dataesr/scientific-paragraphs-categorization huggingface dataset:
Our dataset contains about 1300 images, which might not be sufficient for the YOLOv3, composed of about 61. 9 million parameters to learn. To improve performance, we used transfer learning and data augmentation. Transfer learning is a method where we reuse a pretrained model as the starting point for a model on a new related task. Instead of learning the model from scratch, we initialize it using the pretrained model. In our case, we used a pretrained YOLOv3 trained on the Common Object in Context (COCO)  [37]  dataset, which is a large-scale object detection, segmentation, key-point detection, and captioning dataset containing images of everyday objects (e. g. , persons, cars, etc. ). The COCO dataset is often used to benchmark models and compare their performance. Using pretrained weights on a large dataset could mitigate the problem of insufficient training data and help prevent overfitting by transferring the knowledge from the source to the target model.
1 and 0. 5 mostly lies in the ground truth of small size (<100 px) and large size (≥800 px), meaning the model struggles to make accurate predictions with these box sizes. It is indeed more challenging to detect small boxes as even a slight shift in the prediction can change whether the prediction will be accounted as TP or FP. On the other hand, the issue with missing large-size ground truth might be due to the fact that the model has not seen a lot of large TCs during training, as shown in Figure  7b : the distribution of the ground truth surface area in train set shows that the frequency of large boxes is indeed low, the dataset being mostly composed of the ground truth of smaller size.
Conclusions In this paper, we attempted to detect TCs in the North Atlantic Basin. We proposed a framework to process a selection of 50 channels from IASI radiance data into multi-channel images, as well as a labeling procedure using the HURDAT2 database. YOLOv3 object detection model has been trained on this dataset, firstly using three channels, then using an autoencoder to exploit all 50 channels. IASI data are not combined with other instruments' data to form the dataset, and no wind data are used. We achieved promising results with Average Precision at threshold 0. 1 (AP@. 1) of 78. 31%, although AP@. 5 gave lower results (31. 05%), hinting at a lack of precision of the model in its predictions, more particularly in the size of the predicted boxes. The model could further be optimized by fine-tuning the hyperparameters.
First, Pang et al. trained their model with satellite images, but the region of interest is quite different: we focused on a fixed region (North Atlantic Basin), while Pang et al. used zoomed images centered on TCs of the Southwest Pacific area. Shi et al. used ERA5 data with variables such as mean sea level pressure or vorticity that characterize cyclones and are not present in our training dataset, as we focused on using mostly raw radiances from IASI.
Acknowledgments: This work was supported by the  CNES. It is based on observations with IASI embarked on Metop. The authors acknowledge the Aeris data infrastructure (https://www. aerisdata. fr,accessed on  22 March 2021 ) for providing access to the IASI Level 1C data and Level 2 water vapor data used in this study. This study benefited from the ESPRI computing and data center (https://mesocentre. ipsl. fr,accessed on  15 March 2021 ) which is supported by  CNRS ,  Sorbonne Université ,  Ecole Polytechnique , and CNES as well as through national and international grants. This work was granted access to the  HPC/AI resources of IDRIS  under the allocations  2021-AD011012607  and  2022-AD011012607R1  made by  GENCI. The authors thank  Christopher Landsea  ( NOAA ) for his updates on the HURDAT2 database.
Specifically in Indonesia, the combination of new variants within a highly populated nation put extreme stress on the healthcare system, necessitated a nation-wide, multi-sectoral mobilisation of healthcare and laboratory capacities, 9 and a tenacious vaccination campaign. However, unanswered questions remain, for example the relative influence of contributing factors to the recorded COVID-19 incidence. Importantly, no viral sequencing data was obtained from the infected participants of this study, thus limiting any extrapolated correlation between circulating viral variants and hospitalisation incidence. As is true for many topics, hospitals and communities in different contexts have unique challenges and methods for overcoming those challenges fully or partially. These regional (East vs West Java) and national variations (across the Indonesian provinces) have not been discussed adequately, and thus limit our understanding and the extent to which these results can inform national and international health policies, though the national efforts for a standardised approach to the pandemic should be recognised.
