dataesr/scientific-paragraphs-categorization huggingface dataset:
Overview of the Proposed Method We propose a novel approach based on unpaired-CycleGAN to overcome the scanner dependent domain mismatches while preserving the subtitle molecular-biomarker information of MRI data. The basic idea is to overcome the problem of LGG MRI data scarcity and make the small raw clinical data usable from multiple institutions for improved performance of subtype glioma classification, which consists of: (a) using unpaired-CycleGAN to map source domain MRIs (FLAIR, T1ce) to target domain MRIs. (b) The combined MRI data are still small in size because of (i) still less number of subjects, (ii) poor resolution of 3D MRI at sagittal and coronal views, (iii) large class imbalance in IDH genotype. Therefore, deep convolutional GAN (DCGAN) is used to augment synthetic MRIs across different modalities to enlarge the training data.
These mapped data are added to the target domain to obtain total data D. To further enlarge the size of training data D train for each modality, image augmentation is done by employing deep convolution GAN (DCGAN)  [35]. As the datasets have no tumor masks, the tumor regions are extracted by fixing a tight rectangular bounding box around ROI of images. These images with only tumor regions are used in a two step training strategy by 2-streams of convolutional autoencoder (CAE)  [34]. During pre-training, phase features are learned from augmented images Dtrain (T1ce-MRI and FLAIR-MRI). In refined training stage, features are fine tuned from D train MRIs in two streams which are further followed by feature fusion and two fully connected layers for prediction. Once the model is trained (green dashed box in Figure  1 ), the prediction is made on test data D test (yellow dashed box).
Unpaired Cyclegan for Domain Mapping Among many Generative Adversarial Network (GAN) models for image-to-image transformation, we selected CycleGAN  [33]  for mapping realistic images from source domain to the target domain aiming to increase the data size by combining the datasets from multiple sources. A conventional GAN consists of two-sub networks: a generator and a discriminator. A generator learns to produce fake image distribution similar to the real image distribution while discriminator learns to distinguish between both distributions. Both the networks are trained simultaneously to reach an optimal solution by minimizing the adversarial loss. In contrast, a CycleGAN uses two inputs in two streams, different from GAN that consists of one stream of input. In addition to adversarial loss, CycleGAN aims to also minimize the cycle-consistency losses.
Formulation of the Unpaired Cyclegan The idea is to learn the two mappings between the two unpaired sources of data A and B respectively. As shown in Figure  2 , MRIs (FLAIR, T1ce) from two datasets are inputs to their corresponding generators G B and G A. The two output discriminators D B and D A are to compare the corresponding real images from the synthetic ones. The objective of the unpaired CycleGAN is given as: L(G A , G B , D A , D B ) = L GAN (G B , D B , A, B)+ L GAN (G A , D A , B, A) + λL cyc (G A , G B ) (1) where L GAN (G B , D B , A, B) = E b∼p data (b) [(D B (b) -1) 2 ] +E a∼p data (a) [D 2 B (G B (a))] (2) Similarly, L GAN (G A , D A , B, A) can be defined as in (2), L cyc (G B , G A ) = E a∼p data (a) [ G A (G B (a)) -a 1 ] +E b∼p data (b) [ G B (G A (b)) -b 1 ] (3) We denote the data distribution as a∼p data (a) and b∼p data (b) respectively given the training samples {a i } N i=1 from domain A and {b j } M j=1 from domain B.
G B is the generator that takes {a} an input dataset and generates the mapped dataset { ã}, D B is the discriminator and aims to discriminate between the real {b} and augmented { ã} images. For estimating cycle-consistency and reversible mappings between the two domains, it uses explicit reconstruction error to ensure the cycle-consistency and to reduce L cyc in (3), where λ is the regularization parameter. For stable training, the least square loss is used in L GAN compared to the conventional negative log likelihood. The optimized generator and discriminator are obtained by training on the total loss in (1): G A * , G B * = arg min G A ,G B max D A ,D B L(G A , G B , D A , D B ) (4) The unpaired CycleGAN learns to map realistic MRIs from the source domain A to the target domain B without any correspondence from small datasets at both ends therefore it is named as unpaired.
