dataesr/scientific-paragraphs-categorization huggingface dataset:
Total erupted mass The volumes of the six studied Plinian eruptions are relatively close to each other comprising between 0. 04 km 3 DRE (for the Etoile eruption) and 1. 02 km 3 DRE (for the Fig. 5  Ash loading probability maps for the maximum expected eruption scenario (P3 event) using the 41-year wind database, with a 5% probabilities of reaching the ash load thresholds of 10. 7, 107, 214 and 1,070 kg m -2 , and b all probabilities to reach the 10. 7 kg m -2 threshold P3 eruption), which correspond to erupted masses ranging from ≈ 10 11 to ≈ 10 12 kg (Michaud-Dubuy 2019;  Carazzo et al. 2020). All eruptions are thus ranked 4 on the VEI scale, with the exception of the P3 event, which is a VEI5 eruption (and the most powerful recorded at the Mount Pelée volcano). Bearing in mind that less powerful VEI3 eruptions most certainly occurred at Mount Pelée volcano, but did not leave any trace in the field due to severe erosion processes, we also consider lower masses (down to 10 10 kg) in our hazard assessment.
New hazard maps for Martinique We now combine the simulation outputs for the 16 scenarios into a single map using the probabilities of occurrence of each scenario given in Table  2. The results are presented in Figs. 8  and 9. Figure  8  shows the probabilities of exceeding each static load threshold, except for the smallest one (i. e. , 1. 07 kg m -2 , corresponding to a 1 mm thickness of ash) as this threshold is largely exceeded in many locations of the island. As in the current hazard map for tephra fallout (Fig. 1 ), the northern part of Martinique remains the most threatened area in case of a future Plinian eruption at Mount Pelée volcano, with a 55% probability of reaching Fig. 7  5% probability maps for selected scenarios using the 41-year wind database, with a scenario 4, b scenario 7, c scenario 10, and d scenario 13 (Table  2 ).
Our stratigraphical record indeed shows that some Plinian eruptions (P2 and P5) mainly affected the eastern coast. Only the volcano flanks are likely to exceed the last threshold of 1,070 kg m -2 and thus to be completely destroyed (Fig. 8d ). This result is consistent with our field studies as we often measured more than 1 m of tephra in the northern part of Martinique (on every flanks of the volcano)  (Carazzo et al. 2012; Carazzo et al. 2019; Michaud-Dubuy et al. 2019; Carazzo et al. 2020). It is also important to note that the airport (and Fort-de-France, the most populated area in Martinique) exceeds a 10% probability of reaching the threshold corresponding Fig. 9  Main hazard map for tephra fallout in Martinique using the 41-year wind database and considering the 16 eruptive scenarios, with a the 5% probabilities to reach a deposit thickness of 1 cm (10.
Acknowledgements The authors thank two anonymous reviewers, the editor,  R. E. A. Robertson , and the editor-in-chief,  J. M. Lindsay , for their useful comments, which greatly improved the original version of this paper. We are thankful to  C. Bonadonna  and  T. Esposti Ongaro  for fruitful discussions on the content of this article. We are grateful to the staff of the  Mt. Pelée volcanological observatory (OVSM)  for their assistance. This study contributes to the  IdEx  project " Université de Paris " ( ANR-18-IDEX-0001 ).
Funding This work was funded by the  RAVEX ANR Project (ANR  contract  ANR-16-CE03-0002 ), and by the  PREST  project co-funded by  INTERREG Caraïbes for the European Regional Development Fund. This work has been supported by the  European Union 's  Horizon 2020 research and innovation programme , under grant agreement No  731070  ( EUROVOLC  project).
Availability of data and materials The dataset of the tephra deposits from the Mount Pelée eruptions used in this work is from previous publications cited in the text. More field data are provided in the supporting information. We acknowledge the use of the HAZMAP code provided by the Istituto Nazionale di Geofisica e Vulcanologia (http://datasim. ov. ingv. it/models/hazmap. html). ERA5 reanalysis data were provided by the European Centre for Medium-Range Weather Forecasts, from their website (https://cds. climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview).
