dataesr/scientific-paragraphs-categorization huggingface dataset:
Two major ways of tackling this issue have emerged in the literature. The first approach to address the long-tail dataset issue consists in balancing the dataset itself, for which the most popular methods are data augmentation or data generation. The technique of subsampling, which removes data from classes with large samples is not considered here because it wastes a large amount of useful information. Data augmentation consists of artificially augmenting the number of images of classes with fewer data in order to increase their impact on the model training  [34]. There are numerous methods to increase images datasets, such as resampling, geometric transformations, kernel filters (sharpness, colours and blurring) or feature space augmentation  [35, 36]. Apart from resampling, which simply uses the same image multiple times during the training phase, data augmentation consists in transforming existing images in the dataset and induces changes in order to mimic changing conditions and limit the overfit of DL models.
Data generation, through a generative adversarial network (GAN), variational autoencoder (VAE) or neural style transfer, is another way to increase a dataset's size  [35, [37] [38] [39] [40] [41]. GANs are Deep Neural Networks (DNN), which, through learning thanks to existing data, are able to generate new images in the same representation space (i. e. , images that "look like" those of the GAN training dataset). However, similarly to data augmentation, it is important to induce variations in the artificial dataset to prevent overfitting. Data can also be generated through visual engines, such as Unreal or Unity. In the field of object detection, a few studies have directly cropped out objects of their original scenes and pasted them in new scenes  [42, 43]. Furthermore, a number of studies have simultaneously used both data augmentation and data generation  [44].
The second approach to tackle the long-tail dataset issue consists in accounting for the imbalance of samples for each class in the training algorithms itself  [45]. The focal loss, introduced by  [46] , adds to the Cross-Entropy (CE, a value used to improve deep model predictions during the training stage) two variables taking into account the ability of the networks to discriminate all classes and the proportion of each class in the dataset. In the same spirit,  [47] [48] [49]  propose to modify the CE with respect to dataset imbalances. Furthermore,  [48]  propose to control the classification space and margin between classes to boost the classification of classes with few data. It has been shown that such methods can significantly improve the accuracy of DL model predictions  [41].
Scarce Data Deep learning models are efficient when a reasonable number of images per class is available during the training phase. However, ecosystems are composed of a large part of rare species, with only a few images in training datasets (e. g. , the tail of species distribution). The current popular proposal to unlock DL training with limited datasets is Few-Shot Learning (FSL), which builds algorithms able to discriminate classes with very few samples, classically from 1 to 20 images per class. Finn et al. [50]  was a precursor of FSL and was based on a phase of meta-training, during which the model was trained on a different task at each iteration. To build a model able to discriminate five classes, at each training iteration, five classes were randomly selected from among the 64 possible training classes.
This phase enabled the model to "learn to learn" and to adapt itself to new tasks. Once the meta-training task was completed, the model could be adapted to a new task with very few images. More recently,  [51, 52]  proposed to improve meta-learning by tweaking the meta-training or training batch compositions. Apart from meta-training, matching network and metrics learning are other popular options  [53] [54] [55] [56]. Such methods aim to train a model to match a "Support Set" (i. e. , a small training dataset composed of 1-5 images per class) with a "Query image" (i. e. , the image to predict the label for). These approaches are two-fold: (1) they require a DL model able to manage the classification space with few images, and (2) they require the training of a robust metric to measure distances between the Query Image and the different Support Set clusters.
More details on FSL can be found in the more exhaustive  [57]  review. FSL is usually limited to 5-20 classes. However, recent papers are trying to overcome this limitation with approaches known as Many Classes Few-Shots  [58, 59]  by leveraging a possible hierarchy among classes. This could be an opportunity for ecological applications, as there is a known hierarchy between species (i. e. , taxonomy). Unfortunately, to date, most FSL algorithms are not able to discriminate more than 20 classes or with low accuracy  [60]. Moreover, the study of methods for both imbalanced datasets and scarce data is for now limited to traditional benchmarks (such as imageNet  [61]  or mnist (http://yann. lecun. com/ exdb/mnist/)(accessed on 1 June 2021). ) without ecological questions. However, FSL's application to underwater videos was first trialled in 2021  [62].