During each session, the focus group carried out an activity related to the LCA of a tiny house. Each working session was audio recorded and then coded. The researcher who conducted the experiment was present at every stage of the LCA process. All the processes of LCA were done in a collective way. Thus, the members of the focus group had the opportunity to participate in the definition of the tasks of the LCA (definition, functional unit), the data collection, the choice of the calculation method and the impact indicators, and to discuss the results in order to act in the ecovillage. Three types of data were collected: • Qualitative data on values supported by the inhabitants (individual interview conducted with the method "motivational interviewing" to understand the deep motivations of inhabitants in their life); • Qualitative data on engineering competencies for sustainability of inhabitants.
Yet, if diarization algorithms existed and worked on single-channel ADOS recording, they could be readily used a posteriori on any previous recording to alleviate time spent on participants' vocalizations extraction. In the scope of digital psychiatry, diarization algorithms allowing a large collection of vocal data in preschoolers with ASD could help train algorithms identifying early ASD diagnostic/prognostic patterns  22, 23. Here, we explored how spontaneous prosody production relates to developmental delay and ASD symptom severity phenotypes in preschoolers with ASD. With the aim of reducing manual pre-processing time and in view of the lack of existing methods, we present a novel diarization pipeline to automatically extract children vocalizations from single channel ADOS recordings.
For each participant, the audio track that had undergone voice enhancement and detection was chunked into 250 ms segments and F0, F1 and F2 were computed within each of those chunks. We rst built a classi er using a training sample consisting of 18 minutes of voices previously classi ed manually (9 minutes of three TD children voices and 9 minutes of three male adult examiners). MG extracted children's voices from three ADOS recordings of three different typically developing (TD) children available in our cohort (1 male aged 2. 5 y. o. and 2 females aged 3. 1 and 3. 0) and adult voices from three other ADOS performed by three different adult male examiners. Segments with adult and child voice overlapping were discarded. Quadratic Fisher Discriminant (QFD) was applied on the training dataset to classify each 250 ms chunk as belonging to either adult or child speaker.
In the 18 minutes training sample, 96. 1% of 250 ms chunks classi ed as belonging to a child were correct. Then, we built a validation sample consisting of 12 minutes of voices (6 minutes of children voices from three different TD children and 6 minutes of three different male adult examiners, extracted manually by MG). Children were not the same as in the training sample (one male aged 3. 8y and two females aged 2. 5y and 2. 6y) whereas adults were the same. We applied the classi er obtained with the testing sample on the validation dataset. Our classi cation model showed great generalization performance on validation dataset with 87. 5% of segments classi ed as child voice, being correct.
Acknowledgement This research was supported by the  Swiss National Foundation Synapsy  (Grant No. 51NF40-185897 ), the  Swiss National Foundation for Scienti c Research  (Grant Nos. # 191227  to MG and # 163859 , # 190084 , # 202235  & # 212653  to MS), the  Fondation Privée des Hôpitaux Universitaires de Genève  (https://www. fondationhug. org) and by the  Fondation Pôle Autisme  (https://www. pole-autisme. ch). The funders were not involved in this study and had no role other than to provide nancial support. The authors would like to thank all the families who participated in the study, as well as  Alexandra Bastos ,  Stéphanie Baudoux ,  Lylia Ben Hadid ,  Aurélie Bochet ,  Léa Chambaz ,  Flore Couty ,  Sophie Diakonoff ,  Lisa Esposito ,  Constance Ferrat ,  Marie-Agnès Graf ,  Oriane Grosvernier ,  Sara Maglio ,  Matthieu Mansion ,  Eva Micol ,  Priska Müller ,  Irène Pittet ,  Sonia Richetin ,  Laura Sallin ,  Stefania Solazzo ,  Myriam Speller ,  Chiara Usuelli , and  Ornella Vico Begara  for their help with data collection.
