dataesr/scientific-paragraphs-categorization huggingface dataset:
Data augmentation Open dataset Uveitis Clinical decision support based on artificial intelligence (AI) methods has increasingly been employed in medical applications to support medical diagnosis. Developing efficient AI methods, however, depends necessarily on the availability of sufficiently large amount of data to provide reliable results. But, in medicine, it is not always possible to find sufficient amount of real data on all pathologies, particularly, for rare diseases. This paper proposes a methodological framework for generating synthetic data using data augmentation techniques combined with epidemiological profiles. It focuses on Uveitis, a rare disease in ophthalmology, which is difficult to diagnose because of the disparity in prevalence of its etiologies. The generated synthetic data have been qualitatively validated by specialist ophthalmologists and quantitatively tested using machine learning methods.
, 2018)  and 60. 6 per 100,000 persons  (Zhang et al. , 2020). Thus the need to found a dataset, for the scientific community, to improve the potential of its diagnosis. Uveitis causal epidemiology varies according to genetic factors, environmental factors, disease definition, certain ophthalmological entities, paraclinical investigations and so forth. These cause wide heterogeneity of the disease  (Bonnet & Brézin, 2020) , which complicates its data model representation and generation of realistic data. These challenges and the low number of cases of Uveitis as well as the multidisciplinary management of the disease have prompted doctors to look for tools to help diagnose these pathologies, in order to shorten the delays in establishing etiological diagnosis. To improve clinical diagnosis, clinical decision-making is usually aided by Clinical Decision Support Systems (CDSS), which can utilize a knowledge-based or AI-based approach to derive its decisions.
Evaluation shows very promising results, with more than 55% of the generated data assessed as good or excellent, which is very satisfactory for synthetic data generation for rare diseases. It also shows the proposed approach is consistent in generating synthetic data, of different sizes, that meets Uveitis pathology and the distribution of its etiologies and profile characteristics, achieving more than 80% diagnosis prediction accuracy for datasets of 2000 records or larger. Although the developed framework is for Uveitis, however the followed approach is generic, and can be employed for other rare disease, and the number of generated synthetic samples can be increased according to the need of the employed AI algorithm. The rest of paper is organized as follows: section 2 presents related work on synthetic data generation, section 3 describes epidemiological profile of Uveitis and the developed synthetic data generation method.
Related work Synthetic Data (SD) are data created by a model that has been trained or built to replicate real data (RD) based on its distributions (i. e. , shape and variance) and structure (i. e. , correlations among attributes)  (Hoptroff & El Emam, 2019). It can be employed for various applications, for example, data augmentation, which is used to balance datasets or supplement existing data before training a machine learning model, or privacy preservation, which is used to allow secure and private sharing of sensitive data. SDG has been studied in healthcare for a variety of modalities, including biological signals  (Hernandez-Matamoros et al. , 2020) , medical pictures  (Han et al. , 2018) , free-text content in electronic health records (EHR)  (Guan et al. , 2018) , time series smart-home activity data  (Dahmen & Cook, 2019) , and EHR tabular data  (Yale et al.
It is interesting to note that epidemiology of Uveitis changes over time in the same geographical region. Thus, in Japan, Behcet's disease, which was the first cause of Uveitis 30 years ago, now occupies sixth place, behind sarcoidosis and Vogt-Koyanagi-Harada (VKH) disease. The place of epigenetic factors is also better identified. Indeed, the risk of Uveitis associated with Behcet's disease is very high in Turkey. Surprisingly, the incidence of the disease in patients of Turkish origin migrating to Germany quickly reaches that of the German population  (Scardapane et al. , 2018). Thus the proposed method aims to generate a realistic dataset that could represent French patients, both treated and followed up for Uveitis, and respect the epidemiological characteristics already mentioned. It needs to draw up an epidemiological profile of Uveitis extracted from the most recent French descriptive studies.