The SoftMax function transforms the activation vector (i. e. , the last feature vector of a Deep Network input) into a vector of n values, with n being the number of classes to discriminate. OpenMax adds a rejection function to SoftMax. This rejection function relies on the distance computed between previously learned data and the new input. The two main risks of OSR approaches are (1) depending on a training dataset to learn something that is not present in the training dataset and (2) potential overfit through the minimisation of potential classification space for learned classes. We also noted that most approaches are applied to image classification issues, and very few works cover object detection. To date, there is only one research article  [82]  trying to resolve the three issues of imbalanced datasets, scarce data and the open world at the same time for a classification problem.
Conclusions Deep learning applications for biodiversity monitoring have been increasingly explored since 2017  [9]  and are still in their early stages in marine realm applications. However, most studies rely on methods designed for and tested on generic benchmarks, which restrains the field of applications. To become an efficient tool for unconstrained wildlife census and conservation monitoring, collaborative research in computing science and ecology shall account for some of the universal rules of biodiversity. In this perspective, we highlighted methods from the state-of-the-art of artificial intelligence. Such methods have the potential to overcome the current limits of automatic video processing by focusing more thoroughly on the topics of imbalanced datasets, scarce data and open-world application.
As such, efficient deep networks working with few data, such as few-shot learners and one-shot learners, improvement of the robustness to data imbalances through a specifically built learning process, and the ability to treat information absent from the training datasets with Open Set Recognition paves the way for an interdisciplinary branch of science between computer sciences and ecology. Rather than merely transferring the DL methods originally developed to perform on benchmarks to ecological questions, ecologists and computer scientists should foster their collaborations at the interface of both disciplines. As such, DL algorithms would become question-driven instead of adapted, which could leverage the immense challenges that biodiversity is facing with climate change and the Anthropocene defaunation.
III. THE PADME EXPERIMENT RUN III The DAΦNE BTF at LNF offers interesting prospects for a X 17 search based on resonant production  [34]. In particular, the LNF accelerator complex can provide a positron beam and vary its energy in the required range around 280 MeV. Assuming a typical diamond target (with an electron density of 10 24 cm -3 ) of 100 μm such as the one actually in use in the PADME experiment  [41, 42] , several thousand X 17 can be produced per 10 10 positrons on target (POT). For this reason the PADME experiment has planned dedicated data taking to search for X 17 exploiting resonant production, called Run III. For this run, starting in the autumn of 2022, the detector has been optimized to measure X 17 visible decays. Using the ECal instead of the charged particle veto to detect the electron positron pairs will allow PADME to reach a much stronger rejection of the beam related background with respect to Run II conditions.
ACKNOWLEDGMENTS We acknowledge several discussions with members of the  PADME Collaboration , particularly  P. Valente  for fundamental inputs on the LNF beam setup and  P. Gianotti  for discussions on the details of the PADME experimental strategy. L. D. and E. N. have been supported in part by the  INFN "Iniziativa Specifica" Theoretical Astroparticle Physics (TAsP-LNF ). E. N. and M. R. are partially supported by the  Sapienza Grant "Ricerca del bosone   X17  nell'esperimento  PADME ai laboratori Nazionali di Frascati. "L. D. is supported by the  European Union 's  Horizon 2020 research and innovation program  under  Marie Skłodowska-Curie Grant  Agreement No. 101028626. M. M. acknowledges  Professor A. D'Angelo  for bringing him into contact with beyond Standard Model physics. Note added.
Acknowledgments We thank  J. -Y. Monchambert  ( UMR 8167 ) and  M. Mashkour  ( UMR 7209 ) for their supervision. We finally thank  V. Dorison  (etiopath) to have let us follow her on her etiopathy sessions and  C. Guintard  ( ONIRIS - Nantes ) for enlightening us on veterinary studies.