Study design This cross-sectional study was conducted between May and June 2021 and included a sample of adolescents aged between 13 and 16 years old, recruited from private schools chosen in a convenient way from all Lebanese districts. Following the restrictions imposed by the Lebanese government due to COVID-19 and the online teaching adopted by schools, the collection of the data was done using an online form. The questionnaire was tested on 25 students, prior to data collection, with the aim of clarifying questions if necessary, correcting errors and assessing the length of the questionnaire; the results of the pilot study was not included in the analysis. We contacted the directors of fifteen selected schools, explained the objective of the study and presented the questionnaire to them; the anonymity of the school and students was ensured.
Statistical analysis SPSS software v. 23 was used to analyze the data. The normality of distribution of the anxiety and depression scores was confirmed since the skewness and kurtosis values fell within the -2 and + 2 range. Therefore, the Student t test was used to compare two means, whereas the ANOVA test was used to compare three or more means. Correlations between two continuous variables were tested using the Pearson test. Two linear regressions were conducted, taking the anxiety and depression scores as dependent variables. Regarding suicidal ideation, the Chi-square test was used to compare two categorical variables, whereas the Student t test was used to compare two means. A logistic regression was conducted, taking the presence/absence of suicidal ideation in the last month as the dependent variable.
Limitations and strengths Many limitations can be found in our study. First, the cross-sectional type of the study limiting us from establishing causal relationships between variables. In addition, data obtained was self-reported; responders may have over-or under-estimated some questions, leading to information bias. Other factors related to bullying perpetration have not been assessed in the questionnaire, predisposing us to a confounding bias. The cyber bully/ cyber victim scale used in this paper has not been validated in Lebanon yet. A selection bias is present because of the snowball technique followed to collect the data and the inability to know the refusal rate of participation. Despite these limitations, this study was the first in Lebanon aiming to assess the association between cyberbullying perpetration, and anxiety, depression and suicidal ideation among Lebanese adolescents.
The available field of view was extended horizontally by positioning the rotation axis off-centre and extended vertically by recording a series of acquisitions with vertical movement of the sample. The volume (3. 9 µm isotropic voxel size) was reconstructed from the radiographs by using a filtered back-projection algorithm implemented in PyHST2 software  [69] , with a single distance phase retrieval algorithm  [70]. Three-dimensional rendering was performed using 3DSlicer (https://www. slicer. org/). We generated a false colour overlay of the median and standard deviation projections of 40 tomograms using ImageJ, displayed as levels of green and blue, respectively (figure  3k ). Ethics. The material (osteoderm) of the armadillo Priodontes maximus was acquired in accordance with local laws and donated by the curator of the Vertebrates collection at LABIC-UFCAT for study, under the guidance and co-supervision of the co-author Mariela Cordeiro de Castro (Associate Professor).
5 and carbon monoxide (CO) in predicting changes in PEFR. Future studies will have to link measurable asthma features with either local environmental exposure or, preferably, personalized exposure monitoring such as used recently in Delhi with the AirSpec device. 66 Spatial-temporal modeling of six air pollution parameters (CO, PM 10 and PM 2. 5 , nitrogen dioxide, sulfur dioxide, and ozone) using a random forest model identified PM 2. 5 and PM  10 67  and distance from parks  68  being associated with seasonal occurrences of asthma in children across Tehran, Iran. Finally, data from the National Air Toxics Assessment (NATA) in the USA were used to assess air pollution in the residential areas of patients with asthma who were part of the AiRway in Asthma (ARIA) cohort. 69 e ML algorithm used (Data-driven ExposurE Profile extraction, DEEP) identified 18 separate air-toxic molecules and 20 combinations of molecules including acrylic acid, ethylidene dichloride, and hydroquinone as being significantly associated with asthma outcomes.
