dataesr/scientific-paragraphs-categorization huggingface dataset:
Our classifier achieves very high performance on coherent eddy signatures while being robust to high levels of cloud coverage. Our data is available under a creative commons licence. The structure of this study is as follows: In Section II an automatic method is presented to retain a large dataset of SST image patches containing eddy signatures based on altimetry detection region proposal. In Section III the methods used to train and evaluate CNN-based classifiers are described. In Section IV the performance of the classifiers is evaluated on images containing coherent eddy signatures. Subsequently, in Section V we assess the effect of cloud coverage on the performance of the classification. Finally, in Section VI main conclusions on the given task and future prospects of Deep Learning for eddy detection are discussed.
II. DATASET CREATION AND FEATURES The task of this study consists in classifying SST images which can contain either the signature of an Anticyclonic Eddy (AE), a Cyclonic Eddy (CE) or No Eddy signature (NE). Anticyclones (cyclones) rotate in the opposite (same) direction with the earth's rotation, viz. clockwise (counter-clockwise) in the Northern Hemisphere. To this end a dataset containing such SST image patches needs to be extracted from images of larger domains. In this section a regional proposal method through altimetric detection is presented, and the extracted dataset is presented.
A. Region proposal through altimetric eddy detections The domain of the dataset of this study is the Mediterranean Sea on a 3-year time period  (2016) (2017) (2018). Two data sources are considered: • SST images are received with a daily resolution from the Copernicus -Marine environment monitoring service. These high-resolution (1/120 ) images are a product of supercollation, as described in  [37]  and stem from merged multisensor data, representative of nightime SST values. • Eddy locations and contours are retained by applying the AMEDA on daily Adjusted Dynamic Topography and the AVISO/CMEMS surface geostrophic velocity fields with applied cyclogeostrophic corrections  [24]. The AMEDA  [23]  detects eddies by identifying minima and maxima of the Local Normalized Angular Momentum (LNAM), computed on the surface velocity fields, and selecting closed streamlines around them.
B. Dataset creation and labeling Examples of images retained through the aforementioned process are given in Figure  2. These images are used to create datasets, which are used for training and testing CNN-based classifiers. The characteristics of the datasets used in this study are outlined in Table  I. Image labels received by the altimetric region proposal do not necessarily visually correspond to the SST signature depicted in them. This can be due to various reasons: • Uncertainty of the AVISO/CMEMS altimetry maps, due to interpolation between satellite track measurements. • Error induced by the AMEDA algorithm. • Strong cloud coverage of the SST signature. • Unclear SST eddy signatures due to air-sea interactions. A large dataset named EDDIES-AUTO, is automatically created as described above, and contains images with labels corresponding to 3 classes k 2 AE, CE, N E.
To filter out incorrect proposals stemming from altimetric detection, only the RoI that correspond to large and intense eddies detected by altimetry are retained. Still, the automatically retrieved dataset contains images whose visual signature does not clearly refer to their assigned label, due to a combination of the aforementioned reasons. This set is denoted as D and contains u examples (x, ỹ). The labels retained by the altimetry region proposal automatic selection, are denoted as ỹ to refer to the presence of label noise. The effect of label noise on the EDDIES-AUTO dataset is visualized in Figure  2    By defining the label-noise distribution p(ỹ|y, x) we can specify the level of discrepancy between the expert labeling and the noisy labels obtained by the automatic altimetry region proposal.
This distribution for the EDDIES-AUTO dataset can be inferred by manually labeling a random sample of images, with /u ⌧ 1. We receive thus the 3 by 3 sized noise matrix of probabilities:  class. Likewise, out of the sampled NE-labeled images, an average 5% allocated to each of the AE and CE classes. This reflects the percentage of eddies missed by altimetric detection, corresponding to examples (p) and (q) of Figure  2. Overall, the noise matrix evaluation shows that less than half of the eddy labeled (AE,CE) images in the EDDIES-AUTO dataset have a humanly visible signature corresponding to their labels. Additionally, a small fraction of the NE labeled images contain missed eddy signatures. This discrepancy between visible signatures and labels portrays the effect of noisy labeling on this dataset, and is tackled through a transfer learning approach in the next sections.
