dataesr/scientific-paragraphs-categorization huggingface dataset:
(2020) , the authors leverage the cGAN predictive capabilities to develop a predictive model of the IMs for some mainshock events and the corresponding AS seismic data recorded by the Pacific Earthquake Engineering Research Center (PEER) and stored in the Next Generation Attenuation-West2 (NGA-West2) database. Specifically, the implemented model was a cGAN which also exploited WGAN to provide stability during training. The authors identified 33 different IM variables in terms of peak displacement, velocity, acceleration, and time duration. Since these IMs act as random variables, there was no wellestablished mathematical framework to establish a direct relationship to predict the IM's magnitude starting from mainshock and AS seismic data. On the other hand, the cGAN model offered the possibility to perform such high-dimensional data distribution correlation conditioned by real data availability and even accounting for the stochastic nature of the underneath phenomenon.
In structural engineering to respect this task, usually more than one input accelerogram is analyzed for nonlinear time history dynamic analyses, and the codes usually prescribe their number: ASCE 7-16 requires the use of 11 records as the minimum number, whereas Italian national technical code NTC2018 requires the minimum use of 7 accelerograms. Thus, the average response spectra of the different artificial accelerograms adopted must respect spectral compatibility within a certain tolerance. Therefore, the authors adopted a limited number of spectral-compatible ground motions as a training set for their DCGAN model. The authors employed Hancock's wavelet algorithm  Hancock et al. (2006)  to adapt their earthquake database to match the desired target response spectrum. In  Grijalva et al. (2021) , the authors proposed a model denoted as ESeismic-GAN to provide data augmentation for generating synthetic seismic signals for volcano events.
Specifically, volcano origin event signals have been studied: long-period earthquakes and volcano-tectonic earthquakes. The authors implemented a modified DCGAN to flatten to 1D convolutional layers both for the generator and discriminator. The time-series training data comes from a publicly available dataset referred to Cotopaxi Volcano in Ecuador, and they have been preprocessed in the frequency domain with the FFT algorithm. The magnitude response was collected and fed to the GAN model to generate new magnitude responses based on features and information from input data. A new synthetic time series was generated by combining the generated responses with actual phases extracted during the FFT preprocessing and adopting the inverse FFT algorithm. To provide a better evaluation of the GAN during training and even for the generated seismic signals, the authors adopted the Fréchet distance metric  (Heusel et al.
Earthquake-induced ground deformations such as liquefaction-induced lateral spreading may cause severe damage to engineered structures. The authors in  Woldesellasse and Tesfamariam (2022)  used a cGAN with a 10-fold cross-validation procedure to solve the regression problem of lateral spreading from estimated horizontal ground displacement from Japan and U. S. A. databases. Since complex DL model interpretability is still a challenging task, the authors employed the Shapley additive explanations (SHAP) values  (Lundberg and Lee 2017)  to illustrate the contribution of each input feature to the cGAN model predictions.
The same authors in  Wei et al. (2021c)  used the cGAN for interpolating seismic data in the mainstream of data augmentation. In detail, the cGAN, trained on synthetic datasets, successfully removed spatial aliasing from measured signals. Unfortunately, despite the successful attempts, the scholars do not provide any comparison with other approaches' performances. Analogously, the authors in  Ferreira et al. (2019a)  attempted to recover the frequency content or the missing traces in seismic data using the cGAN. The authors propose amplitude encoding and histogram equalization to stabilize the performance of GANs on seismic data. The analyses were promising for typicalseismic processing and interpretation applications. They generated low-pass-filtered seismic images and then trained the pix2pix network to recover the high frequencies from seismic images based on the low-pass filtered input images.
2002) , and computing two metrics, i. e. the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). Although GAN is not a unique method for performing ground-roll attenuation, traditional methods require an optimal definition of various filters. In contrast, GAN can be trained to identify and filter out any random noise in the data. The authors in  Kaur et al. (2020b)  adopted a similar CycleGAN implementation from their previous paper  (Kaur et al. 2020a (Kaur et al. , 2019a) )  to perform seismic ground-roll noise attenuation. Two seismic field datasets have been analyzed: the foremost contained nearly radial ground-roll noise contamination, whereas severely aliased hyperbolic ground-roll phenomena characterized the latter. To create labels, the authors adopted two traditional techniques, the local timefrequency transform  (Liu and Fomel 2013) , and the regularized non-stationary regression  (Fomel 2009 ).