Purpose: Prevalence, demography, antiseizure medication (ASM) usage, healthcare resource utilization (HCRU), and mortality of Dravet syndrome (DS) in the UK were investigated using primary and secondary care data in this retrospective cohort study. Methods: Patients with confirmed DS were anonymously identified from the UK Clinical Practice Research Datalink (CPRD) GOLD database (01/01/1987-31/10/2018) using the DS Read Codes (F25G. 11 or F25G. 00). Probable DS was identified using the International Classification of Diseases-10/Read Code for epilepsy plus stiripentol or potassium bromide prescription. CPRD data were linked to the Hospital Episode Statistics database and Office for National Statistics to calculate HCRU and mortality. Results: The prevalence of confirmed (n = 32; 1. 1/100,000) and probable (n = 22; 0.
6/100,000) DS in 2017 was 1. 5/100,000. Most patients with DS (confirmed, n = 22/28; probable, n = 8/14) were aged <18 years in 2017. Mean (standard deviation) ASM usage was 5. 5 (2. 7) in confirmed DS and 7. 6 (3. 8)  in probable DS, over 3. 4 (3. 5) years and 10. 0 (6. 2) years of follow-up, respectively. HCRU (per patient-year) was similarly high in patients with confirmed and probable DS; mainly consisting of general practitioner consultations (mean, 4. 8-7. 9), outpatient visits (5. 6-8. 3), hospital admissions (0. 9-4), and emergency department visits (0. 3-2. 3). Fewer than five deaths were recorded in patients with confirmed and probable DS. Conclusion: Using linked national healthcare databases, our study showed that the UK prevalence of DS recorded in primary care was low, and most cases were in patients aged <18 years.
Introduction Dravet syndrome (DS) is a rare childhood-onset epilepsy often caused by SCN1A mutations, and characterized by multiple intractable seizure types  [1]. Patients with DS usually develop intellectual disability  [2] , comorbidities are common  [3] , and the premature mortality rate is high  [2]. Despite treatment with multiple antiseizure medications (ASMs), disease management is poor  [3] , and misdiagnosis for focal epilepsy results in incorrectly prescribed ASMs  [4]. Patients with DS have high healthcare resource utilization (HCRU). UK primary and secondary healthcare data are captured in electronic medical records (EMRs), with diagnoses recorded in primary care using Abbreviations: ASM, antiseizure medication; CPRD, Clinical Practice Research Datalink; DS, Dravet syndrome; ED, emergency department; EMR, electronic medical record; GP, general practitioner; HCRU, healthcare resource utilization; HES, Hospital Episode Statistics; ICD, International Classification of Diseases; ONS, Office for National Statistics; SMEI, severe myoclonic epilepsy in infancy.
Patient population Data were included for patients with records between 01/01/ 1987-31/10/2018, who had at least one EMR containing a Read Code for DS (CPRD-GOLD; F25G. 11) or SMEI (CPRD-GOLD; F25G. 00) indicating confirmed DS; or an EMR containing an International Classification of Diseases (ICD)-10 code (HES) or Read Code (CPRD) for epilepsy, and a formulary product code for stiripentol or potassium bromide within the same year or year after being coded for epilepsy with an ICD-10 code or Read Code, indicating probable DS. Prescriptions of stiripentol or potassium bromide were deemed the best indicators of DS in patients with epilepsy; stiripentol is specifically indicated for patients with DS  [9] , while potassium bromide (which was available prior to the introduction of stiripentol)  [10]  has historically been used to treat patients with DS  [4, 9].
Also, patients may have been diagnosed with refractory epilepsy before DS was widely recognized and routine genetic testing introduced. In the confirmed DS group, the median age at diagnosis of epilepsy (1 year) and DS (5. 5 years) differs. This likely reflects the delay in DS diagnosis after seizure onset in the affected child that may result from the evolving DS phenotype and diagnosis or coding delays  [14]. Average number of ASMs used (confirmed, 5. 5; probable, 7. 6) is higher than reported in UK patients with epilepsy  [15] , and reflects the drug-resistant nature of DS. As all probable DS cases were recorded before 2014, data for the confirmed DS cohort may be more reflective of current treatment patterns. ASM use reported in a given year (one on average, not more than two) is in line with National Institute for Health and Care Excellence guidelines for management of epilepsies  [16].