C. Cloud Coverage Cloud coverage has a direct impact on SST images and on the learning process, as it creates missing values in the sampled images, and often corrupts the signature apparent in the image. Cloud coverage is also related with p(ỹ|y, x): the ability to infer the True Label y of a cloud-covered signature depends on both the location and the density of the cloud pattern. To quantify the presence of clouds in the datasets used in this study, a cloud coverage percentage (CCP) is calculated for every Region of Interest as: CCP = n NaN /m 2 (2) where n NaN is the number of missing value pixels in each image, excluding the ones that represent the coast and m = 230 is the RoI side in pixels. The distribution of CCP values is quantified in Figure  4. With a black line, the histogram of CCP values is plotted for all the available RoI to be retained through the regional proposal methodology.
Out of them, only images under a threshold of 80% of Cloud Coverage are retained on the EDDIES-AUTO dataset (Figure  4 , Purple Line). Thus images with a large degree of cloud coverage (last two black line bins of Figure  4 ) which completely corrupts the temperature signature are avoided. Finally, the EDDIES-EL dataset, has distribution with much lower values of CCP, due to the expert selection process: more than 80% of images in the EDDIES-HL have only 0 10% of cloud-coverage (Orange Line, Figure  4 ). [38]  have been successfully used in numerous computer vision applications, including ones of remote sensing. In this section we describe the architecture of a CNN-based classifier and the methods used in the training process. We also introduce a transfer learning scheme as well as indices of evaluation of the classification performance.
A. CNN Architecture Due to the large size of the dataset and the complexity of the image features, a deep CNN architecture is used build a classifier. Residual networks  [39]  utilize skip connection between layers in order to build efficient deep architectures. Here, a ResNet18 architecture, with 18 fully-connected layers and skip connections, is used through the torchvision package of the Pytorch library. The input layer of the network is modified so that a two channel input image can be received: The first channel represents the normalized temperature values and the second channel a semantic mask representing missing data locations. The final layer of the network is also adapted to a three-class output, normalized through the soft max equation. Training and weight update is performed through a cross-entropy loss and stochastic gradient descent with momentum.
B. Training methods and transfer learning Random orthogonal rotation is performed on input images during the training process, in order to achieve rotational invariant model training. Rotational data augmentation provides both a different geometric perspective as well as potential alternative instants of image that depict physically rotating structures. A 5-fold cross-validation is performed in all model training runs. A different 20% of the train set, serves each time for validating the performance after every training epoch. To avoid overfitting, regularization is performed in the training process. An early stopping scheme is adopted based on the loss of the validation set. Transfer learning aids CNN training aids by extracting features from a large dataset of images and utilizing the learned features for a more specific task.
Here, we do this by pretraining CNNs on datasets of images larger than the specific task. We perform a non-zero weight initialization in our CNN training by pretraining in two different ways: • All the ResNets trained for the purposes of this study are already pretrained on Imagenet, a large dataset of more than 14 million images. This way, weight initialization is performed with the shallower layers being able to detect common image features such as edges or gradients. • Pretraining is also performed on the larger EDDIES-AUTO dataset, providing weight initialization for finetuning on the EDDIES-EL dataset. The model trained this way is referenced as AU T O/EL.
C. Trained Classifiers and evaluation indices We train three different classifiers, through the 5-fold crossvalidation scheme: • Classifier-EL is trained on the EDDIES-EL  (16/17)  dataset, that is on a relatively small amount of coherent signature images, with weak cloud coverage, whose labels can be directly validated by an expert. Finetuning is performed on all layers of the CNN. This way features from the more diverse in signatures and cloud-coverage EDDIES-AUTO dataset can be extracted, while finetuning on the EDDIES-EL dataset of coherent signatures. Evaluation of the classifiers is performed on test sets in the form of precision normalized confusion matrices. Each cell (i, j) of the 3 by 3 sized matrix represents the precision defined as the probability of an image predicted by the classifier (y pred ) in class j to be labeled in the dataset (y true ) as class i: C pre ij = p(y pred = j|y true = i) Values of equation 3, where i = j, i.
