dataesr/scientific-paragraphs-categorization huggingface dataset:
Results. Results for the symptom detection task are shown in Table  2  in macro multi-class precision, recall, and F1 score. We can observe that all models Overall, SciBERT uncased is the best-performing model (in bold) with a macro F1-score of 0. 86, outperforming the other approaches for each of the categories. In Table  3  we report the performances for each entity with the best-performing model. The Sign or Symptom detection task obtains a 0. 82 F1 score. In the work of  (Ngai and Rudzicz, 2022) , the authors also detect symptoms obtaining an F1 score of 0. 61. However, these results can not be directly compared since the datasets on which both models were fine-tuned are different: we train on clinical cases, while they use dialogues between doctors and patients. Moreover, given that the dataset they use is not released, we can not evaluate our approach to their data.
The results of the symptoms alignment module experiments are summarised in Table  4. As baseline models, we propose to use the same methods but without the context of the symptom, similarly to  (Manzini et al. , 2022)  DASH. In Table  4  we show only the bestperforming baseline PubMedBERT no context obtaining similar results to DASH (0. 41 and 0. 37, respectively). Adding contextual representation to the embeddings results in a significant improvement (up to 0. 53 in accuracy) supporting the hypothesis that context plays an important role when translating layperson terms to formal medical terms. Error Analysis. HPO has limitations with respect to the number of symptoms associated with each diagnosis. For some diagnoses, we have multiple symptoms, while for others we can have only one or none.
CONCLUSION In this paper, we present a full pipeline to generate natural language explanatory arguments for correct and incorrect diagnoses in clinical cases. More precisely, based on a novel annotated linguistic resource, our pipeline first automatically identifies in a clinical case description the relevant symptoms and matches them to the HPO medical knowledge base terms to associate symptoms to the correct and incorrect diagnoses proposed as potential answers to the test, and second, automatically generates a natural language explanatory argument which highlights why a certain answer is the correct diagnoses and why the others are not. Extensive experiments on a dataset of 314 clinical cases in English on various diseases show good results (0. 86 F1-Score on symptom detection and 0. 53 Accuracy on relevant symptom alignment for Top 5 matches), outperforming competitive baselines and SOTA approaches.
Results for entity recognition in macro multi-class precision, recall, and F1-score. some cases, it outperforms them. This may be due to the fact that the clinical cases from our dataset are written for medical exams at the med school. They contain some technical specialized words, but overall the symptoms are described in layperson terms. It is worth noticing that the majority of our labels do not pertain to medical terminology (e. g. Age and Population Group, Location and Temporal Concept). Sign or Symptom and Finding are the only labels that require specialized vocabulary.
ACKNOWLEDGEMENTS This work has been supported by the  French government , through the  3IA Côte d'Azur Investments in the Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-19-P3IA-0002. This work was supported by the  CHIST-ERA  grant of the  Call XAI 2019 of the ANR  with the grant number  Project - ANR-21-CHR4-0002.
I. INTRODUCTION The increasing need to process large amounts of data has been a major driver for developing novel, energy-efficient This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101070238. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. computing architectures  [1]. Among the multitude of approaches to tackle energy efficiency requirements, braininspired neuromorphic architectures are one of the most viable solutions thanks to their inner absence of I/O bottleneck between memory and processing units and their more natural mapping to machine learning (ML) algorithms  [2].
II. PROPOSED APPROACH IN NEUROPULS In NEUROPULS, we will develop three different platforms at technological, hardware computing, and simulation levels (see Fig. 1 ). The technological platform is based on the openaccess silicon photonics platform from CEA-LETI with the addition of phase-change materials (PCMs) and III-V materials. The building blocks based on these technologies, such as plastic non-volatile photonic synapses  [5]  and non-linear neurons based on spiking lasers  [6] , will enable various kinds of neuromorphic photonic architectures that NEUROPULS will investigate. Security layers will be built upon Physical Unclonable Functions (PUFs), security primitives that avoid digital keys stored in memory  [7]. These primitives will be embedded in the photonic and electronic chips. A software/hardware interface will leverage the security layers at a silicon level to set up multiple security features for the accelerator, such as secure authentication, encryption of neural network (NN) data, and signing of the output data.
well its security features. In NEUROPULS, we target highly demanding performance from our accelerator, which will allow us to improve by, e. g. , two orders of magnitude the energy/MAC metric compared to concurring GPU technology for the selected edge-computing use cases. Besides, security features based on PUFs will be investigated for the stand-alone accelerator and the targeted use cases using both hardware and software approaches, from the security primitives level to the software-driven security protocols implemented.
