dataesr/scientific-paragraphs-categorization huggingface dataset:
The combination of observations with the MRS and the coronagraphs will be decisive in further constraining the atmospheres of known directly imaged planets as illustrated in  Danielski et al. (2018). 7. 6. Complementarity with future ground-based projects MIRI/MRS has the advantage of not being impacted by telluric lines in comparison to the data taken with ground-based instruments. Still, future ELT instruments are highly complementary to the MRS in providing higher spectral resolution and different wavelength coverage. In the near-IR, HARMONI allows a range of spectral resolution settings (3000, 7000, 18 000). Houllé et al. (2021)  simulated molecular mapping observations combined with a matched-filter approach, showing that planets with contrasts of up to 16 mag and separations of down to 75 mas can be detected at >5σ.
Acknowledgements. M. M. acknowledges the  Centre National d'Études Spatiales (CNES, Toulouse, France ) for supporting the Ph. D. fellowship. B. C. and B. B. acknowledge financial support from the  Programme National de Planétologie (PNP)  of  CNRS/INSU. This work was granted access to the HPC resources of MesoPSL financed by the  Région Île-de-France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (COBREX ; grant agreement no. 885593 ;  EPIC , grant agreement no. 819155 ). This publication makes use of data products from the  Wide-field Infrared Survey Explorer , which is a joint project of the  University of California, Los Angeles , and the  Jet Propulsion Laboratory/California Institute of Technology , funded by the  National Aeronautics and Space Administration.
Large language models (LLMs) such as ChatGPT have sparked extensive discourse within the medical education community, spurring both excitement and apprehension. Written from the perspective of medical students, this editorial offers insights gleaned through immersive interactions with ChatGPT, contextualized by ongoing research into the imminent role of LLMs in health care. Three distinct positive use cases for ChatGPT were identified: facilitating differential diagnosis brainstorming, providing interactive practice cases, and aiding in multiple-choice question review. These use cases can effectively help students learn foundational medical knowledge during the preclinical curriculum while reinforcing the learning of core Entrustable Professional Activities. Simultaneously, we highlight key limitations of LLMs in medical education, including their insufficient ability to teach the integration of contextual and external information, comprehend sensory and nonverbal cues, cultivate rapport and interpersonal interaction, and align with overarching medical education and patient care goals.
Background on Large Language Models Artificial intelligence has consistently proven itself to be a transformative force across various sectors, with the medical field being no exception. A recent advancement in this sphere is large language models (LLMs) such as OpenAI's ChatGPT and its more recent model, GPT-4  [1]. Fundamentally, LLMs leverage deep neural networks-complex structures with multiple layers of statistical correlation, or "hidden layers"-that facilitate nuanced, complex relations and advanced information abstraction  [2]. The breakthrough of ChatGPT represents the convergence of two significant advancements in computer science: scaled advancement of the processing power of LLMs and the implementation of real-time reinforcement learning with human feedback  [3] [4] [5]. As a result, computers can now handle vast volumes of training data and generate models with billions of parameters that exhibit advanced humanlike language performance.
Significant constraints accompany the use of LLMs. These include their sporadic propensity to concoct fictitious information, a phenomenon aptly named "hallucinating," as well as their unpredictable sensitivity to the structure of user input "prompting"  [6] [7] [8]. Additionally, both ChatGPT and GPT-4 were not trained on data sourced past 2021 and largely do not have access to information behind paywalls  [9, 10]. As the training was proprietary, it is challenging to model a priori bias and error within the model  [11, 12]. Deducing these vulnerabilities and understanding how they influence model output is important for the accurate use of LLMs. Since ChatGPT's release in November 2022, LLMs' potential role in medical education and clinical practice has sparked significant discussion. Educators have considered ChatGPT's capacity for studying assistance, medical training, and clinical decision-making  [6, 7, 13].