IV. CLASSIFICATION OF COHERENT SIGNATURES The classification performance is firstly evaluated on images containing coherent signature with a small or no amount of corruption due to cloud coverage. To this end, the EDDIES-EL(18) test set is used in order to evaluate classifier performance. The three trained classifiers (EL,AUTO,AUTO/EL) are inter-compared based on the precision normalized confusion matrices. The confusion matrices of Figure  5  show the precisions C pre ij for each of the given cells. All classifiers show a robust performance on the EDDIES-EL test set, with mean classification accuracies of 91. 8±1. 9% (Classifier-AUTO), 96. 1 ± 1. 1% (Classifier-EL) and 97. 5 ± 0. 3%. (Classifier-AUTO/EL). The high classification accuracy achieved by the Classifier-EL shows that by training on a small dataset of coherent signature images, as is the EDDIES-EL(16/17) train set, a classifier with robust performance on these type of examples can be constructed.
The effect of noisy labeling of the EDDIES-AUTO(16/17) set in the training process can also be seen here: The Classifier-AUTO achieves the lowest classification accuracy between the three classifiers, when evaluating on a dataset of coherent signature images (Figure  5b ). However, by finetuning it on the EDDIES-EL dataset, the received Classifier-AUTO/EL achieves the best performance between the three by increasing the mean and reducing the standard deviation of the classification accuracy (Figure  5c ). Nevertheless, the experiment presented here is evaluated on a dataset containing signatures which are much more clear that the ones existing in the whole domain of application. The robustness of classification on examples with strong cloud coverage corrupting the SST signature, is evaluated on the next section.
A. Cloud Data Augmentation The EDDIES-AUTO dataset has a distribution with higher cloud coverage values than the EDDIES-EL dataset (Figure  4 ), and is therefore more depictive of the application domain, albeit being limited by the 80% threshold on CCP. Nevertheless, the noisy labeling of the EDDIES-AUTO dataset creates a discrepancy between visible signatures and image labels. Therefore, using this dataset to test the CNN-based classifier, does not allow for a confident evaluation of their robustness to cloud coverage. To tackle this issue, a test set representative of cloud values is constructed based on the coherent signature images contained in EDDIES-EL  (18)  test set, whose labels have been validated by experts. The produced augmented test set, named EDDIES-CLOUDY here on, is created by randomly adding to the images contained in the EDDIES-EL test set, cloud masks which are retrieved from the EDDIES-AUTO dataset.
This way a test set of images with expert-validated labels is produced, which is also corrupted by realistic cloud patterns, effectively simulating samples from the domain of application of the classifier. The cloud masks are extracted from images corresponding to the year 2018, so that the same cloud patterns Masks are randomly added to each of 300 images selected from the EDDIES-EL test set, in order to create corrupted images falling in 8 different bins of cloud percentages (0 10% to 70 80%). 10 random corruption realizations for each original uncorrupted image are performed for each of the 8 cloud range bins, creating 80 class-balanced test sets of 300, for a total of 24000 images (see Table  I ) Algorithm 1 describes the iterative process followed for the test data augmentation. An example from the EDDIES-CLOUDY dataset is given in Figure  6.
An inter-model comparison of the classification accuracies (Eq. 4) for increasing CCP ranges on Figure  8a  shows essentially the same behaviour: training on the EDDIES-AUTO dataset proves more robust to cloud coverage than training on the EDDIES-EL dataset, while pretraining on EDDIES-AUTO and finetuning on EDDIES-EL, furtherly improves classification accuracy on clear signature images. Overall, the best performing Classifier-AUTO/EL, achieves a considerable precision of more than 90% for the AE and CE classes and more than 80% for the NE class, for images with up to 50% of cloud coverage. It still shows robust performance for images with up to 80% of cloud coverage, although with a lower precision, with a minimum of 70% mean precision of eddy detection. Robustness on classification of cloud covered eddy signature images is higher for Anticyclonic than Cyclonic signatures, shown by the stable number of AE predictions (Figure  7f ).