Limitations of this study include potential miscoding, misdiagnosis, and lack of sensitivity in the probable DS algorithm. While our algorithm may have missed some patients, its specificity for DS is evidenced by the lack of cases of probable DS identified following the introduction of Read Codes in 2013-14 (SMEI and DS). However, we would note that a greater prevalence of DS was reported when a similar, but slightly broader probable DS algorithm was used in a German population  [19]. Additionally, the potassium bromide product code used in the probable DS algorithm potentially identified patients with other severe epilepsies that manifest in the first year of life. Further limitations are that secondary care data (e. g. letters) are not fully captured, only a subset of patients in the CPRD database are eligible for HES linkage, lack of access to patient level data to confirm DS diagnosis, and there were insufficient patient-years of follow-up and reported deaths to accurately assess mortality.
Standard KS Staging and Determination of the Diagnostic Accuracy of FDG PET/CT The determination of KS disease sites were defined by KS extension to cutaneous and extracutaneous sites assessed by clinical examination, available standard imaging (CT and MRI), endoscopy, and/or pathology  [11] , and in all cases from follow-up data, thus defining the gold standard for staging ("standard staging"). Follow-up data included results from physical examination and standard imaging if performed. FDG PET/CT results were compared with "standard staging" at the time of the FDG PET/CT examination. FDG PET/CT staging (PET staging) was classified as true positive (TP), true negative (TN), false positive (FP), or false negative (FN). Per site analysis: the site was considered TP if at least one uptake was identified in the studied organ and confirmed by standard staging.
Our study provided important results in determining the place of FDG PET/CT in KS staging; however, it had some limitations. First, heterogeneous patients with all types of KS were included. In our study, FDG PET/CT staging and standard staging correctly reflected the various clinical presentations associated with each KS type. Half of the patients were previously treated. However, FDG PET/CT showed similar diagnostic performances in this group and the treatment-naïve patients' group. SUVmax were not statistically different (data not shown), which may be because most previously treated patients were referred for FDG PET/CT with suspicion of recurrence. The pathological documentation of all KS lesions in each patient was not always available and may limit the interpretation. However, the gold standard was derived from a multimodal evaluation in those cases, including clinical and biological features, imaging, and follow-up data.
This experimental model is tested in various conditions including regular waves of several periods and steepness, and irregular waves of various intensity, including extreme 50-year return period conditions. Highly nonlinear hydroelastic responses are observed in regular waves when higher order hydrodynamic loads excite the bending mode. In irregular sea-states, this behaviour was also triggered by strongly nonlinear effects resulting in a springing or ringing response of the system. This highlights the importance of nonlinear hydrodynamics in hydro-elastic analysis of large floating wind turbines. Eventually, the collected database could be relevant for a more advanced analysis with comparison to both linear and nonlinear hydro-elastic simulation tools.
Rotor thrust force An actuator is used to represent the aerodynamic thrust force. The RNA uses a single-rotor thruster, designed in the SoftWind project  (Arnal, 2020). It is installed on the tower top and is shown in Fig. 3. In the SoftWind project  (Arnal, 2020) , the applied aerodynamic thrust was calculated in real-time in a Software-In-the-Loop (SIL) framework. In this study, only a constant thrust is applied. The reason for this is the simplification of the aerodynamic load for an analysis focused on the hydro-elastic response. The constant thrust gives a positive tilt of the platform, and enables investigating its effect on the wave-induced response, but the aero-hydro-elastic couplings are not studied in these experiments.
However, the presented measurements can be a relevant validation database of coupled simulation tools including nonlinear hydrodynamics and structure dynamics. Wave impacts were also observed, particularly in the extreme conditions. An example is shown in Fig. 17 , with a large wave, inducing a significant and violent change in acceleration on the TP. This results in a large oscillation of the bending mode, which is damped in less than 200 s. The ringing responses observed before lasted for longer, sometimes for over 400 s.