The patient were referred to the Regional Competence Center dedicated to systemic and autoimmune diseases for diagnostic and/or therapeutic management. The non-inclusion criteria were as follows: patients aged fewer than 18, patients with  After discussion with the specialist physicians in ophthalmology, the following points were considered to generate profiles that will be used for synthetic data generation: • Our reference profile is constructed based on the three studies described above. For each etiology, we calculated an average prevalence weighted by the size of each of the three populations following equation (  1 This resulted into the profile detailed in Table  B. 1 (appendix), containing all the considered etiologies, those mentioned by these three studies and taking into account the recommendations of our specialist physicians.
Method In this section, we describe our methodology to generate a Synthetic Dataset for Uveitis pathology. To achieve, specialist physicians, in ophthalmology, were involved in profiling and characterizing Uveitis, based on the medical data collected from the above studies, and in validating the generated dataset. The generated and validated dataset is made available on this link. 1 he dataset is made available for the scientific community to accelerate research and innovation on the diagnosis of Uveitis using ad-  vanced AI techniques. This dataset contains synthetic data for patients treated for Uveitis, based on the epidemiological profile, as described in Table  3.
Data generation protocol To generate new synthetic dataset, we undertook the following steps: 1. Original or base dataset creation: Since there is no real dataset of Uveitis patients available, we opted to generate an initial or base (or original) realistic dataset, which will serve as a training base for the data augmentation model (Fig. 2 ). To generate this dataset, we used two elements: (1) the epidemiological profile of Uveitis, partly described in Table  2 , is used to determine the number of patients by etiology, and (2) the description provided by the SUN Working Group (The Standardization of Uveitis Nomenclature , SUN), to describe the results of the clinical examination within each uveitis etiology. Our generated dataset includes 200 lines, each line describes the result of the clinical examination of a patient followed for uveitis, presenting with the associated etiology.
Base dataset creation algorithm To generate our base or original realistic dataset, we initially created a new dataframe with one column for etiologies and 27 columns for examination results and other disease features. We developed an algorithm that automate the synthetic data generation. The developed algorithm is detailed in Algorithm 1, described below. First, it defines the size of the dataset, which was 200 rows (line 2); this size allowed us to distribute the different etiologies on the etiology column, each according to its corresponding frequency as per the epidemiological profile already defined (lines 3 to 5) of Algorithm 1. Secondly, it processes etiologies; for all rows of each etiology, it goes through the columns, and fills each column with the corresponding values according to their frequencies, as described in the knowledge base (lines 6 to 9), while taking into account the conditions and relationships between some features or columns, as defined by the specialist physicians.
The autoencoder aids the original GAN in learning the distribution of multi-label discrete variables, in this Case 3. Architecture of medGAN: the discrete x comes from the source data (original dataset), z is the random prior for the generator G; G is a feed-forward network with shortcut connections, as shown in the right hand side of Fig. 3 ; An auto-encoder (Enc and Dec) is learned from x; The same decoder Dec is used after the generator G to construct the discrete output. The discriminator D tries to differentiate real input x and discrete synthetic output Dec(G(z)). As a contribution, medWGAN have used an improved generative network called WGAN-GP (Wasserstein GAN with gradient penalty) instead of the general GAN. It uses the same structure as that of medGAN shown in 3  (Baowaly et al.
Evaluation and results The proposed method is evaluated in two different ways. In the first, a sample of 2000 synthetic data generated by the proposed method was evaluated and examined manually by medical experts on their correctness and validity. In the second, datasets with different sizes, generated by the proposed model, were tested using various machine learning methods on their diagnosis prediction accuracy to assess its consistency and scalability. In both ways, the aim is to evaluate the distribution of etiologies and features in the base or original dataset compared to those in the generated datasets, and their scalability consistency of generating synthetic data with different dataset sizes.