Data Augmentation by Deep Convolutional GAN This part explains the data augmentation block in the pipeline from Figure  1  to generate augmented synthetic data Dtrain. In medical imaging, insufficient training dataset is partially resolved by slicing the 3D-MRIs to 2D slices with the maximum number covering tumor regions. Usually if data has enough resolution in all directional views, 2D slices are extracted from all directions of 3D volume (e. g. , axial, coronal and sagittal). However, this strategy helps to some extent to increase diversity in training set and prevents the model from over-fitting. Since, the size of the datasets A and B are quite small which is still not sufficient to train a good predictive model. In this regard, we have used deep convolutional GAN (DCGAN)  [35]  for enlarging the training data size by generating augmented images for both modalities T1ce-MRIs and FLAIR-MRIs.
Although the CycleGAN generated data are also considered as synthetic but because it preserves the anatomy of brain image from molecular level of tumor to the whole brain image unlike DCGAN, we call it here as mapped data. While the augmented distribution of data from DCGAN presents some differences, for instance; size of tumor, tumor location and introduce other structural differences. A detail description of the architecture is given in Table  1. Unlike CycleGAN from Section 2. 1 which accepts input as an image, here, the generator G learns a mapping from an input vector z (typically from a uniform distribution p z ) and maps to an image y in the target domain p g. While discriminator D learns to distinguish between the true images y and the fake images G(y). While training, both G and D learn simultaneously where G aims to generate images with high probability to achieve the goal p g = p data and look more real.
Conversely, D learns aiming to discriminate the fake and true images. This is obtained by optimizing the given adversarial loss function in Equation (  5 ): L GAN (G, D) = E y∼p data logD(y) + E z∼p z log(1 -D(G(z))) (5) where G tries to minimize the loss function L GAN for images with y p data and D tries to maximize L GAN for images with y∼p data simultaneously. The aim is that G learns to produce more realistic augmented images that D might not differentiate from the real ones. For each MRI-modality, DCGAN is trained separately to synthesize the augmented images from the corresponding modality. A vector of 100 random samples drawn from a uniform distribution is given to the generator network as input to generate the augmented MR images and the discriminator compares the original and augmented images to output a decision: real or fake?.
Review of Multi-Stream Convolutional Autoencoder and Feature Fusion For the sake of convenience to the readers, a brief overview of the classifier is given in Figure  4. After overcoming the possible mismatches between the two domains A and B, we have obtained data D in total. Moreover, both the datasets are available without the tumor masks so to allow the network to focus on learning the tumor characteristics, we have fixed a rectangular tight bounding box on the ROI (region of interest) of each image. This step is further proceeded with a two-stage training strategy based on our previous work on Multistream Convolutional Auoteoncder  [25]  as a classifier. By doing so, a noticeable performance is obtained from our empirical test results. A detailed architecture of one stream of classifier is described in Table  2.
Datasets The datasets used in the evaluation are provided by two different hospitals for patients with known 1p/19q codeletion/non-codeletion and IDH mutation/wild-type status: USA dataset from University of California San Fransisco and France dataset from Department of Neurosurgery, University of Paris, GHU Paris, Sainte-Anne Hospital. Note that the data are unpaired which means that both the data sources are from two different institutions having no subject in common. Unlike other MRI open datasets, the patient's tumor mask annotations and other demographic characteristics are not available for both datasets. Based on the availability of modalities, class labels and quality of scan, 82 subjects were selected out of 87 from France dataset and 79 subjects out of 95 were used from USA dataset.