This depicts the fact that cyclones have a more complex, and difficult to classify, signature on the SST. The pretraining methodology followed here, allows for feature extraction from a large, automatically retained dataset with a high variety of signatures, corrupted by missing values and with presence of label noise. By finetuning a classifier trained on such images on a smaller subset of coherent signatures with accurate labels, we show that coherent signature, uncorrupted cases can be classified with almost no error, while maintaining a robust performance on images corrupted by missing values. The Deep Learning approach also achieves a performance which exceeds that of an oceanographic human expert in classifying eddy signatures with strong cloud coverage: the Classifier AUTO/EL proves able to correctly classify eddy signatures with up to 80% of cloud coverage with an increasing amount of error as CCP increases.
VI. CONCLUSION In this study a novel Deep Learning approach is presented to validate the detection of mesoscale eddies from standard altimetry products, using Sea Surface Temperature images. An SST image CNN-based classifier is trained, showing potential to detect eddy signatures, even if the images are corrupted by a high level of cloud coverage. Such a classifier can be used as a tool to validate or correct standard eddy detections based on altimetry products, which are often uncertain due the to interpolation between satellite track measurements. A methodology to automatically retain a large dataset of SST image samples, based on altimetric detection region proposal, is first presented. However, a dataset retrieved this way contains a large number of noisy labels, due to complex eddy signatures or to a significant amount cloud coverage.
On the other hand, a smaller subset of coherent signature images is labeled by oceanographic expert, in order to extract a reference dataset with coherent eddy signature images. The best performing SST eddy signature classifier is constructed by pretraining a ResNet18 CNN on a large dataset of automatically retained images, and then fine tuning it on a smaller subset of coherent signature, expert labeled ones. A mean classification accuracy of 97. 5% is achieved on a test set containing coherent eddy signatures. Our classifier achieves significant performance on cloudcovered eddy-signature images, with a precision larger than 90% on Anticyclonic and Cyclonic signature predictions for images having up to 50% of cloud coverage. Furthermore it shows robust performance on images with 80% of cloud coverage, reaching a minimum mean precision of 70% on eddy detection.
Cloud Data Augmentation Input: Datasets: EL{Contains 300 uncorrupted images}, AUTO {Contains cloud masks} Output: CLOUDY {80 sets of 300 images} initialization; for cbin = 10-20 to 70-80% {Loop over CCP bins} do for rep = 1 to 10 {Repeat different masks} do for img = 1 to 300 {Repeat for img in EL} do Get uncorrupted img from EL; Compute CCP of img; while CCP outside of cbin do Get random mask from AUTO; Apply random mask on uncor. img; Compute CCP of corrupted img; end Save corrupted img to CLOUDY; end end end appearing in the images used for training the classifiers, corresponding to years 2016/2017, are not repeated in the EDDIES-CLOUDY test sets.
Algorithm 1 : 1 Cloud Data Augmentation Input: Datasets: EL{Contains 300 uncorrupted images}, AUTO {Contains cloud masks} Output: CLOUDY {80 sets of 300 images} initialization; for cbin = 10-20 to 70-80% {Loop over CCP bins} do for rep = 1 to 10 {Repeat different masks} do for img = 1 to 300 {Repeat for img in EL} do Get uncorrupted img from EL; Compute CCP of img; while CCP outside of cbin do Get random mask from AUTO; Apply random mask on uncor. img; Compute CCP of corrupted img; end Save corrupted img to CLOUDY; end end end appearing in the images used for training the classifiers, corresponding to years 2016/2017, are not repeated in the EDDIES-CLOUDY test sets.
RESULTS Vdr Expression Is Associated with Monocyte Differentiation and Patient Survival in AML VD is a compound well known to induce AML differentiation, but the role of VDR has never been investigated in leukemogenesis. To evaluate VDR expression and activity in AML, we analyzed a previously published gene expression dataset (GEO: GSE12417)  (Metzeler et al. , 2008)  that includes normal karyotype AML patients (n = 163) classified according to the French-American-British (FAB) system  (Bennett et al. , 1976). In this patient cohort, we found that Vdr expression was higher in AML subtypes with features of monocytic differentiation (AML4 and AML5) compared with immature or undifferentiated AML subtypes (AML0, AML1, and AML2). Not surprisingly, AML6 subtype exhibited a low level of Vdr expression (Figure  1A ).