Expert evaluation of model generation For the first generated 2000 synthetic records by the proposed model, we noticed that the error rate is higher within the class of granulomatous uveitis. This is due to the unbalanced nature of our base or original dataset, given that granulomatous uveitis accounted for a quarter of the dataset compared to non-granulomatous uveitis, which represents the remaining three quarters. To address, to balance the data, new records of granulomatous uveitis were added to the base or original dataset before training the model. These records were generated by the medWGAN network and validated by the specialist physicians, and concatenated to the base or original dataset. This created a balanced dataset of 290 patients that we used as the base or original dataset for the initial training of our model, using 1000 epochs.
To assess it correctness, a second training using the original dataset, using 500 epochs, was used to generate a new synthetic dataset. The generated dataset was assessed to have the same distribution of etiologies as that of the original dataset, and respects the defined epidemiological profile of Uveitis. The resulting distribution of etiologies is shown in Fig. 4. Similarly, the distribution of generated values of features or columns, e. g. medical tests, was assessed. As shown in Table  3 , results show that the model kept almost the same distribution in the generated dataset.
Qualitative evaluation To assess the correctness and validity of the generated data, three ophthalmologists examined a sample of 10% randomly selected from the 2000 generated records, generated as a representative dataset for manual expert evaluation. The 2000 dataset size was deemed a suitable representative dataset, by medical experts, for the possible tedious manual expert evaluation, larger sizes may not provide additional significant evaluation gains, as shown by quantitative evaluation (see 4. 3). To ensure a representative qualitative evaluation of the proposed GAN-based work, 200 samples were randomly selected from the generated dataset, randomly shuffled the order, before manual expert evaluation by the ophthalmologists. Our specialist physicians were asked to determine how realistic those records are, using three classes of description: "Poor", "good", or "excellent" (excellent being most realistic).
Additionally, 40% of records, which got a "poor" label, belonged to rare etiologies with low prevalence of less than 4%, from amongst all other rare etiologies, of which 78% were labeled as "poor" and 22% as "good". The remaining 55%, of evaluations, were classed between "good" and excellent". Therefore, these results are very satisfactory for, a first version of, a dataset on Uveitis, as a rare disease. As more medical information and descriptions become known and available about the disease, the quality of the generated data can, accordingly, be improved.
Quantitative evaluation The aim for the quantitative evaluation is three folds: to test the balanced distribution of etiologies and Uveitis features in accordance with its profile characteristics, to assess validity of the consistency and scalability of the proposed method and to determine the suitable dataset size that generates reasonable prediction accuracy. For the first, on several generated datasets, statistical distribution was conducted to ensure the proposed data generation model obeys Uveitis profile characteristics as defined by the base or original dataset. Results show that our generated dataset were of good quality and draw very similar distributions, as depicted in Fig. 4  and Table  3. The proposed model kept a similar distribution of etiologies and disease features in the generated data as that in the original dataset.
For Uveitis as a rare diseases, which require a specific distribution, maintaining similarity of distributions between real data and generated data is important. Results confirm that our generated data meets the requirements of a realistic dataset. For the second, i. e. to assess the consistency and scalable data validity of the proposed synthetic data generation model, several datasets were generated with different sizes. Several machine learning methods, to draw a spectrum of behavior, were, then, applied to measure the diagnosis prediction accuracy of Uveitis etiologies. These assess how ML methods, in terms of accuracy, behave as data generation scales up or dataset size increases. For valid synthetic data generation consistency and scalability, of the proposed method, for separately generated datasets, ML methods should achieve improving prediction accuracy with increasing data sizes, until a datasize threshold.