It will be enhanced with recent extensions supporting RISC-V-based systems modeling with a rich set of accelerators and flexible interfaces. The platform's flexibility will ease the exploration of trade-offs between security levels and corresponding performance, power consumption, and reliability penalties. NEUROPULS will also investigate the reliability and power estimation of photonic hardware components. The NEU-ROPULS simulation platform aims to provide a flexible infrastructure that enables the comparison of various photonic accelerator parameters to determine neuromorphic hardware's power reduction and performance improvements. In addition, the platform will focus on RISC-V CPU cores and customized neuromorphic accelerators, along with their corresponding software stack, to evaluate the reliability of the complete heterogeneous system and identify potential reliability issues that different NN accelerators may face.
Novel security layers based on the developed CMOS-compatible photonic platform will be developed by tightly integrating security primitives, i. e. , PUFs, and security software interfaces. Such integration will enable various security features, which we will apply to the accelerator as stand-alone and for specific use cases. The modular approach we propose in NEUROPULS will inform us about the technology scaling potential without relying solely on hardware prototypes but by accurate system modeling using gem5-based simulators. The project's outcomes will foster an augmented interest in this technology and spur novel research directions and investments at a European level in photonics, which has been indicated as a key enabling technology for Industry 4. 0 and RISC-V processors technology.
This suggests Gallopamil may target similar pathways to Triamcinolone during early COVID-19 infection. Our in silico approach is based on publicly available data from online databases such as STITCH  38  and ChEMBL  39  , and studies published in response to the pandemic  19, 20, 26, 27. By combining this information, we have predicted two drugs that interact with key pathways and proteins involved in early COVID-19 infection, suggesting this is an effective approach to take in this pandemic. It is likely that further diseases of zoonotic origin will emerge in the future  40  , and we must be prepared to respond rapidly to future pandemic threats. This kind of in silico approach allows for a quick and cost-effective approach to repurposing drugs, which could be deployed in such a situation.
Obtaining list of structurally similar drugs A list of COVID-19 clinical trial drugs (C19-CTDs) was obtained from DrugBank  41  , an online bioinformatics and chemoinformatics database that contains data on drugs and their targets. The ChemMBL API for chemical structural similarity accepts input in the simplified molecular-input line-entry system (SMILES), an ascii string format for representing chemical structures  42. For each C19-CTD, the structure in SMILES format was extracted from the DrugBank XML file. The ChEMBL API library (written in python) was used to obtain a list of structurally similar drugs (SSDs). The ChEMBL structural similarity tool uses the Tanimoto coefficient (Tc) to predict structural similarity.
Damage may be induced by different processes which can lead to: brittle fracture below the Ductile to Brittle Transition temperature (DBTT) or ductile failure for which softening (recovery / recrystallization) process plays a major role. To improve our understanding in the damage mechanism, a numerical tool has been developed (TREX code  [34] ) to assess W crack formation considering experimental heat flux, mechanical properties (elastic-viscoplastic) and softening of the tungsten material. Simulations have been performed assuming quasi steadystate (from 45 MW/m 2 to 70 MW/m 2 parallel heat flux as measured in WEST during phase I  [12] ) and disruption loading (impact heat flux factor HFF of 30 MW⋅m -2. s -1/2 , assuming q n = 600 MW⋅m -2 peak heat flux)  [35]. The disruption thermal cycle is composed of 3 ms heating including a ramp up for 1 ms and a ramp down for 2 ms which is representative of thermal quench duration in ASDEX upgrade  [33] , since we don't have yet accurate measurement of the thermal quench duration in WEST.
The stress and strain are evaluated by the TREX code based on experimental boundary conditions during steady state plasma operation  [34]. The heat load distribution on the block surfaces is determined with the IR measurement and 3D FEM modelling of the heat transfer in the component (ANSYS code). Parallel heat flux of about 90 MW. m -2 was required to reach 2600 • C on the LE as measured with the VHR IR camera (#56535)  [37]. Based on the thermal inputs derived from IR temperature measurements (#56535, I p = 500kA, X-point height dX = 110 mm, P LH = 4. 6 MW), the thermomechanical simulation predicts between 1 and 5 heating/cooling cycles before crack initiation at the maximal plastic strain position (ductile failure). About 9 pulses with similar heat load were performed before W-cracking were detected with the IR system  [37].
Sustained W-melting (C5 experimental campaign) No impact on the main plasma was observed in JET despite a strong increase of the local W source consistent with evaporation, while more detrimental effects were observed in AUG (W ejection followed by disruption  [32] ). To complete previous melting studies, sustained melting of W on actively cooled ITER-grade PFU have been executed in WEST (C5 experimental campaign, Dec. 2019) using the groove geometry on the upstream MB as presented in Fig. 9. Controlled and sustained W-melting has been achieved during three consecutives pulses for about 5 s duration each  [36]. The experiment has been modeled in full detail with the MEMENTO code to simulate the melt dynamics taking into account the cooling pipe system  [38]. The observed melt displacement pattern is due to the melt flow in the J × B direction (inboard if the melting occurs on the outer strike line with forward magnetic field), where J is the replacement current generated by the thermionic emission.