The data consists of 3D brain volume but we have used slices from only axial views as the number of slices were not sufficient in the coronal and sagittal views in majority of subjects. The summary of the datasets is given in Table  3a. Partition of Dataset for Multiple Runs: Since deep learning requires heavy computation, we adopted the commonly used approach by averaging several test runs as the performance index (rather than cross-validation in conventional machine learning). This is done as follows: for each new run, a new partition is performed to split the dataset into subsets of training (60%), validation (20%) and testing (20%), where strictly patient-separated partition is applied (i. e. , MRI slices from each patient would only be used in one of the subsets). Then, the training process is repeated, i.
e. , applying GAN data augmentation on the new training subset, followed by pre-training of GAN augmented data and refined-training of multi-stream CAE with mapped MRIs (using the same hyperparameters and network architecture in all runs). After that, the testing process is applied by using data from the new test subset for feature extraction and classification. The test performance obtained from such 5 runs are then averaged for the final performance evaluation. Based on the confirmed histological identification of subjects as LGGs, we considered two case studies as shown in Table  3b. Two modalities of images, T1ce-MRI and FLAIR-MRI were used in the tests. Case-A: This case was applied for classifying subtype-LGG 1p/19q codeletion and non-codeletion. From Table  3b , one can see that 77 patients are 1p/19q codeleted and 84 patients are non-codeleted.
Observing that the tumor size varies in each subject, 10 slices for each glioma were extracted from each 3D scan for training the multi-stream CAE classifier. Case-B: This case was designed for classifying IDH genotype. From Table  3b , one can see that 137 patients are labeled as IDH mutated and 24 patients as IDH wild-type. Unlike Case-A, the same datasets have large class imbalance for IDH genotype. Therefore, 3 time slices have been extracted for patients from IDH wild-type class.
Metrics for Performance Evaluation To evaluate the performance of diffuse LGG-subtype classification, objective metrics were used based on the following four kinds of samples. This step has an impact on the performance. The clinical 3D volume scans in both the datasets were unregistered. Thus, the anatomical images from FLAIR and T1ce scans were registered to 1 mm MNI space template. In addition to this, the bias field correction and skull-stripping steps were performed using FSL  [39]  and ANTs  [40]  tools. To save computation, slices were rescaled to a 128 × 128 size and then normalized to range [0, 1]. For training CycleGAN, we used all the axial cross sections that contained artifact-free brain tissue from each subject. While training DCGAN for generating augmented images, all images with tumor regions were selected.
Performance Evaluation on the Impact of Individual Parts The purpose of this study is to establish a framework that can enable data to be used from multiple domains for improved performance. To investigate the effectiveness of our approach on each case study, first we had to choose which dataset should be mapped to the other. In this regard, we did a primarily test using multi-stream CAE classifier  [34]  on the USA and France datasets separately and then combined them without applying domain mapping. Then we compared the performance with combined dataset obtained from after domain mapping. Furthermore, we also examined the effect of using GAN augmented data for pre-training. Finally we applied all these methods in the proposed scheme and evaluated the overall performance of the pipeline.
Test Performance Comparison on Data without Domain Mapping on the Effect of Pre-training: Figure  6  shows the test performance from using individual dataset (USA dataset, France dataset) and the simply combined two datasets (USA+ France). Noting in this set of experiments, we also compared results from with and without using GAN augmented data for pre-training (shown in blue and red bars in Figure  6 ). Observing the results from Case-A in Figure  6  (Left), it is found that the USA dataset gives better prediction of 66. 87% on test data as compared to the France dataset which is 61. 47%. After, when data were combined without mapping, the performance increased slightly up to 67. 03% but not to a noticeable extent compared to the size of second dataset added, possibly because of the domain mismatches.
Note that the pre-training effect increased the performance by nearly 3%. Observing the results from Case-B in Figure  6  (Right), one can see a similar trend of behavior but additionally improved performance is observed for IDH1 mutation/IDH1 wild-type classification. Again for this case, USA dataset gave better test result which is 70. 24% and increased up to 72. 38% when combined with France dataset. Note that the pre-training effect increased the performance efficiently by 5%. In this experiment, the reported test results were averaged over 3 runs for both cases. Hence, USA dataset was set as the target domain and France dataset as the source domain in training the unpaired CycleGAN. Effect of Domain Mapping: Domain adaption was then applied by mapping the French dataset to the USA data domain.