Similar findings were obtained by  Si (2020). The authors in  Oliveira et al. (2020)  extended their previous work (Borges  Oliveira et al. 2020 ), proposing for the first time a self-supervised two-step approach for ground-roll noise attenuation in prestack seismic data. In the first step, the authors leveraged a CNN U-net segmentation network to focus on those seismic images portion affected by ground-roll noise. After that, these images underwent a noise attenuation filtering process by a subsequent cGAN model while preserving noise-free signals. The authors provided two datasets with noise-free and noisy data for the two stages of the process, delivering a self-supervised method. The authors effectively combined adversarial learning, detection, and segmentation for a reliable and self-supervised ground-roll attenuation framework.
The author overcame these issues by adopting the Wasserstein loss with a gradient penalty term  (Gulrajani et al. 2017; Arjovsky et al. 2017). This fact underlines that much more research has to be done to improve the promising path offered by GAN. In subsequent studies, e. g. in  Oliveira et al. (2018a) , the cGAN has been adopted for the seismic trace interpolation problem. Specifically, the authors employed the pix2pix GAN  (Isola et al. 2017)  to reconstruct artificially masked seismic traces. Working directly with post-stack seismic datasets, the authors introduced masked vertical strips of the seismic traces in the training and test set with variable pixel widths. The authors led a comparison between a single GAN working with all different pixel widths masks and a set of GANs, each delegated to work with masks of specific pixel width.
The results have been discussed accordingly to a quantitative metric based on the Pearson correlation coefficient among reconstructed and ground truth images and even a qualitative criterion based on visual interpretability. The study evidenced that the more the masked strip width is, the less the similarity between interpolated and ground truth images, expressed through the Pearson coefficient. Furthermore, by analyzing each pixel column, the authors evidenced how the variability is higher for the central part of the reconstructed gap and is much lower at the edge of the gap, thanks to the actual image information provided by the neighbourhood of the gap. In  Oliveira et al. (2018b) , the same authors assess the performance of a cGAN for the interpolation problem in post-stack seismic datasets.
Foremost, the use of L 1 -norm or L 2 -norm makes the networks unable to preserve weak events since these metrics are more biased in the case of strong events. The authors pinpoint the cause in the pixel-wise evaluation metrics and the adversarial loss, which mainly aims to satisfy perceptual realism. Still, they do not incorporate any authenticity of the underlying physical process. The same authors in  Ovcharenko et al. (2021)  presented a modified GAN to extrapolate lowfrequency energy components in seismic data, which can be hard to capture but beneficial to mitigate cycle-skipping issues in the full-waveform seismic inversion task. It is worth noting that low-frequency components exist in the synthetic seismic dataset only because it is pretty hard to measure a high content of low-frequency components in a field measurement campaign.
In  Zhao et al. (2022)  the authors conceived an unsupervised approach to accomplish seismic denoising tasks by leveraging CycleGAN capabilities to work with unlabelled data. Their implementation, named DeGAN, adopted a two-way learning method with double generators with a U-net structure and double discriminators, which use the PatchGAN model. The training of this model adopted adversarial and cycle-consistency loss and exploited information both from synthetic signals and real field data. However, despite the advantage of working without labelled data, the authors argued about some limitations of the model, such as the signal amplitude attenuation or distortion when the signal energy varies considerably. The reason was identified by the restriction of the specific training set adopted (since DL models are severely dataset-dependent) and the inability to use L 1 -norm or L 2 -norm due to the unsupervised approach.