Methods: We collected data from 329 patients up to 365 days after hip fracture surgery and built four models using packages in RStudio. A global Cox Proportional Hazards Model was developed from all covariates. Covariates included sex, age, BMI, white cell count, lactate, creatinine, hemoglobin, C-reactive protein, ASA status, socio-economic status, duration of surgery, total time in the operating room, side of surgery and procedure urgency. We also developed a Cox proportional hazards model (CPH). a logistic regression model (LRM), and a generalized linear model (GLM) for binomial response data using iterative data reduction and elimination. We wrote an app in Shiny in order to present the models in a user-friendly way. The app consists of a drop-down box for model selection, horizontal sliders for data entry, model summaries, and prediction and survival plots.
Methods We collected data from 329 patients. Our variables included sex, age, BMI, white cell count, lactate, creatinine, hemoglobin, C-reactive protein, ASA status, socio-economic status, duration of surgery, total time in the operating room, side of surgery and procedure urgency. Thereafter, we internally calibrated and validated a Cox Proportional Hazards (CPH) model of survival 365 days after hip fracture surgery, logistic regression models 30-, 120-and 365-days after surgery and a binomial model. In order to present models on a laptop, tablet or phone in a user-friendly way, we built any app using Shiny (RStudio). The app consisted of a drop-down box for model selection, horizontal sliders for data entry, model summaries, and prediction and survival plots. A slider represents patient follow-up over 365 days.
Apps can translate statistical modelling using R packages (R Studio Version 1. 3. 1093) into easy-to-understand web-based interactive nomograms (Shiny, RStudio), that readily demonstrate differences between low-risk and high-risk patients. One example is the DynNom package  [7]  in R that pre displays the results of statistical models as a dynamic nomogram, and readily allows individual prediction with 95% confidence intervals. Anaesthetic guidelines and protocols increasingly drive standardization of practice  [10]. However, we believe that individual identification of risk is more likely to improve outcomes  [11]. Several risk specific and generic surgical risk indices are available that predict mortality after hip fracture surgery, but most are limited to prediction of mortality 30 days after operation.
Indices discriminate well, but lack adequate calibration  [12]. The Nottingham Hip Fracture Score has validated nationally  [2]  and internationally  [13]  and is commonly used, albeit its 365-day score discriminates only between low and high-risk patients. Moreover, the more complex a statistical model, due to non-linearity and interactions, the more difficult is to comprehend and apply. As such, survival models based on time to death data  [14]  are uncommon. Therefore, a need arises to develop an easy to interpret app for time to event as well as binary outcome data. Proving the apps utility locally, would provide the platform for prospective development of a large multicentre database that would inform a statistical model, with high calibration and discrimination, that could be used easily at the bedside using a laptop, tablet or phone in order to inform staff and lay-people of outcome after hip fracture surgery.
Methods We conducted a retrospective study of patients undergoing fractured hip surgery. Our study consisted of data collection, statistical modelling and app development. and a rank social deprivation score based on 6976 data zones from the Scottish Index of Multiple Deprivation database (SIMD16)  [15]. We used the SIMD16 vigintile database that ranks deprivation from 1 (most deprived residential area) to 20 (least deprived residential area)  [16]. Blood tests (hemoglobin, white cell count, creatinine, lactate and C-reactive protein (CRP)) were taken on hospital admission. With regard to surgery, we noted the American Society of Anesthesiologists (ASA) status of the patient; type of anesthesia (general or spinal); surgical implant; and time of operation. Operations performed between 0900h and 1700h were classified as "daytime"; between 1700h and 2200h as "evening"; and from 2200h to 0900h as "night-time" procedures.
Model development We developed four statistical models: a global Cox proportionality hazards model using all available covariates (global CPH), a Cox proportional hazards model (CPH), a generalised linear model (GLM) and a logistic regression model (LRM). Models and nomograms were developed using the R packages "shiny", "ggplot2", "ggpubr", "plotly", "stargazer", "rms", "shinythemes" and "plotly" [16]. Our modelling strategy was based on that recommended by Harrell and Steyerberg  [15]. We selected variables based on our clinical experience and evidence from published studies. We collected as much pertinent data as possible, with wide distributions for predictor values. We hypothesized that continuous variables were nonlinear. Imputation replaced missing covariables with the median value. We restricted the number of events per variable (EPV) in the model according to the equation EPV = Events or outcomes/15.