If there is inconsistency in the generated data for different sizes, diagnosis prediction accuracy for the ML methods would show random behavior. Six ML methods were selected from across a set of popular and commonly used ones for prediction to cover different types of models. The selected ML methods are Random Forest, Support Vector Machine (SVM), Neural Network (NN), Naive Bayes (NB), K-nearest neighbor (KNN) and Decision Tree (DT). To conduct the experiments, 10 datasets were generated separately, with sizes ranging from 1000 to 10000 records. Sets of these 10 datasets were generated separately, using separate runs with 500 epochs each. The six selected ML methods were then applied on each set, and the accuracy results of each for each run were averaged. As depicted in Fig. 6 , results show that, for the majority of the six tested ML methods, diagnosis prediction accuracy increases with data sizes and levels out for datasets larger than 2000 patient records (thus the selected dataset size for expert evaluation, see section 4.
2), with accuracy values ranging between 70%, for smaller datasets, to more than 80%, for larger ones. Some ML methods achieve, relatively, marginal accuracy gains for larger datasets, one achieves lower accuracy with larger data sizes. The aim, in this evaluation, is not to improve prediction accuracy of ML methods, but to evaluate the consistency of the generated data validity and to assess the scalability behavior of the proposed method for different generated dataset sizes. As shown, the proposed model is consistent in generating data for synthetic electronic health records for Uveitis pathology. It achieves consistent validity across separately generated datasets, and scales well to different generation sizes.
Conclusion This paper presents a methodological framework for synthetic data generation, for rare diseases, to enable accelerating the use of AI approaches to supporting their diagnosis. Our methodological framework was developed in collaboration with expert ophthalmologists on Uveitis drawing on the medical and scientific expertise of the profiles of disease. The proposed framework generates synthetic data based on the epidemiological profile representative of France population through three steps: in the first, the generation of a base or original (realistic) dataset validated by doctors, in the second, the development of Uveitis characteristic profile model, and in the third, an automatic generation of synthetic dataset by MedWGAN. The generated dataset has been evaluated by specialist physicians, making it the first publically available dataset on Uveitis.
Both qualitative and quantitative evaluations show very promising results, with more than 55% of the dataset assessed by medical experts as "good" or "excellent" and achieving consistent generation of data of larger dataset sizes, with ML methods consistently obtaining more than 80% of diagnosis prediction accuracy, of Uveitis etiologies, for dataset of 2000 records or larger. In perspectives of this work, next is to enrich medical and scientific characteristics of Uveitis, increase the size of base or original data validated by doctors to create a richer model to generate more accurate representative dataset of the rare disease. The objective is to make our work available to form a community of ophthalmologists in order to generate more accurate and reliable dataset. A second objective is to compare our sample with real dataset samples extracted from hospitals, this process is long since the prior agreement of the patient in the RGPD framework must be obtained.
First expert validation: The three ophthalmologists participating in this work have validated the base dataset of 200 patients, which was generated, using Python script, taking into account the clinical characteristics of each etiology. 200 patient records were deemed sufficient by medical experts for manual thorough examination as a realistic representation. Each expert has examined the document, line by line, including the values generated for each clinical observation, as described in Fig. A. 1 (Appendix A). At the end of this step, we obtained a dataset of 200 synthetic patients whose etiological diagnosis is labeled by experienced doctors. 3. Data augmentation: In this step, we used the medWGAN model to generate a new dataset of 2000 patients, based on the learned rules from the base dataset, already validated by ophthalmologists.
2. 2 First expert validation: The three ophthalmologists participating in this work have validated the base dataset of 200 patients, which was generated, using Python script, taking into account the clinical characteristics of each etiology. 200 patient records were deemed sufficient by medical experts for manual thorough examination as a realistic representation. Each expert has examined the document, line by line, including the values generated for each clinical observation, as described in Fig. A. 1 (Appendix A). At the end of this step, we obtained a dataset of 200 synthetic patients whose etiological diagnosis is labeled by experienced doctors. 3. Data augmentation: In this step, we used the medWGAN model to generate a new dataset of 2000 patients, based on the learned rules from the base dataset, already validated by ophthalmologists.