Overall Performance of the Proposed Scheme In this set of experiments, we evaluated the overall performance of the proposed pipeline (see Figure  1 ) for both Case-A and Case-B. The final results from 2-stream feature fusion and CAE classifier on the combined dataset after mapping are shown in Table  4. Columns 3 and 4 show the test results from applying a single modality from mapped dataset, while column 5 shows the test accuracy from fusion of 2 modality information. Noting that the test accuracy on FLAIR-MRIs is better as compared to that on T1ce-MRIs in both case studies, probably due to better contrast of tumor regions against the background tissues. Observing the results from using 2 modality inputs, the features learned from both scans were combined through feature fusion layers which increased the prediction on test accuracy.
Comparison with State-of-the-Art and Discussion To further evaluate the proposed scheme, we compare our performance with several state-of-the-art results on prediction of 1p/19q deletion/non-codeletion and IDH1 mutation/wild-type. There exists some reported work on these molecular-subtype gliomas using open datasets  [41, 42] , but we have mostly selected few ones for comparison that have used clinical datasets as shown in Table  5. It is worth mentioning that these comparisons can only be used just as an indication because they were applied to different datasets with different scan types, MRI modalities and patient's characteristics. For instance, Matsui et al. [6]  used residual network-based deep network that required more modalities of data (FLAIR, T1ce, T1, T2), including PET and CT scans in addition to other side information of patients as numeric data.
All these methods were based mostly on using a single and relatively large dataset. Observing the last row for each case in Table  5 , our proposed scheme indicates relatively good performance on using moderate data size of 161 patients with two MRI modalities obtained from two institutions, provided with no segmented tumor masks or other patient's/tumor's side information for assisting classification. This also supports that the performance is comparable to the state-of-the-art. Limitation and Future Work: Despite the method is promising in domain mapping and molecularsubtype classification, some challenges remain before putting this tool into clinical usage. Further improvement is required to increase the test performance, for example, to make the network work more robustly, more datasets from multiple institutions should be added through domain adaptation to enlarge the training data.
Conclusions Since the introduction of molecular-markers in LGGs was implemented recently in the WHO 2016 classification, the data availability is quite low. The proposed scheme has been tested to enlarge the clinical datasets from independent sources and to overcome the domain mismatches of the datasets for the prediction of molecular-subtypes for LGGs. The results obtained on the test dataset have shown a noticeable increase in the performance compared to when the dataset was not mapped (74. 81%, improved by 7. 78% on 1p/19q codeletion status) and (81. 19%, improved by 8. 81% on IDH mutation status). It shows that unpaired CycleGAN has maintained the subtitle-molecular information while mitigating the domain differences. The effect of pre-training and its effect with GAN augmented images has both resulted in increased generalization performance of multi-stream CAE classifier.
Crowdsourced data is integrated with other sources of information under the ARISTOTLE project, contributing to rapid (3 h) situation reports for the 24/7 Emergency Response Coordination Center (ERCC) unit of the European Union (EU) Civil Protection Mechanism who coordinates the delivery of assistance to disaster-stricken countries. The ERCC also makes these situation reports available to EU national civil protection agencies  (Michelini et al. , 2017). LastQuake utilizes visual communication to erase language and literacy hurdles: felt reports are collected through a set of cartoons describing different shaking and damage levels, each cartoon representing an intensity level of the European Macroseismic Scale 1998  (Grünthal, 1998 ; Figure  1 ). This approach has been validated by comparing it with the USGS's DYFI macroseismic questionnaire system as well as with independently and manually derived macroseismic datasets  (Bossu et al.