The same authors in  Li et al. (2020c)  employed a CycleGAN to work with unpaired data of noisy and clean data for desert regions' seismic denoising data task. Seismic data collected in desert regions requires a proper denoising procedure since those areas present some issues illustrated in  Wang et al. (2020b). In a later work  (Li et al. 2021b ), the same authors provided an interesting improvement to solve the desert seismic data denoising task. Since seismic data in desert regions is tremendously affected by spectral aliasing and low-frequency noise, the authors proposed a semisupervised learning approach with a GAN model with adaptive layer-instance normalization for Image-to-Image translation, denoted as U-GAT-IT. Furthermore, they combined the GAN framework with an attention module which exploited the resulting attention maps to guide the training process.
They applied it to a synthetic two-dimensional river channel reservoir structure test case. In a later work  (Mosser et al. 2019) , the same authors explored the adoption of a DCGAN combined with adjoint-based optimization techniques to extract synthetic stochastic posterior samples for ill-posed seismic inverse problems. Specifically, they exploited the parametric generative model offered by the DL techniques by using the gradient-descendent method to update the prior distribution of earth models to match the observed experimental results. The experimental data can be acoustic P-waves recorded by seismic testing on channelized reservoirs accounting for the facies properties (permeability and porosity) of subsurface shale layers in existing production-injection wells pairs. This optimization procedure, jointly with DCGAN's capability to parametrically deal with latent space of the noise input to the generator, allowed to reduce the variance of the posterior realizations, which become closer to the measured ground-truth observed data.
The authors in  Liu-Rong et al. (2021)  developed a GAN named CAE-SAGAN for the separation of seismic surface-related multiples. The authors implemented a CAE as the generator model with a self-attention mechanism, whereas adopted a CNN for the discriminator. The training was performed considering a set with primaries and surface-related multiples and a second dataset with primaries only. In this study, the authors adopted a supervised strategy with a loss function with a regularization coefficient. Moreover, they provided a z-score standardization procedure to preprocess the input data to mitigate overfitting issues. The same authors have obtained similar findings in  Tao et al. (2022)  in which a GAN with self-attention blocks, denoted as SAGAN, has been employed for seismic surface-related multiples suppression.
Thus, the authors have manually removed all those training samples, which could potentially bias the GAN training, i. e. seismic data collected in synclines or complex geology layouts. The study  (Durall et al. 2020)  proposes using GAN to pick the diffractions from seismic signals, compared to the tedious manually picking. Generally, the scholars create a synthetic labelled training dataset, followed by testing on actual unlabeled data, although synthetic data oversimplifies the real one. Therefore, the authors use GANs to create a semi-synthetic dataset that fills the gap between artificial and actual domains. In hydrocarbon exploration, to overcome the limits of traditional petrophysical facies classification, the authors in  Kim and Byun (2020)  adopted a CycleGAN to perform a data augmentation procedure.
Since the sensors are placed directly in the subsurface, they can capture more detailed information about the stratigraphy. However, it is strictly necessary to proceed with wave separation techniques. Divergence and curl operators are traditionally employed. Nevertheless, they may not be directly applied to VSP data unless a careful calibration of the model parameters is performed. The data-driven method based on a cGAN with a U-net as a generator has been trained with a multi-scale strategy, i. e. starting with the P-wave discrimination in simpler waveform and progressively increasing the complexity of the data. Since the separation task is quite different from standard classification or regression tasks, the authors adopted six metrics to evaluate the results in terms of accuracy, precision, recall, f1-score, R 2 for the envelope and raw data.
Fiber-optic distributed acoustic sensing systems In  Shiloh et al. (2018) , the GAN has been adopted to perform an essential data augmentation procedure, helpful for training an automatic detection and classification of seismic acoustic waves for fibre-optic distributed acoustic sensing (DAS) system. However, a reliable automatic classification based on the DAS system is still computationally and human resources demanding due to all the training database construction, e. g. collecting labeled signals for the different phenomena to classify. Additionally, overly complex approaches may invalidate real-time applications, e. g. due to processing-delay issues. To improve the database creation, the authors in  Shiloh et al. (2018)  adopted GAN for data generation and augmentation purposes, exploiting limited available real data.