Meteorological the Météorage company. data and air quality The model compared data derived from the the raw Arome-EPS Chengdu Meteorological ensemble to the Monitoring Database. calibrated AromeEPS + AROLAG/AromeEPS + PEARP/PEARP + IFSens blends Twitter posts from the area of Melbourne, Australia between 2014 and 2016. Eighteen experimental combinations were conducted using different data set and classifier combinations Prediction Target Prediction Target Ambrosia pollen daily Predicting the occurrence of concentrations wildfires Increase accuracy in counting To identify wildfire events airborne pollen and with a high probability of distinguish between the becoming a large wildfire. Urtica and Parietaria species of the Urticaceae family Present an automated method for pollen analysis Predicting the peak demand Thunderstorm occurrence days of cardiovascular disease admissions using within 400 km 2 of South the hospital admissions Texas domains for up to data, air quality data, 15 h in advance and meteorological data Predicting the probability of in Chengdu, China from thunderstorm occurrence 2015 to 2017 Automated identification of reports of asthma in tweets, and using this data to detect outbreaks prior to the event Model type Model type Pollen • DNN Wildfires • RF Big Data, Remote Sensing, and Data-• XGB Mining algorithms: • Bayes-R • ANNs CNNs • SVM • LR • RF • SVM • MLP CNNs Air pollution • LR Thunderstorms Deep-Learning Neural • SVM • SNNs Network (DLNN) • RF • XGB • LightGBM Multiple ensemble predictions a poor man's ensemble of lagged deterministic forecasts, a high resolution-limited area ensemble (AromeEPS), and two global ensembles (PEARP and IFSens) Four-step architecture combining natural language processing and statistical monitoring Environmental factor 87 Recently, ML took up the ambitious challenge of achieving precision medicine in allergy characterizing allergic endotypes, understanding allergic multimorbidity relationships, contextualizing the impact of the exposome and TA B L E 1.
of the other diseases, and thus may share pathways of response to treatment and disease progression. The 3TR project will generate huge amounts of molecular and clinical metadata that will be integrated and constantly analyzed. With the help of novel AI algorithms, data from one disease will be meta-analyzed across diseases, thus accelerating the identification of new biomarkers for responsiveness or nonresponsiveness to therapy or novel targets for therapeutic intervention based on molecular pathway similarity. 8. 6 | AI enabling the access and use of real-world data (RWD) RWD collected in somewhat passive ways, such as in hospital EHR systems or routine public health data capturing procedures, com- pared with the more targeted and deliberate data collection during clinical trials, tends to consist of much bigger datasets, often multi- ple orders of magnitude bigger.
Participants were not related in any way to the investigators. Some were able to put us in touch with other colleagues who might be interested in our study, thus creating a network of interest around the issue at hand. Data were collected by means of semi-structured interviews of approximately 30 to 75 min, according to an interview grid presented in Table  2. The interviews were conducted in French with secondary translation into English after analysis of the results. These individual interviews were conducted by the principal investigator (PI) at the practitioner's office or home. The interviewees' anonymous comments were recorded with their consent and then transcribed. The data were analysed by Phenomenological Interpretative Analysis (IPA)  [21]. The interviews were coded using NVivo 12 Plus software  [22].
The themes and meta-themes were validated by two senior child psychiatrists (LF and LR) who supervised the research, allowing triangulation of the coding. Data saturation was reached at interview 9, as no additional themes emerged and the narratives were similar in the final interviews. This study meets the COREQ criteria for validation of a qualitative study  [23]. The data were processed according to the CNIL reference methodology MR-004  [24]. In compliance with the General Data Protection Regulation, the research was registered in the Toulouse University Hospital internal register under the reference RnIPH 2021-26.
The feedback is based on the data collected by an automatic writing analysis processor that evaluates the size, order, direction, and shape of the strokes. These handwriting analyses are used to personalize the teaching scenarios. For example, the AI will elaborate specific exercises involving writing letters or segments of letters that have not been mastered for a student who repeatedly fails to write the letters of a word and to link them together. The teacher can use the application to personalize the learning path of each student and review the dynamic layout provided to each student and the feedback they receive. Children can also work autonomously by using the tablet workbook and following their personalized pathway. Thus, such a device can be used as a tutor and teaching aid as well as a learning tool.
HelioSwarm is a recently selected NASA multi-spacecraft mission concurrently covering a wide range of scales in the solar wind which will allow us to determine directly the spatial and temporal structure of plasma turbulence. However, spacecraft data alone will not provide answers to the fundamental questions facing our community; answering these questions requires dedicated support for research of and related to the novel multi-spacecraft data provided by HelioSwarm. In this white paper, some of these fundamentally important questions that can only be addressed by research related to multipoint in situ measurements are presented.