, 2017). Note that felt reports corresponding to intensity 11 and 12 are considered as unreliable and excluded from analyses. Cartoons are also used to offer guidance through safety tips after strong shaking or during a tsunami threat  (Fallou et al. , 2019). LastQuake is the only information system providing information for only felt earthquakes, regardless of their magnitude. Felt earthquakes are automatically identified through what is called "crowdsourced earthquake detections, " which detect eyewitnesses' information-seeking behaviors through their digital footprint immediately after shaking  (Bossu et al. , 2018b). Three independent sources of crowdsourced detections are in operation, based on rapid changes, respectively, in the number of users accessing EMSC websites  (Bossu et al. , 2012) , of tweets (messages posted on the microblogging Twitter site) containing the keyword "earthquake" in various languages  (Earle et al.
Collected Open Comments During the November period, 34% of the felt reports associated with an earthquake were also associated with an open comment, a proportion similar to that observed for other earthquakes. We do not currently exploit these comments for situational awareness. They are generally shared by eyewitnesses to express emotions (anxiety, fear. ) and/or felt experience in plain words. They are visible on EMSC's websites and on the app where users can vote in favor or against a comment through a system of up and down arrows. Reading other eyewitnesses' experiences is an appreciated feature of the app often mentioned as such on the IOS and Android app stores. Moderation is minimal due to language barriers, and the volume of comments typically no more than a few comments are deleted per earthquake, with special attention on comments with significant negative votes.
During the November period, there were 14. 2 million cumulated views of the 2,272 @LastQuake tweets. The most viewed was our dedicated Twitter moment with 238,000 views (a compilation of tweets gathering key information on the earthquake and its effects) and which was pinned (i. e. , remained the first message of the @LastQuake timeline) during the duration of the November period. The second most popular with 140,000 views was our first tweet published on November 26, explaining that earthquake prediction does not exist and should not be trusted (Figure  10 ). On November 26, the day of the mainshock, there were 586 tweets published, mainly automatic ones, relating to the numerous felt earthquakes in Albania (Figure  1 ). This was too high a number of publications to be easily followed. Furthermore, information about the mainshock had been rapidly replaced by new tweets, and so the Twitter moment had to be put in place to correct this and make the mainshock data accessible.
DISCUSSION AND LESSONS LEARNED We analyzed the use of the LastQuake multichannel information system in the aftermath of the deadly M 6. 4 Albania earthquake of November 26, 2019, to identify its strengths and weaknesses in answering public desire for earthquake information, as well as how the data it collects from eyewitnesses can contribute to rapid situational awareness. We acknowledge that since this study is focused on a single country, Albania, the cultural context, risk perception, and technological culture are likely playing a role, but we still believe there are lessons to be learned. This earthquake is of specific interest because the number of LastQuake app users in Albania was significant (25,000) when the mainshock struck thanks to a previous M 5. 6 earthquake, which affected the same region on September 21, 2019.
A similar prioritization issue was identified with the Twitter quakebot. They will need to be corrected to ensure both on the app and on Twitter that essential information, such as reports related to the mainshock, remains easily accessible during an appropriate time window and also to limit information overload due to multiple aftershocks, notably by reducing the number of tweets automatically published for small-magnitude aftershocks. Despite slow and sometimes disrupted app services due to the high demand on the EMSC's internet infrastructure and the lack of an Albanian language version of the LastQuake app (which was only released in January 2020) the overall satisfaction was large with a rating for Albania of 4. 8/5 (based on 504 ratings on January 24, 2020) in the IOS store where statistics per country are available.
Focus then shifted more specifically on earthquake prediction after such a claim spread on the messaging app WhatsApp. Because such claims, observed after many significant earthquakes around the globe can have advert consequences such as panic evacuations, we believe that both EMSC but also the seismological community should be better prepared to debunk them by communicating rapidly with the public. We thus advocate for the establishment of a clear, concise statement on this topic, explaining differences between prediction, aftershock forecast, or early warning, a statement that ideally would be endorsed and shared by the seismological community and made available in multiple languages. EMSC and other actors could then automatically publish it after large earthquakes both on Twitter but also on the LastQuake app.