Acknowledgements: "This work has been carried out within the framework of the  EUROfusion Consortium , funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No  101052200 -EUROfusion ). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the  European Commission  can be held responsible for them. ".
Knowledge of species' functional traits is essential for understanding biodiversity patterns, predicting the impacts of global environmental changes, and assessing the efficiency of conservation measures. Bats are major components of mammalian diversity and occupy a variety of ecological niches and geographic distributions. However, an extensive compilation of their functional traits and ecological attributes is still missing. Here we present EuroBatrait 1. 0, the most comprehensive and up-to-date trait dataset covering 47 European bat species. The dataset includes data on 118 traits including genetic composition, physiology, morphology, acoustic signature, climatic associations, foraging habitat, roost type, diet, spatial behaviour, life history, pathogens, phenology, and distribution. We compiled the bat trait data obtained from three main sources: (i) a systematic literature and dataset search, (ii) unpublished data from European bat experts, and (iii) observations from largescale monitoring programs.
By understanding how traits covary and are related to environmental variables we can infer general ecological principles that overcome taxonomic gaps. The use of a species-level trait dataset is particularly relevant when working at the community level and in the context of rapid environmental changes. With 1,439 species distributed across the globe  13  , bats (order Chiroptera) account for ca. a fifth of global mammalian diversity. While bats are the second richest taxonomic order of mammals, they have been so far understudied compared to other groups  14. In the absence of in-depth knowledge of how species respond to environmental gradients, we can make inferences on understudied species using traits of the most closely related taxa. Trait-based approaches are becoming more common in bat research  (Fig.
Data DEScriPtor oPEN their functional traits has been scattered across the scientific and grey literature and unpublished data held by researchers and special interest groups. There are several ongoing national  95, 96  and regional  97  bat monitoring programs across Europe that are now old enough to provide highly reliable data on population trends and distributions. These programs also collect systematic and standardised data on morphology, echolocation, hibernation patterns and roost selection that are extremely useful to bat functional ecology. Here, we present EuroBaTrait 1. 0, a comprehensive open-access dataset of 118 traits for 47 European bat species built by compiling data from (i) a systematic literature and dataset search, (ii) unpublished data from pan-European bat experts and (iii) large-scale bat monitoring programs in France.
Methods taxon and geographic coverage. Our dataset includes the complete bat fauna of Europe based on the Handbook of the Mammals of Europe  98. This represents 47 bat species from 12 genera (Table  1 ). The taxonomic nomenclature follows the Handbook of the Mammals of Europe. The geographic coverage is mainland Europe and European islands, including the British Isles, Mediterranean islands and Macaronesia (Supplementary Material 2). The dataset is based mainly on recent publications, following recent taxonomic revisions (i. e. 2021)  13  trait categories. Based on the literature and expert-knowledge, we selected a comprehensive set of relevant traits, including both commonly used and bat-specific traits. These traits reflect a variety of ecological strategies, niches, and functional roles that are routinely collected for bats and other taxa to enable joint analyses with other datasets.
Dataset entry. All traits are given at the species and country/European level in the final dataset, but we also provide individual-level data (when available). Each trait value (i. e. dataset entry) is accompanied (whenever possible and applicable) by (i) a location (x,y coordinates and/or country), (ii) contextual characteristics, (iii) source, and (iv) estimates of precision. For aggregated values such as means and medians, we also provide the number of replicates and a measure of dispersion. Data sources and acquisition. We followed three strategies to compile the EuroBaTrait 1. 0 dataset. First, we conducted a systematic literature and dataset search and retrieved trait data from published books, taxonomic monographs, scientific articles, online resources, and existing datasets. Second, we put out a call to request unpublished data from European bat experts via the network of COST Action Network CA18107 "ClimBats" (https://climbats.
eu/). Third, we used data from two large-scale French standardised bat monitoring programs ("Vigie-Chiro" and "CACCHI" programs; https://www. vigienature. fr/fr/chauves-souris & https://croemer3. wixsite. com/teamchiro/cacchi) to retrieve key traits and to derive traits that could not be collected following the first two strategies. The first version of the trait dataset was finalised after the three strategies had been completed for all taxa. Literature and dataset search. We conducted the literature and dataset search using Web of Science and Google Scholar. We implemented a multi-step approach to target our search. In step 1, we based our search at the trait category level using the search string "(Bat* OR Chiroptera)" in addition to the name of a given trait category (see section 2. 2 Trait categories) (e.
g. "(Bat* OR Chiroptera)" and "acoustic*"). In step 2, we looked for each specific trait name (e. g. "(Bat* OR Chiroptera)" and "call duration"). In step 3, we focused our search on missing data. For this we used as a search string the name of the missing trait and the Latin name of the target bat species (e. g. "Plecotus auritus" and "call duration"). Altogether, we included trait data from six published books, nine taxonomic monographs, 378 scientific articles (published in indexed journals that were checked using clarivate https://mjl. clarivate. com/), 84 articles from the grey literature, 19 unpublished data sources, and three datasets (AnAge  99  , date of access: 02/02/2021, IUCN  100  , date of access: 20/01/2021; DBatVir 101 , date of access: 18/01/2021). ClimBats COST Action network.