The feature extractor is the multi-head self-attention mechanism, generating a spatial attention weight matrix to extract the key information of the noises. In the second step, the generator receives the original signal and the spatial attention weight matrix. Lastly, the noise reduction of the original signal is achieved with the adversarial mechanism between the generator and the discriminator. In  Zhao et al. (2022) , the authors examine deep learning models' limits in denoising DAS signal data. The performance of data-driven approaches depends on the quantity and quality of the training datasets, and thus they are strongly limited with small datasets. Therefore, the authors proposed combining a GAN with CNN. First, they used a small noise dataset to train a GAN for synthesizing artificial noise, with the final purpose of noise dataset augmentation.
Afterward, the expanded noise dataset and the signal dataset from modeling were used together to compile a synthetic training set. Finally, the CNN was specialized to de-noise the experimental data. Seismic data from DAS systems are thus often characterized by a low SNR due to the weak energy of scattered optical signals. Also, in  Dong and Li (2020) , a GAN-based model, named convolutional adversarial denoising network (CADN), has been implemented to address the DAS denoising process and improve data quality by increasing the SNR. The network architecture utilized a RED-Net model  (Mao et al. 2016) , i. e. a deep recurrent convolutional encoder-decoder structure.
2019a, c; Liao et al. 2021; Lowney et al. 2021; Oliveira et al. 2018a Oliveira et al. , b, 2019;; Pan et al. 2021; Picetti et al. 2018 Picetti et al. , 2019;; Saraiva et al. 2021; Wei et al. 2021a; Yuan et al. 2020)  and CycleGANs  (Cai et al. 2020 (Cai et al. , 2022;; Kaur et al. 2019a Kaur et al. , 2021b Kaur et al. , a, 2020a, b;, b; Kim and Byun 2020, 2021; Li et al. 2020c; Li and Wang 2021; O'Brien 2020; Wang et al. 2019b Wang et al. , 2022a;; WU Xuefeng 2021; Zhang et al. 2021; Zhao et al. 2022; Zhong et al. 2020). Nonetheless, the main pitfalls of many data-driven approaches are basically and intrinsically embedded into the input datasets themselves  (Wu et al. 2022). To implement a reliable GAN model, it is desirable to use a high-quality dataset that almost covers the entire input domain range  (Kim and Byun 2020).
This would ensure non-biased training procedures and provide accurate estimates of the input data probability distribution. Furthermore, the considerable number of learnable parameters commonly involved in complex DL model training, such as GANs, usually require large enough datasets. However, this is often economically and time prohibitive to fulfill seismic-related engineering practical purposes. Therefore, in the previously discussed papers, many authors leveraged GAN generative capabilities for data augmentation goal, in order to increase seismic datasets artificially for EEW systems  (Li et al. 2018) , for detecting earthquake events  (Grijalva et al. 2021; Li et al. 2020b, a; Wang et al. 2021) , for earthquake engineering applications and post-disaster analysis  (Yamada et al. 2021) , for geophysical seismic imaging  (Wei et al.
2021c) , noise attenuation  (Dong et al. 2022) , seismic inversion  (Araya-Polo et al. 2019; Ferreira et al. 2019b; Kim and Byun 2020; Zhang and Sheng 2020) , and for DAS systems  (Shiloh et al. 2018). However, the final generalization and robustness of the data augmentation procedure must be carefully evaluated. The actual risk is jeopardizing the original data semantic content if unrealistic synthetic signals are added to the initial dataset  (Wang et al. 2021). Furthermore, in the training procedure of the GAN model itself, some scholars have occasionally used only a small amount of synthetic seismic data generated by current traditional procedures, analytical or numerical  (Jiang and Norlund 2020; Kaur et al. 2020a; Ovcharenko et al. 2021 ). However, training GANs only on these data may provide biased models.
Analytical or numerical synthetic seismic data generated with traditional methods may be unable to capture all the real-world aspects  (Kaur et al. 2020a; Ovcharenko et al. 2021; Wang et al. 2022b). Therefore, many authors trained GAN models by mixing synthetic data with artificially added noise and real field data in the input dataset. A critical future challenge would be providing high-quality, certified, and openly accessible seismic databases to effectively support AI applications and research on both earthquake engineering and geophysical studies. Some efforts in that direction have been already carried on with seismic data curation responsibilities provided by some acknowledged institutions worldwide. It is worth mentioning, e. g. the European Mediterranean Seismological Centre (EMSC) which provides real-time earthquake information for seismic events, or the Italian National Institute of Geophysics and Volcanology (INGV) which provides high-quality and certified seismic databases for open-science and research project.