Statistical models (STM)interpreting natural diversity These models describe observed patterns of plankton diversity using a myriad of statistical and machine learning methods. For the most common proxy of taxonomic biodiversity, species richness, various statistical and machine learning methods  (Melo-Merino et al. , 2020)  can be used to develop species distribution models (SDMs) based on occurrence observations e. g. from open databases (GBIF 1  , OBIS 2  ). Similarly, DNA/RNA sequencing data can be used to characterize diversity patterns of particular groups (e. g. viruses, bacteria, and diazotrophs). The recent availability and lower cost of these data have provided a global scale perspective on the diversity patterns of marine phytoplankton  (Righetti et al. , 2019) , zooplankton  (Brun et al.
This is despite the recognition of phytoplankton, zooplankton and microbial biomass and abundance as Essential Ocean Variables (EOV), Essential Biodiversity Variables (EBV)  (Miloslavich et al. , 2018)  and even Essential Climate Variables  (GCOS, 2021). Development and validation of all presented modelling approaches would benefit in particular from data on stoichiometric composition (C, N, P, Chl-a), traits (e. g. cell/body size and shape) and biomass measured across a full spectrum of environmental conditions (temperature, light, nutrients), with photosynthetic rates, nutrient uptake and respiration rates also critically needed. In addition, comparisons of modelling approaches with molecular data remain scarce, despite the recent availability of a considerable amount of data, which hold great potential as shown with individual-based comparisons  (Hellweger, 2020).
Acknowledgments This paper and the related Foresight Workshop MODIV have been supported by  EuroMarine. We thank the  Marine Biodiversity Observing Network (MBON) , the  DFG-Priority Programme DynaTrait  ( SPP 1704 ), the  Centre for Ocean Life , the organisers and participants of the International Workshops on Trait-based Approaches to  Ocean Life , and the  International Ocean Carbon Coordination Project  for their support and for fostering a decade of collaborations and exchange around the topic of plankton diversity.
Funding The  German Research Foundation (DFG)  supported  EA-T (AC 331/1-1 ) and OK ( KE 1970/2-1 ). BC was supported by  Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)  ( SMSEGL20SC02 ) and a  Leverhulme Trust Research Project Grant  ( RPG-2020-389 ). MG was supported by  National Science Foundation  ( OCE-1851866 ). CL received funding from the  EU Horizons 2020  project  SUMMER  ( 817806 ). AP received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 869383  (ECOTIP), and acknowledges support from the  United States National Science Foundation  grant  OCE-2140395  to the  Scientific Committee on Oceanic Research (SCOR, United States)  for  International Ocean Carbon Coordination Project. AR was partly supported by the  Federal Ministry of Education and Research BMBF   Germany  Project  PEKRIS II  ( 03F0828 ).
The  German Federal Agency for Nature Conservation (Bundesamt für Naturschutz, BfN)  provided financial support to MS with funds from the  Federal Ministry of the Environment, Nature Conservation and Nuclear Safety (BMU) , under grant agreement FKZ: 3521532201. SS was supported by a  JRPs-LEAD grant  from the  Japan Society for the Promotion of Science. SV was supported by  Trond Mohn Research Foundation  starting grant  TMS2018REK02. FP was supported by the  GEOMAR Helmholtz Centre for Ocean Research Kiel. Addional financial support to MODIV was provided by the  FILAMO  project, funded by  The Research Council of Norway.
2010). The passband of the TiO filter is 10 Å, centred at 705. 7 nm, while its temporal resolution is about 15 s with a pixel scale of 0. 034. Concerning the VIS, a combination of 5 Å interference filter and a Fabry-Pérot etalon is used to get a bandpass of 0. 07 Å in the H α line. The VIS field of view (FOV) is about 70 with a pixel scale of 0. 029. To obtain more spectral information, we scan the H α line at 11 positions with a 0. 2 Å step following this sequence: ±1. 0, ±0. 8, ±0. 6, ±0. 4, ±0. 2, 0. 0 Å. We obtained a full Stokes spectroscopic polarimetry using the Fe I 1565 nm doublet over a 85 round FOV with the aid of a dual Fabry-Pérot etalon by the NIRIS Spectropolarimeter  (Cao et al. 2012). Stokes I, Q, U, and V profiles were obtained every 72 s with a pixel scale of 0. 081. All TiO and H α data were speckle reconstructed using the Kiepenheuer-Institute Speckle Interferometry Package  (Wöger et al.