, 1986; Nagler et al. , 1986) , and its activity relies on the expression of its receptor. To quantify Vdr expression in samples from healthy individuals and AML patients, we conducted an additional retrospective analysis (GEO: GSE9476)  (Stirewalt et al. , 2008) , confirming higher Vdr expression in AML4-5 subtypes compared with AML0-2 subtypes (Figure  1B ). This analysis also revealed that Vdr expression in AML0-2 patients did not differ from that observed in CD34 + cells purified from bone marrow of healthy subjects or from peripheral blood in granulocyte colony-stimulating factor (G-CSF)-mobilized healthy donors (Figure  1B ). Moreover, Vdr expression was similar between AML4-5 subtypes and bone marrow mononuclear cells (BMMCs) from healthy subjects (Figure  1B ), but Vdr expression in AML4-5 subtypes was decreased in comparison with peripheral blood mononuclear cells (PBMCs) from healthy subjects (Figure  1B ).
In the absence of transduction, there was no difference between Vdr +/+ and Vdr À/À blood cell differentiation (data not shown). Transduction of Vdr À/À hematopoietic precursors resulted in a dramatic increase in undifferentiated CD11bÀ F4/80À double-negative cells (Figure  1C ; Figure  S2B ) and relatively less mature morphology (Figure  1D ) compared with transduced wild-type (WT) cells, suggesting that Vdr inactivation is sufficient to limit myeloid differentiation in AML/MPN models. To investigate the clinical relevance of Vdr expression in the pathogenesis of AML, we examined its relationship with patients' prognosis. Because Vdr expression was low in AML0-2 patients, we analyzed the outcome of variations in baseline Vdr expression only in AML4-5 patients from the GEO: GSE12417 series (n = 62 patients).
Analysis of these AML patients also revealed increased Vdr expression in AML4-5 relative to AML0-2 subtypes (Figure  S3A ), further confirming observations from gene set databases GEO: GSE12417 and GEO: GSE9476. Moreover, in the GEO: GSE12417 cohort also, expression of CAMP and CYP27B1 correlated with survival (Figures  S3B  and S3C ). We also performed analysis on The Cancer Genome Atlas (TCGA) cohort  (Ley et al. , 2013). Vdr expression was higher in AML4-5 subtypes (Figure  S3D ), and survival analysis performed on low and intermediate cytogenetics AML4-5 revealed an increased OS with high VDR (Figure  S3E ) or high CAMP (Figure  S3F ) expression. Taken together, these data correlate Vdr/ VDR-target gene expression with improved prognosis in AML patients and with features of monocyte differentiation.
Promoter Methylation Decreases Vdr Expression in AML Cells, and Sensitivity to DNA Methyltransferase Inhibitors Is Dependent on Vdr Expression To evaluate the molecular mechanisms of decreased Vdr expression in AML cells, we focused on the methylation profile of CpG islands  (Akalin et al. , 2012)  in methylation-susceptible Vdr promoter region  (Marik et al. , 2010). Analysis of the Vdr promoter from a previously published gene expression dataset (GEO: GSE18700) of 344 AML patients and 8 normal CD34+ cell samples revealed an important methylation of three clusters out of four that did not differ between AML and CD34 samples (Figure  4A ). In vitro treatment with the DNA methyltransferase inhibitor 5-aza promoted a 2-fold increase in Vdr expression relative to vehicle in HL60 cells (Figure  4B ).
VD administration did not achieve HSC exhaustion. To our knowledge, there are no consistent data in the literature to support that VD alone is able to achieve HSC exhaustion in patients despite a large number of AML patients treated with vitamin D. A refined combination of VD analogs and hypomethylating agents might be interesting to control tumor bulk and to prevent relapse. A clinical trial is ongoing addressing this question (NCT02802267). In summary, we identified a novel role for Vdr and VDR signaling in normal and malignant hematopoiesis. The increases in hematopoietic precursor number and myeloid progenitor populations in Vdr À/À mice suggest that Vdr expression is critical for normal HSC homeostasis. In hematopoietic malignancies, Vdrtargeted gene expression correlates with improved prognosis in AML patients, whereas Vdr inactivation in AML cells results in limited myeloid differentiation and increased clonogenic potential of LSCs.