For instance, the ITalian ACcelerometric Archive (ITACA)  (Luzi et al. 2017 ) is concerned with Italian seismic events only and, on the other hand, the Engineering Strong-Motion (ESM) database  (Luzi et al. 2020 ) is related to strong seismic events mainly recorded in the European-Mediterranean regions and the Middle East. Albeit a well-trained GAN is virtually able to successfully capture the probability distribution of the input data, its limited extrapolation capabilities represent another possible drawback. Some scholars warned about the difficulty of GANs to generate synthetic seismic data which are completely different from the starting training set  (Kaur et al. 2020a ). Specifically, a dataset referred to a specific and topographically limited restricted area will probably bias the generated samples to reflect only seismic events likely to occur in that specific region, thus reflecting the seismological conditions of that region only  (Wang et al.
U-Net  (Ronneberger et al. , 2015)  and its 3D variants  (Çiçek et al. , 2016)  are designed as fully-convolutional encoder-decoder networks with skip connections to leverage contextual information and precise localization. Based on the success of Squeeze and Excitation networks (SENets)  (Hu et al. , 2018) , a squeeze-and-excitation normalization layer was introduced in a U-Net architecture by  Iantsen et al. (2020)  in HECKTOR 2020, reaching the best performance for HNC primary tumor segmentation. nnU-Net (''no new net'')  (Isensee et al. , 2021)  has recently become a popular method for various medical image segmentation tasks. Developed to deal with dataset diversities, it automates key design choices for a successful segmentation pipeline using standard 2D or 3D U-Nets. Vision transformers (ViT)  (Dosovitskiy et al.
Medical image segmentation challenges The interest in medical imaging challenges has grown over the past years. These challenges have enabled a fair comparison of algorithms developed by various research teams across the world on large curated datasets, as opposed to studies performed on non-public data, and/or diverging data splits and evaluation protocols. The number of MICCAI challenges between 2018 and 2022 has increased from 15 to 38 (25 of which feature a segmentation task in 2022), equally supported by a growing participation. A similar trend is observed in other challenges organized independently or at other venues including the International Symposium on Biomedical Imaging (ISBI), the international conference on Medical Imaging with Deep Learning (MIDL), and the annual meeting of the Radiological Society of North America (RSNA).
Data source. Compared to the dataset used in the first edition of the challenge  (Oreiller et al. , 2022) , 71 patients collected in a sixth center (CHUP) were added and distributed between the training (n = 23) and the testing (n = 48) sets. As a result, the 2021 dataset consisted of patient data and images collected from six centers as detailed in Table 1  (Andrearczyk et al. , 2021b). The data consist of PET/CT images of patients with HNC located in the oropharynx region. Inclusion criteria were head and neck cancer patients older than 18 years with primary oropharyngeal lesions of any histologic type, TNM stage and HPV status. Exclusion criteria was unavailable follow-up data. Additional information about the image acquisition is provided in Appendix A.
Training and test case characteristics. The training data comprise 224 cases from five centers (HGJ, HMR, 4  CHUM, CHUS and CHUP). The data from the first four centers originate from  Vallières et al. (2017)  which contained 298 cases, among which we selected the cases with oropharynx cancer. The test data contain 101 cases from a sixth center (CHUV n = 53) and from CHUP (n = 48). Examples of PET/CT images of each center are illustrated in Fig. 1. The HGJ, HMR, CHUM, CHUS cohorts were already used in the training set of HECKTOR 2020 and CHUV cohort was already used in the test set. The number of events in the training set is 56 out of (25%). In the test set, it is 40 out of 101 events (39. 6%). In terms of severity of disease, the distribution of TNM stages in the training set is TNM stages in the test set is I: 4%, II: 6.