable k-epsilon flow model and a Lagrangian dispersion model. Again, the patients were represented in a very simplified way (as parallelepiped solids), thereby limiting the reliability of the computations. Crawford et al. 32  were interested in the distribution of micrometric particles emitted during the coughing and breathing of patients placed in intensive care units and equipped with oxygenation devices. The study used Power Flow® CFD software which is based on the Lattice Boltzmann method to simulate the flow and on motion tracking of the droplets.
Results of the 3D dispersion simulations corresponding to a cough or to cyclic exhalation. This sub-section is organised in three parts. The first part compares the Eulerian and Lagrangian results for the test case of the brief cough for one location of the infected passenger and micrometric droplets, the second part  presents the Lagrangian results for the test case of the brief cough for the other location of the infected passenger and with all sizes of droplets and drops, and the third part presents the Lagrangian results for the test case of the cyclic exhalation with micrometric droplets. Comparison between Eulerian and Lagrangian results. The CFD software operated in this numerical research study offers both the Eulerian and Lagrangian options for the modelling and simulation of the dispersion of aerosols.
The main achievements of the numerical study can be summarised as follows: • Input data have been sought in the relevant literature regarding the geometry and ventilation of suburban trains, and regarding the expectoration of droplets and drops by individuals in the course of processes such as respiration and coughing (as well as speaking, singing or sneezing). Our aim was to reach a satisfying level of realism regarding the internal geometry of the rail coach and the humanoids populating it. Of course, there is still room for improvement in the material and human components of our 3D mock-up. • The thoroughly validated CFD code referred to as Code_SATURNE has been adapted to model turbulent flow and dispersion in the inner part of a rail coach. The results regarding the flow and dispersion of particles are fully consistent with what could be expected.
The agreement was very satisfactory for both the velocity field and the particle concentration field. The validation of Code_SATURNE in this specific case study reinforces our use of the software in the numerical study of the railroad carriage, in which the same turbulent flow model and Eulerian dispersion model were implemented. The Lagrangian dispersion model in Code_SATURNE also benefited from a great attention. The general principles were established for turbulent two-phase flows carrying particles, which might be bubbles, drops or solid inclusions  40, 43. In many cases, the carrier fluid phase is treated in RANS formalism, in particular with k-epsilon turbulence model. The carried phase, that is to say the particles, are taken into account by considering, on the one hand their dynamics where drag and gravity are generally the preponderant forces, on the other hand the stochastic equation of Langevin which shows relaxation and drift terms as well as a random term in the form of a Wiener process.
While coughing may lead to a full spectrum of droplets and drops, breathing out produces micrometric droplets. Regarding the cough, we decided to consider particles separately over a wide range of sizes, from 1 to 1000 µm in aerodynamic diameter. In a further stage of this research, it would be interesting to adopt a more realistic spectrum produced in the event of a cough. It is also worth noting that other dissemination events such as sneezing Table  1. Ventilation characteristics of the compartments occupied by the passengers. These data were used in the simulation of the air flow in the inner space of the rail coach. or speaking could be envisaged. Indeed, these events are quite close to coughing and breathing out, respectively. Tables  2  and 3  compile the features of the cough and the exhalation, respectively, which were considered when constituting the source terms in the dispersion simulations.