App development A data scientist (KG) developed an app using Shiny, a package from RStudio that builds interactive web applications with R. We created three files: ui. R to define the user interface A; server. R to interrogate data from the UI, and define the app logic; and Prediction plots were displayed on a graph with probability on the x-axis. The mean was displayed as a coloured square with horizontal lines representing the 95% confidence interval (CI) for outcome. Survival models showed a Kaplan Meier plot of estimated survival probability over time. The app can be viewed at https://hip-fracturemodelling. shinyapps. io/hip-fracture-modelling/.
Statistical analysis Continuous variables were presented as mean (SD) and analysed using Aspin-Welch Unequal-Variance Test. Non-parametric data was presented as median (IQR [range]) and analyzed using the Mann-Whitney U test. Cross-tabulation of categorical data count (n) was analyzed used χ 2. Measurement of AUROC of the Nottingham Hip fracture score was undertaken using GraphPad Prism 9 (GraphPad, San Diego, CA).
Data collection We recorded data from 329 patients, of whom 224 (68%) were female and 85 (32%) were male. Four percent of biochemical data were missing, and replaced with the median value. Over two thirds of patients (n = 224 (68%)) were classified as American Society of Anesthesiologists (ASA) category 3 or ASA 4. This is compatible with severe systemic disease (ASA 3) that is a constant threat to life (ASA 4  Examples of differences in mortality on admission included: age 80 yr vs 60 yr, Hazard Rate (HR) 0. 6 (0. 3 to 1. 1); plasma lactate levels of 2 mmol. L -1 vs 1 mmol. L -1 , HR 2. 4 (1. 5 -3. 9); and plasma creatinine levels 60 vs 90 mol. L -1 , HR 2. 3 (1. 3 -3. 9).
For example, reduction in BMI from 25 kg. m -2 to 20 kg. m -2 increased the hazard ratio by approximately one third. Unlike other studies, we failed to show a significant effect of anemia. This probably reflects changes in patient blood management strategies since initial studies into this association  [2, 3]  were published. We surprisingly showed an inverse relationship between age and outcome, in contrast to many other models  [12, 24, 25]. This reflects the increased comorbidities in our younger population and therefore, limits the applicability of our model to other populations. Nevertheless, for comparison, we applied the Nottingham hip fracture score to our data. Surprisingly, the Nottingham score showed much poorer discrimination compared to our dataset with AUROC values < 0. 61 and lack of significance for 30-day prediction of mortality.
We used bootstrapping to validate our model. The advantage of bootstrapping is that the entire dataset can be used, unlike data-splitting which reduces the sample size for both model development and testing. Variable selection or stopping rules were not used: these methods provide regression coefficients that are too high and confidence intervals that are too small. Neural networks such as support vector machine, naive Bayes classifier and random forest classifier have been applied to hip fracture datasets but were no better than logistic regression in predicting outcome after surgery  [26]. Third, our mortality was in-line with national data. Mortality increased from 7. 3% at 30 days to 28. 6% at 365 and allowed us to incorporate five variables with good calibration and validation. However, fourth, we had insufficient data to generate a model that incorporated all potential confounders.
Data collection We collected preoperative and operative data from all patients presenting for hip fracture surgery in Ninewells Hospital, Dundee, over an 8-month period between 1 st May 2016 and 31 st December 2016. Patients were followed up for one year by reviewing case notes, anesthetic charts and operative notes as part of a 4 th year medical student project. Caldicott Guardian approval was obtained from the University of Dundee on.
g. , information about their mood, their activity, their sleeping patterns). In the context of these clinical studies, the goal of the dialog model is two-fold. A first goal is to collect a set of pre-defined data points i. e. , answers to a set of pre-defined questions specified in a questionnaire. A second goal is to gather relevant serendipitous information i. e. , health related information that is not addressed by the questionnaire but that is provided by the user during the interaction and which may be relevant to understand the impact of the therapy investigated by the clinical study. This requires keeping the user engaged and prompting him/her with relevant follow-up questions. To model these three goals (collecting a predefined set of data points, keeping the user engaged and gathering more informal information about the state of the patient), we introduce an ensemble model which combines three bots: a task-based bot (MEDBOT) whose goal is to collect information about the mood, the daily life, the sleeping pattern, the anxiety level and the leisure activities of the patients; a follow-up bot (FOLLOWUPBOT) designed to extend the task-based exchanges with health-related, follow-up questions based on the user input; and an empathy bot (EMPATHYBOT) whose task is to reinforce the patient engagement by providing empathetic and socially driven feedback.