Understanding the Scope of LLMs' Performance on Medical Knowledge Tasks The capacity of LLMs to model the semantics of medical information encoded in the clinical sublanguage has shown potential for medical question-answering tasks  [17] [18] [19]. A vanguard of this technology is ChatGPT, which has demonstrated promise beyond specific medical question-answering tasks, responding to questions in domains such as knowledge retrieval, clinical decision support, and patient triage  [20]. As ChatGPT's training data is proprietary, it is difficult to examine the medical knowledge to which the model was exposed. Recent research using multiple-choice questions sourced from the United States Medical Licensing Exam (USMLE) as a proxy for medical knowledge found that ChatGPT could approximate the performance of a third-year medical student  [21, 22].
Interactive Practice Cases: Use Case 2 Simulating clinical cases fosters the application of pathophysiological frameworks learned in lectures and supports clinical skills such as history-taking and physical examination interpretation. With the implementation of explicit prompt engineering  [26] , students can enter into a dialogic, interactive case with ChatGPT playing the role of a simulated patient or medical professor (Figure  2 ). Unlike in static clinical cases from textbooks, ChatGPT's interactive nature allows students to clarify or expand information presented dynamically. This form of constructivist, active learning emphasizes the importance of interaction and hands-on engagement for deeper, more durable knowledge acquisition  [27]. Additionally, manipulating the case by adding or subtracting information supports a mode of inquiry similar to the script concordance test, a tool used for teaching and evaluating medical reasoning in ambiguous clinical scenarios  [28].
The following key considerations emerged from our work. First, it is crucial to validate ChatGPT's outputs with reputable resources, as it aids learning and can prompt critical thinking but does not replace established authorities. Second, much like the advice given to clinical preceptors  [31] , the framing of inquiries should favor open-ended generative questions over binary or definitive ones to foster productive discussion and avoid misleading responses. Third, understanding the scope and limitations of LLMs' training data sets is a key step in guarding against possible biases embedded within these models. Finally, incorporating structured training on artificial intelligence into the medical curriculum can empower students to further discern optimal use cases and understand potential pitfalls  [32].
The significance of patient demeanor and nonverbal communication can additionally not be underestimated. Translating symptoms into medical terminology is beyond simple translation; often patients describe symptoms in unique, unexpected ways, and learning to interpret this is part of comprehending and using clinical sublanguage. Moreover, a physician's intuitive sense of a patient appearing "sick" can guide a differential diagnosis before a single word is exchanged. ChatGPT lacks this first step in the physical exam ("inspection from the foot of the bed"  [34] ) and, thus, is hindered in its use of translated and transcribed medical terminology input by the user.
Future Integration of LLMs Into Health Care and the Importance of Understanding Strengths and Weaknesses The integration of LLMs into health care is fast becoming a reality, with both the availability of LLMs at students' fingertips and the rapid influx of research-driven deployments. Such integration is underscored by the impending inclusion of   [36]. Potential applications range from reducing administrative tasks, like generating patient discharge instructions, assisting with insurance filings, and obtaining prior authorizations for medical services  [37] , to improving care quality through extracting key past medical history from complex patient records and providing interactive cross-checks of standard operating procedures (Figure  6 ). Across the range of emerging applications, the most notable are the potential for LLMs to digest the huge volumes of unstructured data in electronic health records and the possibility for LLMs to assist with clinical documentation  [9, 38].
Acknowledgments Research reported in this publication was supported by the  National Heart, Lung, and Blood Institute of the National Institutes of Health  under award  T35HL007649  (CWS), the  National Institute of General Medical Sciences  of the  National Institutes of JMIR Med Educ 2023  | vol. 9 | e50945 | p. 9 https://mededu. jmir. org/ 2023/1/e50945  (page number not for citation purposes) Safranek et al JMIR MEDICAL EDUCATION XSL •  FO RenderX Health  under award  T32GM136651  (AESE), the  National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health  under award  T35DK104689  (AG), and the  Yale School of Medicine Fellowship for Medical Student Research  (AG). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Two widely available mHealth apps, MASK-air® (Mobile Airways Sentinel NetworK) for the management of rhinitis and asthma and CRUSE® (Chronic Urticaria Self Evaluation) for patients with chronic spontaneous urticaria, were updated to include Ukrainian versions that make the documented information available to treating physicians in their own language. The Ukrainian patients fill in the questionnaires and daily symptom-medication scores for asthma, rhinitis (MASK-air) or urticaria (CRUSE) in Ukrainian. Then, following the GDPR, patients grant their physician access to the app by scanning a QR code displayed on the physician's computer enabling the physician.