Algorithm 1 : 1 Base dataset generation algorithm. VARIABLES : df : DATAFRAME len_df : INTEGER Etiology : COLUMN etiology : STRING etiology_records : LIST etiology_frequency : FLOAT unique_value : STRING value_list : LIST value_frequency : FLOAT INPUT : empty dataframe with named columns OUTPUT : generated dataframe 1 begin 2 len_df ← 200 3 Foreach etiology do 4 etiology_records ← etiology × etiology_frequency ×len_df 5 Etiology ← etiology_records 6 Foreach etiology do 7 Foreach column ≠Etiology do 8 Foreach unique_value do 9 value_list ←unique_value ×value_frequency ×etiology_records 10 Return df 11 end.
The results of the multitaper localization are shown in Figure  1. We make use of the VenusTopo719 topography model  (Wieczorek, 2015a)  and the MGNP180U gravity solution  (Konopliv et al. , 1999) , both of which are based on the final Magellan mission datasets. The map in panel (a) shows the total power of the 9 localization windows summed in the space domain with the target localization region outlined by the white contour. In panel (b) we present the global and localized spectral admittance and correlation of gravity and topography (see Section S6 in Supporting Information S1 for the definition of these quantities). The localization leads to an increase in the admittance of about 30% over the entire spectrum, which is caused by the exclusion of highland regions that have high topography and low gravity.
10. 1029/2023GL103847 5 of 11 To statistically evaluate the uncertainties of our model estimations, and considering the relatively large number of free parameters in our problem, we opted for a Bayesian sampling technique that provides the posterior probability of each parameter. We made use of the DYNESTY package, which is a Python implementation of the dynamic nested sampling method  (Speagle, 2020). Nested sampling estimates the marginal likelihood and the posterior distribution by sampling within nested shells of increasing likelihood. The likelihood function adopted in our inversions is described in Section S4 in Supporting Information S1.
Gravity and topography studies of Venus have mostly argued against the existence of a low-viscosity zone in the upper mantle (e. g. ,  Nimmo & McKenzie, 1998). However, most of those studies either did not perform inversions and limited the analysis to a few models representative of Earth-like scenarios (e. g. ,  Herrick & Phillips, 1992; Kiefer et al. , 1986; Kiefer & Hager, 1991; Steinberger et al. , 2010). Moreover, gravity investigations prior to 1992 used data from the Pioneer Venus mission whose resolution was limited to degree 18. From a different perspective, studies by  Huang et al. (2013)  and  Rolf et al. (2018)  estimated geoid anomalies for Venus using three-dimensional mantle convection models. They showed that the presence of a thick LVZ, ranging from the base of the lithosphere down to the ringwoodite-bridgmanite phase transition at 730 km depth, was inconsistent with the observations.
Data Availability Statement A Python routine to estimate the gravity and topography predicted by the dynamic loading model can be found in  Maia (2023). The localized spectral analysis was performed using the open-source package Pyshtools  (Wieczorek & Meschede, 2018) , while the posterior parameter distributions were estimated using the Dynesty package  (Speagle, 2020). The spherical harmonic model of the gravity field used in this study can be found in  Sjogren (1997) , and the topography data set is from  Wieczorek (2015b). The perceptually uniform color maps used in this work are from  Crameri (2018).
Another work in this category is HelDroid  ( Andronio et al. , 2015 ) , which works on mobile systems, and detects if an application attempts to lock or encrypt the device without the user's consent or if it displays some ransom request. Kharaz et al. (2016)  introduced a dynamic analysis system, called UNVEIL, based on the idea that, to mount a successful attack, ransomware must tamper with the user's files. UNVEIL automatically generates an artificial user environment able to monitor processes' interactions with user data and changes to the system's desktop as telltale signs of ransomware-like behaviour. Other solutions, e. g. , the ones surveyed by  Kharraz et al. (2015)  , hinge on detecting and preventing (zero-day) ransomware attacks by looking at I/O requests and protecting the Master File Table (MFT) in the NTFS file system.