Medical Bot MEDBOT is a retrieval model which uses the pretrained ConveRT dialog response selection model  (Henderson et al. , 2019)  to retrieve a query from the MedTree Corpus  (Liednikova et al. , 2020). It is designed to collect information from the user based on a predefined set of questions contained in a questionaire. The MedTree Dataset. The MedTree corpus  (Liednikova et al. , 2020)  was developed to train a task-based, information seeking, health bot on five domains: sleep, mood, anxiety, daily tasks and leisure activities. It was derived from a dialog tree provided by a domain expert (i. e. , a physician) and designed to formalise typical patient-doctor interactions occurring in the context of a clinical study. In that tree, each branch captures a sequence of (Doctor Question, Patient Answer) pairs and each domain is modeled by a separate tree with the root introducing the conversation (initial question) and the leaves providing a closing statement.
The MedTree corpus is then derived from this tree by extracting from each branch of the tree, all contextquestion pairs, where the context consists of a sequence of patient-doctor-patient turns present on that branch and the question is the following doctor question. A fragment of the decision tree created for the sleep domain and an example dialog are shown in Figure  1. There are two versions of the MedTree corpus: one consisting of only the context/question pairs derived from the dialog tree (INIT) and the other including variants of these pairs based on paraphrases extracted from forum data (ALL). In  (Liednikova et al. , 2020) , the ALL corpus is used to train a generative and a classification model. In our work, we use (a slightly modified version 1  of) the INIT corpus instead, as its small size facilitates retrieval (the number of candidates is small) and preliminary experimentations showed better results when using the INIT corpus.
Rather than artificially restricting the dialog to the limited set of topics pre-defined by the dialog tree, the combined model (MEDBOT + FOL-LOWUPBOT) allows for transitions based either on the dialog tree or on health-related, follow-up questions. In that sense, FOLLOWUPBOT allows not only for the collection of health-related serendipitous information but also for smoother dialog transitions. Like MEDBOT, FOLLOWUPBOT used the pretrained ConveRT model to retrieve context appropriate queries from a dialog dataset. In this case however, the queries are retrieved from the Health-Board dataset, a new dataset we created to support follow-up questions in the health domain. The Healthboard Dataset. This dataset consists of (s, q) pairs where s is a (health related) state-2 Both contexts are encoded using ConveRT as average of embeddings of the last turn and concatenation of preceding ones.
The inner product is used to compute similarity. ment and q is a follow-up question for that statement. We extract this dataset from the Healthboard forum 3  as follows. We first select 16 forum categories (listed in Table  1 ) that are relevant to our five domains. In the forum, each category includes multiple conversational threads, each thread consists of multiple posts and each post is a text of several paragraphs that can be split into sentences. In total, we collect 175,789 posts from 31,042 threads with 5. 68 posts in average per thread. We then segment each post into sentences using the default NLTK sentence segmenter. We label each sentence with a dialogue act classifier in order to distinguish statements ("sd" label) from questions ("qo" label). For this labelling, we fine-tune the Distilbert Transformerbased classification model 4  on the Switchboard Corpus  Stolcke et al.
(2000)  using 6 classes "qo" (Open-Question), "sd" (Statement-non-opinion), "ft" (Thanking), "aa" (Agree/Accept), "%" (Uninterpretable) and "ba" (Appreciation). For each question q (i. e. , sentence labelled "qo") in each thread T , we gather all statements (i. e. , all sentences labeled as "sd") which precede q in T into a pool of candidate statements 5. As dialogue turns in bots should remain short, we filter sentences that have more than 100 tokens. For each candidate statement, we calculate its similarity with the question using the dot product on their ConveRT embeddings. We filter out all candidate statements whose score with the question is less than 0. 6. If after filtering the resulting pool contains at least one candidate, we select the top-ranked statement and add the statement-question pair pair to the dataset.