Providing person-centred care is therefore essential. Many different approaches have been proposed to help Ukrainian refugees. However, the crisis demands new solutions. Tools such as Google Translate can help but do not provide a structured medical history. Personal data protection and cross-border continuity of care could be ensured through the adoption of digital tools, such as the one proposed for the EU Digital COVID green Certificate, where people decide to share their personal information case by case. 25  A health-information system-using data from social media, mHealth apps (Box 1) and crowd-sourcing in a manner that respects data privacy-may be of interest. 26  There is an urgent need for an inexpensive and digitally-enabled, structured, person-centred system of care to help in shared decisionmaking between Ukrainian patients and European physicians for allergic diseases, asthma and/or urticaria.
| AIMS OF THE AC TI ON PL AN 'Language assistance is particularly important in health care, given the sensitivity of the issues involved and the high level of technical language proficiency required to communicate medical terminology'. 18  According to many reports, 'it is key to engage with refugees themselves to understand the barriers they face'. ARIA has therefore proposed a digitally-enabled, person-centred system of care using MASK-air to help shared decision making (SDM) between Healthcare Workers and Ukrainian refugees. MASK-air® is a candidate Best Practice of OECD on person-centred care for chronic diseases. It is particularly suited for SDM. 29  CRUSE®, based on MASK-air®, extends the reach of this project to skin disorders, patients with skin diseases and their treating physician, including dermatologists.
The next step will be to include a COPD module. The Ukrainian patient will fill-in (in Ukrainian) the questionnaires and daily symptom-medication scores for asthma, rhinitis (MASK-air®) and urticaria (CRUSE®). Following the GDPR, the patient will grant his/her physician access to the content of the app. This is done by scanning a QR code displayed on the physician's (or healthcare provider's) computer (MASK-air®) and then sending this information by email (CRUSE®) or as a PDF (MASK-air® and CRUSE®). This will enable the physician to read the app contents in his/her own language. It takes less than 1 min to show patient data to the physician in the physician's web browser or to send the results by email (Table  2 ). The MASK-air/CRUSE action plan will be implemented following the Change Management strategy of Kotter, already used in respiratory allergic diseases.
This percentage was lower for countries where Russian is better understood, but the difference was not significant (47. 2% vs. 67. 6%, p =. 115) (Table  3 ). When communicating with refugees coming alone, 46. 4% of respondents used electronic translating tools. In most cases, such tools were effective in decreasing communication difficulties (44/51-86. 3%). The number of healthcare professionals facing these difficulties was lower-but not negligible-when refugees came accompanied by a person speaking Ukrainian and the respondent's native language (N = 55; 48. 6%). 31 (21. 5%) respondents had not seen any refugees accompanied by a bilingual person. The percentage was higher for countries where Russian is more widely understood (53. 8% vs. 11. 4%; p <. 001). This study supports the MASK-air/CRUSE action plan and the UCRAID initiative.
Moreover, a scroll list of all medications for rhinitis and asthma is available. Additional questionnaires are included: EQ-ED-5L, allowing the assessment of utilities, and CARAT (Control of Allergic Rhinitis and Asthma Test). 38, 39  The maturity level of MASK-air® in rhinitis ranges from Technology Readiness Levels (TRLs) 8 to 9. MASK-air® is combined with the prediction of the allergen season and air quality (pollution) using COPERNICUS data (Finnish Meteorological Institute). 40  MASK-air includes daily electronic symptom-medication scores for rhinitis  41  and asthma,  42  allowing a daily follow up (Table  S1  in Appendix S3). MASK-air® has been awarded eight EU grants and projects. 33  It is a Best Practice of OECD-DG Santé on equity-enhancing, digitally-enabled, person-centred care.