Morphological measures taken from bats captured for research and monitoring are rarely published, so we asked European bat experts to provide data for traits that are usually not found in the literature. We contacted experts through the ClimBats network (representing 28 countries and ca. 100 experts), organised meetings to explain the project goals, and asked for raw or summarised unpublished trait data (e. g. morphological measurements). Data were also provided by the UK Bat Conservation Trust National Bat Monitoring Programme (https://www. bats. org. uk/our-work/national-bat-monitoring-programme). In total, we retrieved 279 trait values (i. e. dataset entry) for two morphological traits (mean forearm length and body mass) measured on 37 species in 12 countries (Bulgaria, Germany, Ireland, Italy, Luxembourg, Moldova, Norway, Poland, Portugal, Slovakia, Spain and the United Kingdom).
Large-scale bat monitoring programs. We used "CACCHI" (capture data) and the French national-scale citizen-science bat monitoring program "Vigie-Chiro" (acoustic data) which are coordinated by the French National Museum of Natural History (MNHN) to retrieve data on key traits and identify missing ones. CACCHI is a French bat monitoring program that aims to achieve national consistency regarding technical and ethical aspects of capture practices and data collection. French bat workers contribute to this program by providing data collected in a local context, thus giving the opportunity to build a dataset on a large spatio-temporal scale. We retrieved four traits from CACCHI, including mean forearm length and body mass (>60,000 and >6,000 dataset entries, respectively). Vigie-Chiro is one of the largest citizen-science bat monitoring programs in Europe with a standardized stationary point survey (among three) corresponding to a total of 16,349 sites acoustically monitored during 2015-2021 by >500 participants (see an overall description of Vigie-Chiro in Supplementary Material 3).
We retrieved 33 species-level traits from the data collected by Vigie-Chiro, including acoustic signature, climatic associations, foraging habitat, and phenology (see full details in Supplementary Material 3). In brief, traits related to acoustic signature (i. e. buzz duration, buzz peak frequency, buzz rate, call duration, call maximum/minimum frequency, call frequency at half call duration, call peak frequency, call slope and interpulse interval) were directly extracted from the reference library of calls using TADARIDA software  102. For traits related to climatic associations (i. e. responses to nightly temperature, precipitation, and wind speed) and foraging habitats (i. e. responses to deciduous forest, coniferous forest, dense urban area, freshwater, cropland and grassland at three spatial scales (50 m, 500 m and 5000 m radius buffer scale)), we used data from the stationary point protocol.
We conducted a series of univariate generalized linear mixed models ('glmmTMB' R package  103  ) with species-specific bat activity as response variables and foraging habitat and weather conditions as explanatory ones. The latter were standardized (mean = 0, SD = 1) and we extracted the slope of the relationship to inform the trait value. Finally, we derived the traits related to phenology (Kurtosis index and Skewness index of the seasonal activity pattern) with the 'moments' R package  104  , which required plotting bat activity obtained from the stationary point protocol as a function of Julian day.
Data records The EuroBaTrait 1. 0 dataset is available at figshare  105  and through the R Shiny App (https://jasja. shinyapps. io/ ClimBats/) under the terms of a Creative Commons Attribution 4. 0 International waiver. The CC-BY-4. 0 waiver facilitates the discovery, re-use, and citation of the dataset. As this is a dynamic and living dataset, future updates of the dataset will be made directly on figshare and associated R Shiny App. We will follow the same technical validation as described hereafter. Dataset structure. We followed the general recommendations proposed by Schneider, et al. 106  to organise the dataset. The dataset consists of 16 tables linked by unique identifiers. The "Taxon" table includes the full scientific name and family of all taxa and a uniform resource identifier linked to GBIF (https://www.
gbif. org/, Table  1 ). The "Trait description" table represents the metadata, in which we describe the different traits (name, category, unit, data type) and define each trait following (when available) current glossaries (e. g. we followed Blatteis, et al. 107  for physiology). The "Trait reference" table lists the full references used to build the dataset. The dataset also includes one table per trait category -namely, genetic composition, physiology, morphology, acoustic signature, climatic associations, foraging habitat, roost type, diet, spatial behaviour, life history, pathogens, phenology, and distribution -with the core observation values (SD, CV and N referring to standard deviation, coefficient of variation and sample size, respectively) and associated information. We decided not to impute missing values to highlight research gaps and data needs.