The resulting dataset contains 3,181 (statement, question) pairs. Model. Similar to the MEDBOT model, the FOL-LOWUPBOT model used the pre-trained ConveRT model to compare the current dialog context (the preceding three turns) with the statements contained in the HealthBoard dataset using the inner product. The top-20 candidates are then retrieved and filtered using Maximal Marginal Rel-    (Carbonell and Goldstein, 1998)  with λ = 0. 5 to control for repetitions 6. Next, we compute the similarity between the remaining selected questions and the questions included in the current dialog context (all preceding dialog turns) and we exclude candidates with similarity score 0. 8 or higher. After filtering, the top ranking candidate is selected and the associated follow-up question is output.
Empathy Bot As the name suggests, the role of the EMPATHY-BOT is to engage the user by showing empathy. For this bot, we use  Roller et al. (2020)  generative model which was pre-trained on a variant of Reddit discussion  (Baumgartner et al. , 2020)  and finetuned on the ConvAI2  (Zhang et al. , 2018) , Wizard of Wikipedia  (Dinan et al. , 2019) , Empathetic Dialogues  (Rashkin et al. , 2019) , and Blended Skill Talk datasets (BST)  (Smith et al. , 2020)  to opti-6 MMR is a measure for quantifying the extent to which a new item is both dissimilar to those already selected and similar to the target (here a selected question). A λ value of 0. 5 favors similarity and diversity equally, both matter equally. mize engagigness and humanness in open-domain conversation.
Data Table  2  shows some statistics for the corpora used for pretraining (ConveRT, Blender) and for retrieval (INIT, HealthBoard). For MEDBOT and FOLLOWUPBOT, we use the ConveRT model from PolyAI 7. For EMPATHYBOT, we use the Blender model with 90M parameters from the ParlAI library 8. One benefit of the ensemble approach is that several models can be combined, each modelling different types of dialog requirements. We compare different configurations of our three bots: COMBOT (which combines the three bots), MEDBOT (using only the task-based bot), MED+EMPATHYBOT an ensemble model which combines the task-based ( MEDBOT) and the social bot (EMPATHYBOT) and MEDBOT+ FOL-LOWUPBOT, a bot combining the task-based and the follow-up question bot. We first use automatic metrics and global satisfaction scores to compare the four models.
Evaluation As there does not exist a dataset of well-formed health-related dialogs whose aim is both to answer a clinical study questionaire and to allow for serendipitous interactions, we have no test set on which to compare the output of our dialog models. Moreover, as has been repeatedly argued, referencebased, automatic metrics such as BLEU or ME-TEOR, fail to do justice to the fact that a dialog context usually has many possible continuations. We therefore use reference-free automatic metrics and human assessment for evaluation. Human evaluation. We use the MTurk platform to collect human-bot dialogs for our four models (COMBOT, MEDBOT and MED+EMPATHYBOT) and ask the crowdworkers to provide a satisfaction rate at the end of their interaction with the bot. We then run a second MTurk crowdsourcing task to grade and compare dialogs pairs produced by different models.
To assess task success, we count the number of unique medical entities (Slots) mentioned. We do this using the clinical NER-model from gthe Stanza library  (Zhang et al. , 2020)   9  , a model trained on the 2010 i2b2/VA dataset  (Uzuner et al. , 2011)  to extract named entities denoting a medical problem, test or treatment. We report the average number of medical entities both per dialog and in the user turns (to assess how much medical information comes from the user). Following  Yu et al. (2017) , we also calculate Information gain (InfoGain), the average number of unique tokens per dialog and Conversation Length (ConvLen), the average number of turns in the overall dialog. Finally, we compute the number of questions asked by the user (UserQ) as an indication of the user trust and engagement.
The first DDA solver, proposed by Draine and Flatau, simulates the light scattering and absorption by isolated or arrays of irregular particles placed in a homogeneous background  [8, 9]. A second code, developed by Yurkin and Hoekstra, simulates the light scattering by an object near a plane substrate  [10]. DDA in homogeneous space is also available as a toolbox of MATLAB  [11]. Here, we propose two codes that extend further the potentialities of DDA. IFDDAM (for Institut Fresnel DDA adapted to Multilayers) is able to simulate the interaction between any arbitrary incident field and an inhomogeneous, possibly anisotropic, object placed in a multilayer. IFDDA, the Institut Fresnel DDA or Idiot-Friendly DDA, for its part, is specifically optimized to deal with objects placed in a homogeneous medium.