MASK-air will soon be available for COPD. Transferring the data of the app from the patient's phone to the physician's computer usually takes less than a minute (Figure  2 ). The app follows the GDPR and has been launched in all MASK-air countries. The transferred information includes the self-reported patient's data, the VAS global allergy symptoms and e-DASTHMA. A video demonstrating the transfer will be provided. The MASK-air app has been translated into Ukrainian and is currently ready to be deployed (Figure  3 ). Reported Outcome Measures) for CSU. 43  Similarly to MASK-air®, it includes a visual analogue scale (VAS) regarding overall urticaria symptoms as well as the EQ-5D-5L. CRUSE® is now available in 17 countries (>4000 users, Figure  4 )-adapted to the local languages and medications-and also has worldwide English and Ukrainian versions.
| Develop a vision and strategy The vision of the project is to enable Ukrainian citizens and refugees to fill-in-in Ukrainian-the MASK-air® app if they suffer from asthma/rhinitis or the CRUSE® app if they suffer from urticaria. The physicians seeing the Ukrainian patients will have all the content of the app in their own language to facilitate shared decision making. The strategy for realising the changes is based on digitallyenabled, person-centred care using IT solutions such as the MASK-air® or CRUSE® apps.
| Generate short-term wins: pilot study We propose to create short-term (e. g. 6 months) and medium-term (e. g. 12 months) targets. To demonstrate the importance and barriers of the project, a pilot study has been initiated in Puglia (Maria Teresa Ventura) and the first Ukrainian patient was enrolled on 20 July 2022. The project is feasible since all MASK-air® tools have been tested in 19 languages and it was found that questions can be understood in all of the languages  41, 48  and by patients of up to 75 years of age. 49, 50.
| E THI C S 1. The download and usage of the apps are free of charge and there are no advertisements. They fall under the French jurisdiction. They follow the General Data Protection Regulation (GDPR) which regulates the processing of personal data in the European Union (EU). 51  Geolocation also follows the GDPR. 52  2. MASK-air® was CE1-registered, and this is the app that will be used. 3. However, in the next iteration of MASK-air®, considering the intended use and other information regarding the Software (SW) operation, as defined as part of the section 'purpose', the mentioned SW is 'determined' and will be classified under the MDR 2017/745  53  and MDCG 2019-11 guidances. 54  The SW is consid-.
| E XPEC TED B ENEFITS 1. Size of the target population: It is estimated that over 1 million Ukrainian refugees suffer from symptoms of asthma, rhinitis and/or urticaria and that over 100,000 have a severe disease. 2. It is vital to engage with refugees to understand the barriers they face. MASK-air® for respiratory symptoms and CRUSE® for urticaria are prototypes of person-centred care for chronic diseases. 3. Ukrainian refugees with asthma, rhinitis and urticaria have easyto-use mHealth apps in their own language to improve symptom control and treatment. 12. The design of UCRAID can be used for patients travelling in different countries to help shared decision making between the patient and the physician speaking different languages.
F I G U R E 2 9 BOUSQUET 29 b 31 respondents did not see Ukrainian refugees accompanied by a person speaking both Ukrainian and the respondent's native language. TA B L E 3 (Continued) Transfer of patient's data to healthcare professional's computer. 13989995, 0, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/all. 15855 by Universität Bern, Wiley Online Library on [29/08/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | et al.
The early propagation of both CME1 and CME2 was detected in the field of view (FOV) of the C2 and C3 instruments A136, page 2 of 19 Fig. 2. Position of STEREO-A, STEREO-B, and Earth on September 9, 2014, at 00:00. The grid in black corresponds to the Stonyhurst coordinate systems. This polar plot is generated using the Solar-MACH tool (https://serpentine-h2020. eu/tools/;  Gieseler et al. 2022). of the Large Angle and Spectrometric COronagraph (LASCO,  Brueckner et al. 1995)  on board the Solar and Heliospheric Observatory (SOHO), and the COR-2 instrument on board the Sun-Earth Connection Coronal and Heliospheric Investigation (SECCHI) package of the twin-spacecraft Solar Terrestrial Relations Observatory (STEREO,  Kaiser et al. 2008). Only STEREO-B recorded the observations, while a data gap was found in STEREO-A during this period.