Yet, due to the severe limitations imposed by single-point measurements, we are unable to characterize sufficiently the spatial and temporal properties of the solar wind, leaving many fundamental questions about plasma turbulence unanswered. Therefore, the time has now come wherein making significant additional progress to determine the dynamical nature of solar-wind turbulence requires multi-spacecraft missions spanning a wide range of scales simultaneously. The recently selected multi-spacecraft NASA mission HelioSwarm will satisfy this requirement by concurrently covering a wide range of scales in the solar wind, thereby providing the necessary in situ data to directly determine the spatial and temporal structure of plasma turbulence. However, data alone will not provide answers to the fundamental questions facing our community; answering these questions requires support for research of and related to the novel multi-spacecraft data provided by HelioSwarm.
Therefore, single-spacecraft tests of these models require combining several days to a month of data, while the auto-correlation time in the solar wind is of order one hour or less, likely mixing different plasma and turbulence conditions in the analysis. Cluster has been used to determine the full spectral anisotropy  [36] ; however, the range of accessible scales were highly limited. Thus, definitively determining the validity of each of these turbulence models or making progress in developing new models requires the analysis of data from a multi-spacecraft mission, where all components of the wavevector can be measured simultaneously, rather than combining many single-point datasets spanning turbulence with widely varying parameters.
We have not carried out a systematic analysis of the dynamics between the national and the regional level in this paper. This will be the focus of the GEIPP project (2023-2025)-MSCA COFUND, led by the first author. Limited conclusions can be anticipated in the case of the Basque Country due to the recent transference of competencies. Regarding Catalonia, our hypothesis is that the issue of women in prison has neither been of primary attention of the feminist movement or the feminist state apparatus. Despite a rhetoric in favor of gender equality, the intrinsic characteristics of the penal state hinder transformation to more public forms, although the future analysis will show the particularities in this evolution. 4 Data from the General Secretariat for Penitentiary Institutions www. instituc ionpenitenciaria.
Acknowledgements We wish to thank  Sylvia Walby ,  Karen Shire , and  Heidi Gottfried  for the inspiring workshop on Future of gender regimes: implications for theory and practice they organized in Berlin in April 2022 and for their and all participants' helpful feedback to our paper. The paper was also presented at the Geypo Seminar Series in May 2023 and greatly benefited from the feedback received. We also thank editors and anonymous reviewers for their valuable insights and suggestions.
Methods: Prevalence of SARS-CoV-2 VOC SARS-CoV-2 testing of HCWs was performed using routine diagnostic procedures in the Virology laboratory of the Hospices Civils de Lyon, and included: TMA (Aptima™ SARS-CoV-2 Assay (Hologic), LAMP (SARS-CoV-2 ID NOW™ (Abbott), and RT-qPCR with different kits (Cobas® 6800 SARS-CoV-2 assay (Roche), Panther Fusion SARS-CoV-2 assay (Hologic). To determine the prevalence of SARS-CoV-2 VOC in HCW, available positive samples with Ct<28 were sequenced using COVIDSeq (Illumina) as previously described  [12]. Libraries were sequenced to 1 M paired-end reads (2x100 bp) and data were analyzed using the in-house seqmet bioinformatic pipeline (available at https://github. com/genepii/seqmet). Clades and lineages were determined on samples with genome coverage >90% using Nextclade and PangoLEARN, respectively.
Ethical statement and cohort description Population of HCW from the hospital database included in the epidemiological investigation We extracted data from the occupational medicine database of the University Hospital of Lyon (Hospices Civils de Lyon), France. A total of 13489 HCWs working at Hospices civils de Lyon throughout the study period (12/15/21 _ 03/21/22) were included. Only subjects who (i) had never contracted COVID-19, (ii) were primed with ChAdOx1-S-nCoV-19 or an mRNA vaccine and (iii) received the second or third dose of an RNA vaccine were included in the epidemiological analysis (Supplementary Figure  1 ). Breakthrough infections, documented by positive RT-PCR or antigenic tests and that occurred after the 15 th of December 2021 and at least 7 days after vaccine injection were took into account to evaluate infection risk in different groups of subjects.