In situ signatures at Earth The in situ signatures of the CMEs are plotted in Fig. 4. The shock (S1) driven by CME1 is observed on September 11 at 22:50 UT based on the IPShock catalogue 5   (Kilpua et al. 2015)  and is characterised by an enhancement in speed and number density. CME1 is directed north of the ecliptic plane as seen from the coronagraph images from the LASCO instruments 6. The Space Weather Database Of Notifications, Knowledge, Information (DONKI) 7  catalogue has also recorded a northeastward launch direction of the CME. These observations suggest that the WIND spacecraft would have encountered the southwestern flank of CME1. The long sheath region (characterised by density enhancement and fluctuating magnetic field signatures in the red-shaded region in Fig. 4 ) after the shock of CME1 can also be inferred as the signature of the CME1 flank.
Acknowledgements. We thank the anonymous referee for their comments and suggestions that led to improvements in the manuscript. This project (EUH-FORIA 2. 0) has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  870405. SP acknowledges support from the projects  C14/19/089  ( C1  project  Internal Funds KU Leuven ), G. 0D07. 19N ( FWO-Vlaanderen ),  SIDC Data Exploitation  ( ESA Prodex -12 ), and  Belspo  project  B2/191/P1/SWiM. The simulations were carried out at the  VSC -Flemish Supercomputer Centre , funded by the  Hercules Foundation  and the  Flemish Government -Department EWI. We are grateful to  Dr. Nariaki Nitta  and  Dr. Tibor Torok  for the valuable discussions that improved our understanding of the eruptions.
We thank  Dr Jasmina Magdalenić  for the suggestions that improved the 3D reconstruction of the halo CMEs. We also appreciate the availability of open-source data and catalogues used in this work: CDAW LASCO catalogue: https://cdaw. gsfc. nasa. gov/ CME_list/, IPShock catalogue: http://ipshocks. fi/, Wind ICME catalogue: https://wind. nasa. gov/ICME_catalog/ICME_catalog_viewer. php, Richardson and Cane ICME catalogue: https://izw1. caltech. edu/ACE/ASC/DATA/level3/icmetable2. htm, AIA images: SDO database http://jsoc. stanford. edu/AIA/AIA_gallery. html.
EXPERIMENTS AND RESULTS In this section, we first present our experiment settings, and then the corresponding results along with their analysis. We use Python as a programming language and PyTorch to construct our model. Some basic code was adopted from the studies by Venkatesha et al. [11]. It is important to highlight that our objective is to primarily use HFedSNN for a comparison study with the FedSNN model, as detailed in  [11]. We do not aim to achieve optimal accuracy with this model in this paper, but rather to understand its performance in relation to FedSNN. We choose the same model and dataset to compare with the baseline  [11]. Simulation Parameters: In alignment with the methodology of  [11] , in all simulations, we use BNTT as a surrogate gradientbased backpropagation approach and we encode the pixel values into spike trains of length 𝑇 using rate coding.
Leakage rate. The leakage factor denoted as 𝜆 ∈ [0, 1] corresponds to the leakage of the membrane potential 𝑢. In particular, it represents the rate at which the neuron decays over time when it is not firing or receiving any input. Thus, the leaky factor can also determine how long a neuron can remember the past data. As shown in Figure  9 , the leakage rate can significantly affect SNNs learning process and the performance of SNNs. For example, a large leakage rate implies that the membrane potential of the neurons decays quickly (Equation  2 ) and, in turn, reduces spike production unless  We also tested our model against FedSNN using both non-IID and IID data settings on the MNIST dataset, which is an open and well-known dataset. The simulation results, shown in Figure  10 , indicate the superior performance of our model over FedSNN under both scenarios.